code: 
  
  
  import argparse
  import logging
  import os
  from datetime import datetime
  
  import numpy as np
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, SequentialSampler, Subset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      GlueDataset,
      default_data_collator,
      glue_compute_metrics,
      glue_output_modes,
      glue_processors,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  UpperCamelCase__				       =			logging.getLogger(__name__)
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			p * torch.log(UpperCAmelCase_			)
         _lowercase					:				List[Any]           =			0
         return -plogp.sum(dim=-1			)
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         logger.info('''lv, h >\t''' + '''\t'''.join(F'{x + 1}' for x in range(len(UpperCAmelCase_			)			)			)			)
         for row in range(len(UpperCAmelCase_			)			):
                if tensor.dtype != torch.long:
                       logger.info(F'layer {row + 1}:\t' + '''\t'''.join(F'{x:.5f}' for x in tensor[row].cpu().data			)			)
                else:
                       logger.info(F'layer {row + 1}:\t' + '''\t'''.join(F'{x:d}' for x in tensor[row].cpu().data			)			)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=True    ,     UpperCAmelCase_=True    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase       ,	_lowercase					:				List[str]           =			model.config.num_hidden_layers, model.config.num_attention_heads
         _lowercase					:				str           =			torch.zeros(UpperCAmelCase_    ,     UpperCAmelCase_			).to(args.device			)
         _lowercase					:				Tuple           =			torch.zeros(UpperCAmelCase_    ,     UpperCAmelCase_			).to(args.device			)
  
         if head_mask is None:
                _lowercase					:				List[str]           =			torch.ones(UpperCAmelCase_    ,     UpperCAmelCase_			).to(args.device			)
  
         head_mask.requires_grad_(requires_grad=UpperCAmelCase_			)
         # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
         if actually_pruned:
                _lowercase					:				Tuple           =			None
  
         _lowercase					:				Optional[int]           =			None
         _lowercase					:				Dict           =			None
         _lowercase					:				Optional[Any]           =			0.0
  
         for step, inputs in enumerate(tqdm(UpperCAmelCase_    ,     desc='''Iteration'''    ,     disable=args.local_rank not in [-1, 0]			)			):
                for k, v in inputs.items():
                       _lowercase					:				Optional[int]           =			v.to(args.device			)
  
                # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
                _lowercase					:				Optional[Any]           =			model(**UpperCAmelCase_    ,     head_mask=UpperCAmelCase_			)
                _lowercase       ,	_lowercase       ,	_lowercase					:				Union[str, Any]           =			(
                    outputs[0],
                    outputs[1],
                    outputs[-1],
                )  # Loss and logits are the first, attention the last
                loss.backward()  # Backpropagate to populate the gradients in the head mask
  
                if compute_entropy:
                       for layer, attn in enumerate(UpperCAmelCase_			):
                              _lowercase					:				str           =			entropy(attn.detach()			) * inputs['''attention_mask'''].float().unsqueeze(1			)
                              attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).detach()
  
                if compute_importance:
                       head_importance += head_mask.grad.abs().detach()
  
                # Also store our logits/labels if we want to compute metrics afterwards
                if preds is None:
                       _lowercase					:				Tuple           =			logits.detach().cpu().numpy()
                       _lowercase					:				Any           =			inputs['''labels'''].detach().cpu().numpy()
                else:
                       _lowercase					:				int           =			np.append(UpperCAmelCase_    ,     logits.detach().cpu().numpy()    ,     axis=0			)
                       _lowercase					:				str           =			np.append(UpperCAmelCase_    ,     inputs['''labels'''].detach().cpu().numpy()    ,     axis=0			)
  
                tot_tokens += inputs["attention_mask"].float().detach().sum().data
  
         # Normalize
         attn_entropy /= tot_tokens
         head_importance /= tot_tokens
         # Layerwise importance normalization
         if not args.dont_normalize_importance_by_layer:
                _lowercase					:				List[Any]           =			2
                _lowercase					:				Tuple           =			torch.pow(torch.pow(UpperCAmelCase_    ,     UpperCAmelCase_			).sum(-1			)    ,     1 / exponent			)
                head_importance /= norm_by_layer.unsqueeze(-1			) + 1E-20
  
         if not args.dont_normalize_global_importance:
                _lowercase					:				Optional[int]           =			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
  
         # Print/save matrices
         np.save(os.path.join(args.output_dir    ,     '''attn_entropy.npy'''			)    ,     attn_entropy.detach().cpu().numpy()			)
         np.save(os.path.join(args.output_dir    ,     '''head_importance.npy'''			)    ,     head_importance.detach().cpu().numpy()			)
  
         logger.info('''Attention entropies'''			)
         print_ad_tensor(UpperCAmelCase_			)
         logger.info('''Head importance scores'''			)
         print_ad_tensor(UpperCAmelCase_			)
         logger.info('''Head ranked by importance scores'''			)
         _lowercase					:				Optional[Any]           =			torch.zeros(head_importance.numel()    ,     dtype=torch.long    ,     device=args.device			)
         _lowercase					:				Optional[Any]           =			torch.arange(
             head_importance.numel()    ,     device=args.device			)
         _lowercase					:				Optional[Any]           =			head_ranks.view_as(UpperCAmelCase_			)
         print_ad_tensor(UpperCAmelCase_			)
  
