code: 
  
  
  import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_="data2vec."			)						->							Tuple:
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  							_lowercase					:				List[str]           =			'''backbone.''' if is_semantic else ''''''
  
  							_lowercase					:				int           =			[]
  							for i in range(config.num_hidden_layers			):
  														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.norm1.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')			)
  														rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')			)
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.attn.proj.weight', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')			)
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.attn.proj.bias', F'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')			)
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.norm2.weight', F'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')			)
  														rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')			)
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.mlp.fc1.weight', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')			)
  														rename_keys.append(
  														    (F'{prefix}blocks.{i}.mlp.fc1.bias', F'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')			)
  														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'{hf_prefix}encoder.layer.{i}.output.dense.weight')			)
  														rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'{hf_prefix}encoder.layer.{i}.output.dense.bias')			)
  
  							# projection layer + position embeddings
  							rename_keys.extend(
  							    [
  							        (F'{prefix}cls_token', F'{hf_prefix}embeddings.cls_token'),
  							        (F'{prefix}patch_embed.proj.weight', F'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
  							        (F'{prefix}patch_embed.proj.bias', F'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
  							    ]			)
  
  							if has_lm_head:
  														# mask token + shared relative position bias + layernorm
  														rename_keys.extend(
  														    [
  														        ('''mask_token''', F'{hf_prefix}embeddings.mask_token'),
  														        (
  														            '''rel_pos_bias.relative_position_bias_table''',
  														            F'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
  														        ),
  														        (
  														            '''rel_pos_bias.relative_position_index''',
  														            F'{hf_prefix}encoder.relative_position_bias.relative_position_index',
  														        ),
  														        ('''norm.weight''', '''layernorm.weight'''),
  														        ('''norm.bias''', '''layernorm.bias'''),
  														    ]			)
  							elif is_semantic:
  														# semantic segmentation classification heads
  														rename_keys.extend(
  														    [
  														        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
  														        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
  														        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
  														        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
  														    ]			)
  							else:
  														# layernorm + classification head
  														rename_keys.extend(
  														    [
  														        ('''fc_norm.weight''', F'{hf_prefix}pooler.layernorm.weight'),
  														        ('''fc_norm.bias''', F'{hf_prefix}pooler.layernorm.bias'),
  														        ('''head.weight''', '''classifier.weight'''),
  														        ('''head.bias''', '''classifier.bias'''),
  														    ]			)
  
  							return rename_keys
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_=False    ,     UpperCAmelCase_="data2vec_vision."			)						->							Any:
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  							for i in range(config.num_hidden_layers			):
  														_lowercase					:				str           =			'''backbone.''' if is_semantic else ''''''
  														# queries, keys and values
  														_lowercase					:				Union[str, Any]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'			)
  														_lowercase					:				Tuple           =			state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'			)
  														_lowercase					:				Dict           =			state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'			)
  
  														_lowercase					:				List[str]           =			in_proj_weight[
  														    : config.hidden_size, :
  														]
  														_lowercase					:				Union[str, Any]           =			q_bias
  														_lowercase					:				Dict           =			in_proj_weight[
  														    config.hidden_size : config.hidden_size * 2, :
  														]
  														_lowercase					:				Union[str, Any]           =			in_proj_weight[
  														    -config.hidden_size :, :
  														]
  														_lowercase					:				Union[str, Any]           =			v_bias
  
  														# gamma_1 and gamma_2
  														# we call them lambda because otherwise they are renamed when using .from_pretrained
  														_lowercase					:				List[Any]           =			state_dict.pop(F'{prefix}blocks.{i}.gamma_1'			)
  														_lowercase					:				str           =			state_dict.pop(F'{prefix}blocks.{i}.gamma_2'			)
  
  														_lowercase					:				Optional[Any]           =			gamma_a
  														_lowercase					:				Optional[Any]           =			gamma_a
  