         return attn_entropy, head_importance, preds, labels
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				str           =			compute_heads_importance(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     compute_entropy=UpperCAmelCase_			)
         _lowercase					:				int           =			np.argmax(UpperCAmelCase_    ,     axis=1			) if args.output_mode == '''classification''' else np.squeeze(UpperCAmelCase_			)
         _lowercase					:				Optional[int]           =			glue_compute_metrics(args.task_name    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)[args.metric_name]
         logger.info('''Pruning: original score: %f, threshold: %f'''    ,     UpperCAmelCase_    ,     original_score * args.masking_threshold			)
  
         _lowercase					:				Optional[Any]           =			torch.ones_like(UpperCAmelCase_			)
         _lowercase					:				Optional[int]           =			max(1    ,     int(new_head_mask.numel() * args.masking_amount			)			)
  
         _lowercase					:				Union[str, Any]           =			original_score
         while current_score >= original_score * args.masking_threshold:
                _lowercase					:				List[str]           =			new_head_mask.clone()  # save current head mask
                # heads from least important to most - keep only not-masked heads
                _lowercase					:				Tuple           =			float('''Inf'''			)
                _lowercase					:				str           =			head_importance.view(-1			).sort()[1]
  
                if len(UpperCAmelCase_			) <= num_to_mask:
                       break
  
                # mask heads
                _lowercase					:				List[Any]           =			current_heads_to_mask[:num_to_mask]
                logger.info('''Heads to mask: %s'''    ,     str(current_heads_to_mask.tolist()			)			)
                _lowercase					:				Optional[Any]           =			new_head_mask.view(-1			)
                _lowercase					:				Dict           =			0.0
                _lowercase					:				Any           =			new_head_mask.view_as(UpperCAmelCase_			)
                _lowercase					:				Optional[Any]           =			new_head_mask.clone().detach()
                print_ad_tensor(UpperCAmelCase_			)
  
                # Compute metric and head importance again
                _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Tuple           =			compute_heads_importance(
                    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     compute_entropy=UpperCAmelCase_    ,     head_mask=UpperCAmelCase_			)
                _lowercase					:				Optional[int]           =			np.argmax(UpperCAmelCase_    ,     axis=1			) if args.output_mode == '''classification''' else np.squeeze(UpperCAmelCase_			)
                _lowercase					:				Optional[int]           =			glue_compute_metrics(args.task_name    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)[args.metric_name]
                logger.info(
                    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''    ,     UpperCAmelCase_    ,     new_head_mask.sum()    ,     new_head_mask.sum() / new_head_mask.numel() * 100    ,     )
  
         logger.info('''Final head mask'''			)
         print_ad_tensor(UpperCAmelCase_			)
         np.save(os.path.join(args.output_dir    ,     '''head_mask.npy'''			)    ,     head_mask.detach().cpu().numpy()			)
  
         return head_mask
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				int           =			datetime.now()
         _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Dict           =			compute_heads_importance(
             UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     compute_entropy=UpperCAmelCase_    ,     compute_importance=UpperCAmelCase_    ,     head_mask=UpperCAmelCase_			)
         _lowercase					:				List[str]           =			np.argmax(UpperCAmelCase_    ,     axis=1			) if args.output_mode == '''classification''' else np.squeeze(UpperCAmelCase_			)
         _lowercase					:				Tuple           =			glue_compute_metrics(args.task_name    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)[args.metric_name]
         _lowercase					:				Optional[int]           =			datetime.now() - before_time
  
         _lowercase					:				Optional[Any]           =			sum(p.numel() for p in model.parameters()			)
         _lowercase					:				Optional[int]           =			{
             layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCAmelCase_			)			)
         }
  
         assert sum(len(UpperCAmelCase_			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
         model.prune_heads(UpperCAmelCase_			)
         _lowercase					:				List[Any]           =			sum(p.numel() for p in model.parameters()			)
  
         _lowercase					:				List[str]           =			datetime.now()
         _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Optional[int]           =			compute_heads_importance(
             UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     compute_entropy=UpperCAmelCase_    ,     compute_importance=UpperCAmelCase_    ,     head_mask=UpperCAmelCase_    ,     actually_pruned=UpperCAmelCase_    ,     )
         _lowercase					:				str           =			np.argmax(UpperCAmelCase_    ,     axis=1			) if args.output_mode == '''classification''' else np.squeeze(UpperCAmelCase_			)
         _lowercase					:				int           =			glue_compute_metrics(args.task_name    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)[args.metric_name]
         _lowercase					:				int           =			datetime.now() - before_time
  
         logger.info(
             '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     pruned_num_params / original_num_params * 100    ,     )
         logger.info('''Pruning: score with masking: %f score with pruning: %f'''    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
         logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''    ,     original_time / new_time * 100			)
  
  def   UpperCamelCase__   (    )						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Union[str, Any]           =			argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             '''--data_dir'''    ,     default=UpperCAmelCase_    ,     type=UpperCAmelCase_    ,     required=UpperCAmelCase_    ,     help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''    ,     )
         parser.add_argument(
             '''--model_name_or_path'''    ,     default=UpperCAmelCase_    ,     type=UpperCAmelCase_    ,     required=UpperCAmelCase_    ,     help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,     )
         parser.add_argument(
             '''--task_name'''    ,     default=UpperCAmelCase_    ,     type=UpperCAmelCase_    ,     required=UpperCAmelCase_    ,     help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()			)    ,     )
         parser.add_argument(
             '''--output_dir'''    ,     default=UpperCAmelCase_    ,     type=UpperCAmelCase_    ,     required=UpperCAmelCase_    ,     help='''The output directory where the model predictions and checkpoints will be written.'''    ,     )
  
         # Other parameters
         parser.add_argument(
             '''--config_name'''    ,     default=''''''    ,     type=UpperCAmelCase_    ,     help='''Pretrained config name or path if not the same as model_name_or_path'''    ,     )
         parser.add_argument(
             '''--tokenizer_name'''    ,     default=''''''    ,     type=UpperCAmelCase_    ,     help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''    ,     )
         parser.add_argument(
             '''--cache_dir'''    ,     default=UpperCAmelCase_    ,     type=UpperCAmelCase_    ,     help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''    ,     )
         parser.add_argument(
             '''--data_subset'''    ,     type=UpperCAmelCase_    ,     default=-1    ,     help='''If > 0: limit the data to a subset of data_subset instances.'''			)
         parser.add_argument(
             '''--overwrite_output_dir'''    ,     action='''store_true'''    ,     help='''Whether to overwrite data in output directory'''			)
         parser.add_argument(
             '''--overwrite_cache'''    ,     action='''store_true'''    ,     help='''Overwrite the cached training and evaluation sets'''			)
  
         parser.add_argument(
             '''--dont_normalize_importance_by_layer'''    ,     action='''store_true'''    ,     help='''Don\'t normalize importance score by layers'''			)
         parser.add_argument(
             '''--dont_normalize_global_importance'''    ,     action='''store_true'''    ,     help='''Don\'t normalize all importance scores between 0 and 1'''    ,     )
  
         parser.add_argument(
             '''--try_masking'''    ,     action='''store_true'''    ,     help='''Whether to try to mask head until a threshold of accuracy.'''			)
         parser.add_argument(
             '''--masking_threshold'''    ,     default=0.9    ,     type=UpperCAmelCase_    ,     help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''    ,     )
         parser.add_argument(
             '''--masking_amount'''    ,     default=0.1    ,     type=UpperCAmelCase_    ,     help='''Amount to heads to masking at each masking step.'''			)
         parser.add_argument('''--metric_name'''    ,     default='''acc'''    ,     type=UpperCAmelCase_    ,     help='''Metric to use for head masking.'''			)
  
         parser.add_argument(
             '''--max_seq_length'''    ,     default=128    ,     type=UpperCAmelCase_    ,     help=(
                 '''The maximum total input sequence length after WordPiece tokenization. \n'''
                 '''Sequences longer than this will be truncated, sequences shorter padded.'''
             )    ,     )
         parser.add_argument('''--batch_size'''    ,     default=1    ,     type=UpperCAmelCase_    ,     help='''Batch size.'''			)
  
         parser.add_argument('''--seed'''    ,     type=UpperCAmelCase_    ,     default=42			)
         parser.add_argument('''--local_rank'''    ,     type=UpperCAmelCase_    ,     default=-1    ,     help='''local_rank for distributed training on gpus'''			)
         parser.add_argument('''--no_cuda'''    ,     action='''store_true'''    ,     help='''Whether not to use CUDA when available'''			)
         parser.add_argument('''--server_ip'''    ,     type=UpperCAmelCase_    ,     default=''''''    ,     help='''Can be used for distant debugging.'''			)
         parser.add_argument('''--server_port'''    ,     type=UpperCAmelCase_    ,     default=''''''    ,     help='''Can be used for distant debugging.'''			)
         _lowercase					:				Dict           =			parser.parse_args()
  
         if args.server_ip and args.server_port:
                # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
                import ptvsd
  
                print('''Waiting for debugger attach'''			)
                ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,     redirect_output=UpperCAmelCase_			)
                ptvsd.wait_for_attach()
  
         # Setup devices and distributed training
         if args.local_rank == -1 or args.no_cuda:
                _lowercase					:				List[str]           =			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
                _lowercase					:				List[Any]           =			0 if args.no_cuda else torch.cuda.device_count()
         else:
                torch.cuda.set_device(args.local_rank			)
                _lowercase					:				List[str]           =			torch.device('''cuda'''    ,     args.local_rank			)
                _lowercase					:				List[Any]           =			1
                torch.distributed.init_process_group(backend='''nccl'''			)  # Initializes the distributed backend
  