  														# relative_position bias table + index
  														if not has_lm_head:
  																					# each layer has its own relative position bias
  																					_lowercase					:				Optional[int]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
  																					_lowercase					:				Optional[Any]           =			state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'			)
  
  																					_lowercase					:				Union[str, Any]           =			table
  																					_lowercase					:				List[str]           =			index
  
  def   UpperCamelCase__   (    )						->							Any:
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  							_lowercase					:				Optional[Any]           =			argparse.ArgumentParser(
  							    '''Convert Data2VecVision to HF for image classification and pretraining'''    ,     add_help=UpperCAmelCase_			)
  							parser.add_argument('''--hf_checkpoint_name'''    ,     type=UpperCAmelCase_			)
  							parser.add_argument('''--input_size'''    ,     default=224    ,     type=UpperCAmelCase_    ,     help='''images input size'''			)
  							parser.add_argument('''--beit_checkpoint'''    ,     default=''''''    ,     help='''beit checkpoint'''			)
  
  							return parser.parse_args()
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							str:
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  							def load_state_dict(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_=""    ,     UpperCAmelCase_="relative_position_index"			):
  														_lowercase					:				List[str]           =			[]
  														_lowercase					:				List[str]           =			[]
  														_lowercase					:				int           =			[]
  														# copy state_dict so _load_from_state_dict can modify it
  														_lowercase					:				Union[str, Any]           =			getattr(UpperCAmelCase_    ,     '''_metadata'''    ,     UpperCAmelCase_			)
  														_lowercase					:				List[str]           =			state_dict.copy()
  														if metadata is not None:
  																					_lowercase					:				Optional[Any]           =			metadata
  
  														def load(UpperCAmelCase_    ,     UpperCAmelCase_=""			):
  																					_lowercase					:				Union[str, Any]           =			{} if metadata is None else metadata.get(prefix[:-1]    ,     {}			)
  																					module._load_from_state_dict(
  																					    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  																					for name, child in module._modules.items():
  																												if child is not None:
  																																			load(UpperCAmelCase_    ,     prefix + name + '''.'''			)
  
  														load(UpperCAmelCase_    ,     prefix=UpperCAmelCase_			)
  
  														_lowercase					:				List[str]           =			[]
  														_lowercase					:				Union[str, Any]           =			[]
  														for key in missing_keys:
  																					_lowercase					:				Union[str, Any]           =			True
  																					for ignore_key in ignore_missing.split('''|'''			):
  																												if ignore_key in key:
  																																			_lowercase					:				int           =			False
  																																			break
  																					if keep_flag:
  																												warn_missing_keys.append(UpperCAmelCase_			)
  																					else:
  																												ignore_missing_keys.append(UpperCAmelCase_			)
  
  														_lowercase					:				List[Any]           =			warn_missing_keys
  
  														if len(UpperCAmelCase_			) > 0:
  																					print(
  																					    '''Weights of {} not initialized from pretrained model: {}'''.format(
  																					        model.__class__.__name__    ,     UpperCAmelCase_			)			)
  														if len(UpperCAmelCase_			) > 0:
  																					print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__    ,     UpperCAmelCase_			)			)
  														if len(UpperCAmelCase_			) > 0:
  																					print(
  																					    '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
  																					        model.__class__.__name__    ,     UpperCAmelCase_			)			)
  														if len(UpperCAmelCase_			) > 0:
  																					print('''\n'''.join(UpperCAmelCase_			)			)
  
  							_lowercase					:				int           =			{
  							    '''pretrained''': False,
  							    '''use_shared_rel_pos_bias''': True,
  							    '''use_abs_pos_emb''': False,
  							    '''init_values''': 0.1,
  							}
  
  							if is_finetuned:
  														model_kwargs.update(
  														    {
  														        '''num_classes''': 1000,
  														        '''use_mean_pooling''': True,
  														        '''init_scale''': 0.001,
  														        '''use_rel_pos_bias''': True,
  														    }			)
  