         # Setup logging
         logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
         logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device    ,     args.n_gpu    ,     bool(args.local_rank != -1			)			)			)
         # Set the verbosity to info of the Transformers logger (on main process only):
         if is_main_process(args.local_rank			):
                transformers.utils.logging.set_verbosity_info()
                transformers.utils.logging.enable_default_handler()
                transformers.utils.logging.enable_explicit_format()
  
         # Set seeds
         set_seed(args.seed			)
  
         # Prepare GLUE task
         _lowercase					:				Dict           =			args.task_name.lower()
         if args.task_name not in glue_processors:
                raise ValueError('''Task not found: %s''' % (args.task_name)			)
         _lowercase					:				Optional[int]           =			glue_processors[args.task_name]()
         _lowercase					:				List[str]           =			glue_output_modes[args.task_name]
         _lowercase					:				List[str]           =			processor.get_labels()
         _lowercase					:				Union[str, Any]           =			len(UpperCAmelCase_			)
  
         # Load pretrained model and tokenizer
         #
         # Distributed training:
         # The .from_pretrained methods guarantee that only one local process can concurrently
         # download model & vocab.
  
         _lowercase					:				str           =			AutoConfig.from_pretrained(
             args.config_name if args.config_name else args.model_name_or_path    ,     num_labels=UpperCAmelCase_    ,     finetuning_task=args.task_name    ,     output_attentions=UpperCAmelCase_    ,     cache_dir=args.cache_dir    ,     )
         _lowercase					:				Optional[int]           =			AutoTokenizer.from_pretrained(
             args.tokenizer_name if args.tokenizer_name else args.model_name_or_path    ,     cache_dir=args.cache_dir    ,     )
         _lowercase					:				Union[str, Any]           =			AutoModelForSequenceClassification.from_pretrained(
             args.model_name_or_path    ,     from_tf=bool('''.ckpt''' in args.model_name_or_path			)    ,     config=UpperCAmelCase_    ,     cache_dir=args.cache_dir    ,     )
  
         # Distributed and parallel training
         model.to(args.device			)
         if args.local_rank != -1:
                _lowercase					:				Union[str, Any]           =			nn.parallel.DistributedDataParallel(
                    UpperCAmelCase_    ,     device_ids=[args.local_rank]    ,     output_device=args.local_rank    ,     find_unused_parameters=UpperCAmelCase_			)
         elif args.n_gpu > 1:
                _lowercase					:				List[Any]           =			nn.DataParallel(UpperCAmelCase_			)
  
         # Print/save training arguments
         os.makedirs(args.output_dir    ,     exist_ok=UpperCAmelCase_			)
         torch.save(UpperCAmelCase_    ,     os.path.join(args.output_dir    ,     '''run_args.bin'''			)			)
         logger.info('''Training/evaluation parameters %s'''    ,     UpperCAmelCase_			)
  
         # Prepare dataset for the GLUE task
         _lowercase					:				List[str]           =			GlueDataset(UpperCAmelCase_    ,     tokenizer=UpperCAmelCase_    ,     mode='''dev'''			)
         if args.data_subset > 0:
                _lowercase					:				Dict           =			Subset(UpperCAmelCase_    ,     list(range(min(args.data_subset    ,     len(UpperCAmelCase_			)			)			)			)			)
         _lowercase					:				List[Any]           =			SequentialSampler(UpperCAmelCase_			) if args.local_rank == -1 else DistributedSampler(UpperCAmelCase_			)
         _lowercase					:				Optional[Any]           =			DataLoader(
             UpperCAmelCase_    ,     sampler=UpperCAmelCase_    ,     batch_size=args.batch_size    ,     collate_fn=UpperCAmelCase_			)
  
         # Compute head entropy and importance score
         compute_heads_importance(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  
         # Try head masking (set heads to zero until the score goes under a threshole)
         # and head pruning (remove masked heads and see the effect on the network)
         if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
                _lowercase					:				List[str]           =			mask_heads(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
                prune_heads(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  
  
  if __name__ == "__main__":
     main()
style_context: 
  
  
  import copy
  import fnmatch
  import json
  import os
  import pickle as pkl
  import shutil
  import sys
  import tarfile
  import tempfile
  from collections import OrderedDict
  from contextlib import contextmanager
  from functools import partial
  from hashlib import shaaaa
  from io import BytesIO
  from pathlib import Path
  from urllib.parse import urlparse
  from zipfile import ZipFile, is_zipfile
  
  import cva
  import numpy as np
  import requests
  import wget
  from filelock import FileLock
  from PIL import Image
  from tqdm.auto import tqdm
  from yaml import Loader, dump, load
  
  
  try:
     import torch
  
     UpperCamelCase__				       =			True
  except ImportError:
     UpperCamelCase__				       =			False
  
  
  try:
     from torch.hub import _get_torch_home
  
     UpperCamelCase__				       =			_get_torch_home()
  except ImportError:
     UpperCamelCase__				       =			os.path.expanduser(
         os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
     )
  
  UpperCamelCase__				       =			os.path.join(torch_cache_home, 'transformers')
  
  UpperCamelCase__				       =			'https://cdn.huggingface.co'
  UpperCamelCase__				       =			'https://s3.amazonaws.com/models.huggingface.co/bert'
  UpperCamelCase__				       =			'/'.join(str(Path(__file__).resolve()).split('/')[:-1])
  UpperCamelCase__				       =			os.path.join(PATH, 'config.yaml')
  UpperCamelCase__				       =			os.path.join(PATH, 'attributes.txt')
  UpperCamelCase__				       =			os.path.join(PATH, 'objects.txt')
  UpperCamelCase__				       =			os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
  UpperCamelCase__				       =			os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
  UpperCamelCase__				       =			os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
  UpperCamelCase__				       =			'pytorch_model.bin'
  UpperCamelCase__				       =			'config.yaml'
  
  def   UpperCamelCase__   (    UpperCAmelCase_=OBJECTS    ,     UpperCAmelCase_=ATTRIBUTES			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			[]
         with open(UpperCAmelCase_			) as f:
                for object in f.readlines():
                       vg_classes.append(object.split(''','''			)[0].lower().strip()			)
  
         _lowercase					:				str           =			[]
         with open(UpperCAmelCase_			) as f:
                for object in f.readlines():
                       vg_attrs.append(object.split(''','''			)[0].lower().strip()			)
         return vg_classes, vg_attrs
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			OrderedDict()
         with open(UpperCAmelCase_    ,     '''rb'''			) as f:
                _lowercase					:				Union[str, Any]           =			pkl.load(UpperCAmelCase_			)['''model''']
         for k in copy.deepcopy(list(ckp.keys()			)			):
                _lowercase					:				Union[str, Any]           =			ckp.pop(UpperCAmelCase_			)
                if isinstance(UpperCAmelCase_    ,     np.ndarray			):
                       _lowercase					:				Dict           =			torch.tensor(UpperCAmelCase_			)
                else:
                       assert isinstance(UpperCAmelCase_    ,     torch.tensor			), type(UpperCAmelCase_			)
                _lowercase					:				Dict           =			v
         return r
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  {}
  
  
  
  
       def __init__( self       :       Union[str, Any]	,      UpperCamelCase       :       dict	,      UpperCamelCase       :       str = "root"	,      UpperCamelCase       :       Optional[int]=0		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			name
              _lowercase					:				Tuple           =			level
              _lowercase					:				str           =			{}
              for k, v in dictionary.items():
                     if v is None:
                            raise ValueError()
                     _lowercase					:				Union[str, Any]           =			copy.deepcopy(UpperCamelCase		)
                     _lowercase					:				List[Any]           =			copy.deepcopy(UpperCamelCase		)
                     if isinstance(UpperCamelCase	,      UpperCamelCase		):
                            _lowercase					:				Any           =			Config(UpperCamelCase	,      name=UpperCamelCase	,      level=level + 1		)
                     _lowercase					:				Tuple           =			v
                     setattr(self	,      UpperCamelCase	,      UpperCamelCase		)
  
              _lowercase					:				Union[str, Any]           =			d
  
  
  
  
       def __repr__( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return str(list((self._pointer.keys())		)		)
  
  
  
  
       def __setattr__( self       :       Union[str, Any]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			val
              _lowercase					:				Optional[int]           =			val
              _lowercase					:				List[Any]           =			key.split('''.'''		)
              _lowercase					:				Dict           =			len(UpperCamelCase		) - 1
              _lowercase					:				Optional[Any]           =			self._pointer
              if len(UpperCamelCase		) > 1:
                     for i, l in enumerate(UpperCamelCase		):
                            if hasattr(self	,      UpperCamelCase		) and isinstance(getattr(self	,      UpperCamelCase		)	,      UpperCamelCase		):
                                   setattr(getattr(self	,      UpperCamelCase		)	,      '''.'''.join(levels[i:]		)	,      UpperCamelCase		)
                            if l == last_level:
                                   _lowercase					:				Union[str, Any]           =			val
                            else:
                                   _lowercase					:				Tuple           =			pointer[l]
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return self._pointer
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              with open(F'{file_name}'	,      '''w'''		) as stream:
                     dump(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              with open(F'{file_name}'	,      '''w'''		) as stream:
                     json.dump(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       @staticmethod
       def   lowerCAmelCase_   ( UpperCamelCase       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              with open(UpperCamelCase		) as stream:
                     _lowercase					:				Union[str, Any]           =			load(UpperCamelCase	,      Loader=UpperCamelCase		)
              return data
  
  
  
  
       def __str__( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			'''    '''
              if self._name != "root":
                     _lowercase					:				int           =			F'{t * (self._level-1)}{self._name}:\n'
              else:
                     _lowercase					:				int           =			''''''
              _lowercase					:				List[Any]           =			self._level
              for i, (k, v) in enumerate(self._pointer.items()		):
                     if isinstance(UpperCamelCase	,      UpperCamelCase		):
                            r += F'{t * (self._level)}{v}\n'
                            self._level += 1
                     else:
                            r += F'{t * (self._level)}{k}: {v} ({type(UpperCamelCase		).__name__})\n'
                     _lowercase					:				Any           =			level
              return r[:-1]
  