  							_lowercase					:				List[Any]           =			create_model(
  							    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''    ,     **UpperCAmelCase_    ,     )
  							_lowercase					:				Dict           =			model.patch_embed.patch_size
  							_lowercase					:				Optional[int]           =			(args.input_size // patch_size[0], args.input_size // patch_size[1])
  							_lowercase					:				Union[str, Any]           =			torch.load(args.beit_checkpoint    ,     map_location='''cpu'''			)
  
  							print(F'Load ckpt from {args.beit_checkpoint}'			)
  							_lowercase					:				List[Any]           =			None
  							for model_key in ("model", "module"):
  														if model_key in checkpoint:
  																					_lowercase					:				Union[str, Any]           =			checkpoint[model_key]
  																					print(F'Load state_dict by model_key = {model_key}'			)
  																					break
  
  							_lowercase					:				int           =			list(checkpoint_model.keys()			)
  							for key in all_keys:
  														if "relative_position_index" in key:
  																					checkpoint_model.pop(UpperCAmelCase_			)
  
  														if "relative_position_bias_table" in key:
  																					_lowercase					:				List[str]           =			checkpoint_model[key]
  																					_lowercase					:				Optional[Any]           =			rel_pos_bias.size()
  																					_lowercase					:				Union[str, Any]           =			model.state_dict()[key].size()
  																					_lowercase					:				int           =			model.patch_embed.patch_shape
  																					if dst_patch_shape[0] != dst_patch_shape[1]:
  																												raise NotImplementedError()
  
  							load_state_dict(UpperCAmelCase_    ,     UpperCAmelCase_    ,     prefix=''''''			)
  
  							return model
  
  def   UpperCamelCase__   (    )						->							List[Any]:
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  							_lowercase					:				Optional[Any]           =			get_args()
  
  							_lowercase					:				Optional[int]           =			'''ft1k''' in args.hf_checkpoint_name
  							_lowercase					:				Any           =			'''large''' in args.hf_checkpoint_name
  
  							if is_finetuned:
  														# To convert Beit's data2vec_vision to HF you need to copy
  														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  														# into this folder.
  														import modeling_finetune  # noqa: F401
  							else:
  														# To convert Beit's data2vec_vision to HF you need to copy
  														# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  														# into this folder
  														# IMPORTANT: Note that for now we've only converted the down-stream
  														# model and not the full pretrained model. This means for the integration
  														# test you need to add a `return x` after the following line:
  														# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  														# to make the integration test pass.
  														import modeling_cyclical  # noqa: F401
  
  							# 1. Create model config
  							_lowercase					:				List[Any]           =			DataaVecVisionConfig()
  							if is_finetuned:
  														_lowercase					:				Optional[Any]           =			True
  														_lowercase					:				Optional[int]           =			False
  														_lowercase					:				List[str]           =			True
  														_lowercase					:				str           =			1000
  
  														_lowercase					:				Union[str, Any]           =			'''huggingface/label-files'''
  														_lowercase					:				Tuple           =			'''imagenet-1k-id2label.json'''
  														_lowercase					:				List[Any]           =			json.load(open(hf_hub_download(UpperCAmelCase_    ,     UpperCAmelCase_    ,     repo_type='''dataset'''			)    ,     '''r'''			)			)
  														_lowercase					:				List[Any]           =			{int(UpperCAmelCase_			): v for k, v in idalabel.items()}
  														_lowercase					:				int           =			idalabel
  														_lowercase					:				int           =			{v: k for k, v in idalabel.items()}
  							else:
  														_lowercase					:				Optional[int]           =			False
  														_lowercase					:				Tuple           =			True
  														_lowercase					:				Optional[Any]           =			False
  
  							if is_large:
  														_lowercase					:				Tuple           =			1024
  														_lowercase					:				Optional[int]           =			4096
  														_lowercase					:				Optional[int]           =			24
  														_lowercase					:				Any           =			16
  