  
  
  
       @classmethod
       def   lowerCAmelCase_   ( cls       :       List[str]	,      UpperCamelCase       :       str	,      **UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase       ,	_lowercase					:				Dict           =			cls.get_config_dict(UpperCamelCase	,      **UpperCamelCase		)
              return cls(UpperCamelCase		)
  
  
  
  
       @classmethod
       def   lowerCAmelCase_   ( cls       :       List[str]	,      UpperCamelCase       :       str	,      **UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			kwargs.pop('''cache_dir'''	,      UpperCamelCase		)
              _lowercase					:				str           =			kwargs.pop('''force_download'''	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			kwargs.pop('''resume_download'''	,      UpperCamelCase		)
              _lowercase					:				str           =			kwargs.pop('''proxies'''	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			kwargs.pop('''local_files_only'''	,      UpperCamelCase		)
  
              if os.path.isdir(UpperCamelCase		):
                     _lowercase					:				List[Any]           =			os.path.join(UpperCamelCase	,      UpperCamelCase		)
              elif os.path.isfile(UpperCamelCase		) or is_remote_url(UpperCamelCase		):
                     _lowercase					:				Dict           =			pretrained_model_name_or_path
              else:
                     _lowercase					:				List[str]           =			hf_bucket_url(UpperCamelCase	,      filename=UpperCamelCase	,      use_cdn=UpperCamelCase		)
  
              try:
                     # Load from URL or cache if already cached
                     _lowercase					:				str           =			cached_path(
                         UpperCamelCase	,      cache_dir=UpperCamelCase	,      force_download=UpperCamelCase	,      proxies=UpperCamelCase	,      resume_download=UpperCamelCase	,      local_files_only=UpperCamelCase	,      )
                     # Load config dict
                     if resolved_config_file is None:
                            raise EnvironmentError
  
                     _lowercase					:				Dict           =			Config.load_yaml(UpperCamelCase		)
  
              except EnvironmentError:
                     _lowercase					:				Any           =			'''Can\'t load config for'''
                     raise EnvironmentError(UpperCamelCase		)
  
              if resolved_config_file == config_file:
                     print('''loading configuration file from path'''		)
              else:
                     print('''loading configuration file cache'''		)
  
              return Config.load_yaml(UpperCamelCase		), kwargs
  
  
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			torch.load('''dump.pt'''    ,     map_location=in_tensor.device			)
         _lowercase					:				Optional[Any]           =			in_tensor.numpy()
         _lowercase					:				Tuple           =			out_tensor.numpy()[0]
         print(na.shape    ,     na[0, 0, :5]			)
         print(na.shape    ,     na[0, 0, :5]			)
         assert np.allclose(UpperCAmelCase_    ,     UpperCAmelCase_    ,     rtol=0.01    ,     atol=0.1			), (
             F'{sum([1 for x in np.isclose(UpperCAmelCase_    ,     UpperCAmelCase_    ,     rtol=0.01    ,     atol=0.1			).flatten() if x is False]			)/len(na.flatten()			)*100:.4f} %'
             " element-wise mismatch"
         )
         raise Exception('''tensors are all good'''			)
  
         # Hugging face functions below
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			urlparse(UpperCAmelCase_			)
         return parsed.scheme in ("http", "https")
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=True			)						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Tuple           =			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
         _lowercase					:				Optional[Any]           =			'''/''' not in model_id
         if legacy_format:
                return F'{endpoint}/{model_id}-{filename}'
         else:
                return F'{endpoint}/{model_id}/{filename}'
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=0    ,     UpperCAmelCase_=None    ,     )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[Any]           =			'''python/{}'''.format(sys.version.split()[0]			)
         if _torch_available:
                ua += "; torch/{}".format(torch.__version__			)
         if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                ua += "; " + "; ".join('''{}/{}'''.format(UpperCAmelCase_    ,     UpperCAmelCase_			) for k, v in user_agent.items()			)
         elif isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                ua += "; " + user_agent
         _lowercase					:				Optional[Any]           =			{'''user-agent''': ua}
         if resume_size > 0:
                _lowercase					:				List[Any]           =			'''bytes=%d-''' % (resume_size,)
         _lowercase					:				str           =			requests.get(UpperCAmelCase_    ,     stream=UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     headers=UpperCAmelCase_			)
         if response.status_code == 416:  # Range not satisfiable
                return
         _lowercase					:				List[Any]           =			response.headers.get('''Content-Length'''			)
         _lowercase					:				Optional[Any]           =			resume_size + int(UpperCAmelCase_			) if content_length is not None else None
         _lowercase					:				Optional[int]           =			tqdm(
             unit='''B'''    ,     unit_scale=UpperCAmelCase_    ,     total=UpperCAmelCase_    ,     initial=UpperCAmelCase_    ,     desc='''Downloading'''    ,     )
         for chunk in response.iter_content(chunk_size=1024			):
                if chunk:  # filter out keep-alive new chunks
                       progress.update(len(UpperCAmelCase_			)			)
                       temp_file.write(UpperCAmelCase_			)
         progress.close()
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=10    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False    ,     )						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if cache_dir is None:
                _lowercase					:				Dict           =			TRANSFORMERS_CACHE
         if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                _lowercase					:				int           =			str(UpperCAmelCase_			)
  
         os.makedirs(UpperCAmelCase_    ,     exist_ok=UpperCAmelCase_			)
  
         _lowercase					:				Union[str, Any]           =			None
         if not local_files_only:
                try:
                       _lowercase					:				Union[str, Any]           =			requests.head(UpperCAmelCase_    ,     allow_redirects=UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     timeout=UpperCAmelCase_			)
                       if response.status_code == 200:
                              _lowercase					:				Tuple           =			response.headers.get('''ETag'''			)
                except (EnvironmentError, requests.exceptions.Timeout):
                       # etag is already None
                       pass
  
         _lowercase					:				Any           =			url_to_filename(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
         # get cache path to put the file
         _lowercase					:				Any           =			os.path.join(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
         # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
         # try to get the last downloaded one
         if etag is None:
                if os.path.exists(UpperCAmelCase_			):
                       return cache_path
                else:
                       _lowercase					:				Optional[Any]           =			[
                           file
                           for file in fnmatch.filter(os.listdir(UpperCAmelCase_			)    ,     filename + '''.*'''			)
                           if not file.endswith('''.json'''			) and not file.endswith('''.lock'''			)
                       ]
                       if len(UpperCAmelCase_			) > 0:
                              return os.path.join(UpperCAmelCase_    ,     matching_files[-1]			)
                       else:
                              # If files cannot be found and local_files_only=True,
                              # the models might've been found if local_files_only=False
                              # Notify the user about that
                              if local_files_only:
                                     raise ValueError(
                                         '''Cannot find the requested files in the cached path and outgoing traffic has been'''
                                         ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
                                         ''' to False.'''			)
                              return None
  
      # From now on, etag is not None.
         if os.path.exists(UpperCAmelCase_			) and not force_download:
                return cache_path
  
         # Prevent parallel downloads of the same file with a lock.
         _lowercase					:				List[str]           =			cache_path + '''.lock'''
         with FileLock(UpperCAmelCase_			):
                # If the download just completed while the lock was activated.
                if os.path.exists(UpperCAmelCase_			) and not force_download:
                       # Even if returning early like here, the lock will be released.
                       return cache_path
  
                if resume_download:
                       _lowercase					:				Dict           =			cache_path + '''.incomplete'''
  
                       @contextmanager
                       def _resumable_file_manager():
                              with open(UpperCAmelCase_    ,     '''a+b'''			) as f:
                                     yield f
  
                       _lowercase					:				str           =			_resumable_file_manager
                       if os.path.exists(UpperCAmelCase_			):
                              _lowercase					:				List[Any]           =			os.stat(UpperCAmelCase_			).st_size
                       else:
                              _lowercase					:				List[Any]           =			0
                else:
                       _lowercase					:				Optional[Any]           =			partial(tempfile.NamedTemporaryFile    ,     dir=UpperCAmelCase_    ,     delete=UpperCAmelCase_			)
                       _lowercase					:				List[str]           =			0
  
                # Download to temporary file, then copy to cache dir once finished.
                # Otherwise you get corrupt cache entries if the download gets interrupted.
                with temp_file_manager() as temp_file:
                       print(
                           '''%s not found in cache or force_download set to True, downloading to %s'''    ,     UpperCAmelCase_    ,     temp_file.name    ,     )
  
                       http_get(
                           UpperCAmelCase_    ,     UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     resume_size=UpperCAmelCase_    ,     user_agent=UpperCAmelCase_    ,     )
  
                os.replace(temp_file.name    ,     UpperCAmelCase_			)
  
                _lowercase					:				int           =			{'''url''': url, '''etag''': etag}
                _lowercase					:				Union[str, Any]           =			cache_path + '''.json'''
                with open(UpperCAmelCase_    ,     '''w'''			) as meta_file:
                       json.dump(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
         return cache_path
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=None			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			url.encode('''utf-8'''			)
         _lowercase					:				List[str]           =			shaaaa(UpperCAmelCase_			)
         _lowercase					:				str           =			url_hash.hexdigest()
  
         if etag:
                _lowercase					:				Dict           =			etag.encode('''utf-8'''			)
                _lowercase					:				Tuple           =			shaaaa(UpperCAmelCase_			)
                filename += "." + etag_hash.hexdigest()
  
         if url.endswith('''.h5'''			):
                filename += ".h5"
  
         return filename
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=None    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if cache_dir is None:
                _lowercase					:				Union[str, Any]           =			TRANSFORMERS_CACHE
         if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                _lowercase					:				Tuple           =			str(UpperCAmelCase_			)
         if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                _lowercase					:				List[str]           =			str(UpperCAmelCase_			)
  