  							# 2. Load Beit model
  							_lowercase					:				Tuple           =			load_beit_model(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
  							orig_model.eval()
  
  							# 3. Forward Beit model
  							_lowercase					:				Optional[int]           =			BeitImageProcessor(size=config.image_size    ,     do_center_crop=UpperCAmelCase_			)
  							_lowercase					:				str           =			Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''			)
  							_lowercase					:				Dict           =			image_processor(images=UpperCAmelCase_    ,     return_tensors='''pt'''			)
  							_lowercase					:				Optional[Any]           =			encoding['''pixel_values''']
  
  							_lowercase					:				Optional[Any]           =			(pixel_values,) if is_finetuned else (pixel_values, None)
  							with torch.no_grad():
  														_lowercase					:				Any           =			orig_model(*UpperCAmelCase_			)
  
  							# 4. Load HF Data2VecVision model
  							if is_finetuned:
  														_lowercase					:				Dict           =			DataaVecVisionForImageClassification(UpperCAmelCase_			)
  														hf_model.eval()
  														_lowercase					:				List[str]           =			False
  														_lowercase					:				int           =			'''data2vec_vision.'''
  							else:
  														_lowercase					:				List[str]           =			DataaVecVisionModel(UpperCAmelCase_			)
  														hf_model.eval()
  														_lowercase					:				Optional[int]           =			True
  														_lowercase					:				int           =			''''''
  
  							_lowercase					:				Optional[int]           =			create_rename_keys(UpperCAmelCase_    ,     hf_prefix=UpperCAmelCase_    ,     has_lm_head=UpperCAmelCase_			)
  							_lowercase					:				Union[str, Any]           =			orig_model.state_dict()
  							for src, dest in rename_keys:
  														_lowercase					:				str           =			state_dict.pop(UpperCAmelCase_			)
  														_lowercase					:				str           =			val
  
  							read_in_q_k_v(UpperCAmelCase_    ,     UpperCAmelCase_    ,     hf_prefix=UpperCAmelCase_    ,     has_lm_head=UpperCAmelCase_			)
  							_lowercase					:				Dict           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
  							print('''HF missing'''    ,     UpperCAmelCase_			)
  							print('''HF unexpected_keys'''    ,     UpperCAmelCase_			)
  
  							# 5. Forward HF Data2VecVision model
  							with torch.no_grad():
  														_lowercase					:				List[Any]           =			hf_model(UpperCAmelCase_			)
  
  							_lowercase					:				List[Any]           =			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
  							# 6. Compare
  							_lowercase					:				Any           =			torch.max(torch.abs(hf_output - orig_model_output			)			).item()
  
  							print(F'max_absolute_diff = {max_absolute_diff}'			)
  							_lowercase					:				Optional[int]           =			torch.allclose(UpperCAmelCase_    ,     UpperCAmelCase_    ,     atol=1E-3			)
  							print('''Do both models output the same tensors?'''    ,     '''🔥''' if success else '''💩'''			)
  							if not success:
  														raise Exception('''Something went wRoNg'''			)
  
  							# 7. Save
  							print(F'Saving to {args.hf_checkpoint_name}'			)
  							hf_model.save_pretrained(args.hf_checkpoint_name			)
  							image_processor.save_pretrained(args.hf_checkpoint_name			)
  
  
  if __name__ == "__main__":
  			main()
  			# Run the following to convert checkpoints
  			#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  			#          --beit_checkpoint ./pretrained_base.pt \
  			#          --hf_checkpoint_name "./data2vec-vision-base"
  			#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  			#          --beit_checkpoint ./finetuned_base.pt \
  			#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  			#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  			#          --beit_checkpoint ./pretrained_large.pt \
  			#          --hf_checkpoint_name "./data2vec-vision-large"
  			#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  			#          --beit_checkpoint ./finetuned_large.pt \
  			#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
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_			))