         if is_remote_url(UpperCAmelCase_			):
                # URL, so get it from the cache (downloading if necessary)
                _lowercase					:				Union[str, Any]           =			get_from_cache(
                    UpperCAmelCase_    ,     cache_dir=UpperCAmelCase_    ,     force_download=UpperCAmelCase_    ,     proxies=UpperCAmelCase_    ,     resume_download=UpperCAmelCase_    ,     user_agent=UpperCAmelCase_    ,     local_files_only=UpperCAmelCase_    ,     )
         elif os.path.exists(UpperCAmelCase_			):
                # File, and it exists.
                _lowercase					:				str           =			url_or_filename
         elif urlparse(UpperCAmelCase_			).scheme == "":
                # File, but it doesn't exist.
                raise EnvironmentError('''file {} not found'''.format(UpperCAmelCase_			)			)
         else:
                # Something unknown
                raise ValueError('''unable to parse {} as a URL or as a local path'''.format(UpperCAmelCase_			)			)
  
         if extract_compressed_file:
                if not is_zipfile(UpperCAmelCase_			) and not tarfile.is_tarfile(UpperCAmelCase_			):
                       return output_path
  
                # Path where we extract compressed archives
                # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
                _lowercase       ,	_lowercase					:				Any           =			os.path.split(UpperCAmelCase_			)
                _lowercase					:				str           =			output_file.replace('''.'''    ,     '''-'''			) + '''-extracted'''
                _lowercase					:				Union[str, Any]           =			os.path.join(UpperCAmelCase_    ,     UpperCAmelCase_			)
  
                if os.path.isdir(UpperCAmelCase_			) and os.listdir(UpperCAmelCase_			) and not force_extract:
                       return output_path_extracted
  
                # Prevent parallel extractions
                _lowercase					:				Union[str, Any]           =			output_path + '''.lock'''
                with FileLock(UpperCAmelCase_			):
                       shutil.rmtree(UpperCAmelCase_    ,     ignore_errors=UpperCAmelCase_			)
                       os.makedirs(UpperCAmelCase_			)
                       if is_zipfile(UpperCAmelCase_			):
                              with ZipFile(UpperCAmelCase_    ,     '''r'''			) as zip_file:
                                     zip_file.extractall(UpperCAmelCase_			)
                                     zip_file.close()
                       elif tarfile.is_tarfile(UpperCAmelCase_			):
                              _lowercase					:				Dict           =			tarfile.open(UpperCAmelCase_			)
                              tar_file.extractall(UpperCAmelCase_			)
                              tar_file.close()
                       else:
                              raise EnvironmentError('''Archive format of {} could not be identified'''.format(UpperCAmelCase_			)			)
  
                return output_path_extracted
  
         return output_path
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=","			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         assert isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                with open(UpperCAmelCase_			) as f:
                       _lowercase					:				Any           =			eval(f.read()			)
         else:
                _lowercase					:				Tuple           =			requests.get(UpperCAmelCase_			)
                try:
                       _lowercase					:				List[str]           =			requests.json()
                except Exception:
                       _lowercase					:				List[Any]           =			req.content.decode()
                       assert data is not None, "could not connect"
                       try:
                              _lowercase					:				Dict           =			eval(UpperCAmelCase_			)
                       except Exception:
                              _lowercase					:				Any           =			data.split('''\n'''			)
                req.close()
         return data
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[Any]           =			requests.get(UpperCAmelCase_			)
         _lowercase					:				Optional[Any]           =			np.array(Image.open(BytesIO(response.content			)			)			)
         return img
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Dict           =			url.split('''/'''			)[-1]
         if fn not in os.listdir(os.getcwd()			):
                wget.download(UpperCAmelCase_			)
         with open(UpperCAmelCase_    ,     '''rb'''			) as stream:
                _lowercase					:				List[str]           =			pkl.load(UpperCAmelCase_			)
         _lowercase					:				str           =			weights.pop('''model'''			)
         _lowercase					:				Optional[int]           =			{}
         for k, v in model.items():
                _lowercase					:				str           =			torch.from_numpy(UpperCAmelCase_			)
                if "running_var" in k:
                       _lowercase					:				List[Any]           =			torch.tensor([0]			)
                       _lowercase					:				Union[str, Any]           =			k.replace('''running_var'''    ,     '''num_batches_tracked'''			)
                       _lowercase					:				List[Any]           =			zero
         return new
  
  def   UpperCamelCase__   (    )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         print(F'{os.path.abspath(os.path.join(UpperCAmelCase_    ,     os.pardir			)			)}/demo.ipynb'			)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_="RGB"			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         assert isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Any           =			cva.imread(UpperCAmelCase_			)
         else:
                _lowercase					:				Optional[int]           =			get_image_from_url(UpperCAmelCase_			)
                assert img is not None, F'could not connect to: {im}'
         _lowercase					:				List[Any]           =			cva.cvtColor(UpperCAmelCase_    ,     cva.COLOR_BGR2RGB			)
         if input_format == "RGB":
                _lowercase					:				Optional[int]           =			img[:, :, ::-1]
         return img
  
  
  
  
  
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=1			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return (images[i : i + batch] for i in range(0    ,     len(UpperCAmelCase_			)    ,     UpperCAmelCase_			))