code: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  
  from datasets import load_dataset
  
  from transformers import load_tool
  
  from .test_tools_common import ToolTesterMixin
  
  
  
  
  
  class _snake_case   ( unittest.TestCase    ,   a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					load_tool("document-question-answering"	)
  											self.tool.setup()
  											__lowerCamelCase		:      Optional[Any]											=					load_tool("document-question-answering"			,				remote=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
  											__lowerCamelCase		:      int											=					dataset[0]["image"]
  
  											__lowerCamelCase		:      Tuple											=					self.tool(__A			,				"When is the coffee break?"	)
  											self.assertEqual(__A			,				"11-14 to 11:39 a.m."	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
  											__lowerCamelCase		:      Tuple											=					dataset[0]["image"]
  
  											__lowerCamelCase		:      Optional[int]											=					self.remote_tool(__A			,				"When is the coffee break?"	)
  											self.assertEqual(__A			,				"11-14 to 11:39 a.m."	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
  											__lowerCamelCase		:      Union[str, Any]											=					dataset[0]["image"]
  
  											self.tool(document=__A			,				question="When is the coffee break?"	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
  											__lowerCamelCase		:      Dict											=					dataset[0]["image"]
  
  											__lowerCamelCase		:      Optional[int]											=					self.remote_tool(document=__A			,				question="When is the coffee break?"	)
  											self.assertEqual(__A			,				"11-14 to 11:39 a.m."	)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  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
  
  
  __lowercase  :      Union[str, Any]      		=				logging.getLogger(__name__)
  
  
  
  
  
  def 							a_       (						lowercase    )			->List[str]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Dict											=					p * torch.log(lowercase    )
  				__lowerCamelCase		:      Dict											=					0
  				return -plogp.sum(dim=-1    )
  
  
  
  
  
  def 							a_       (						lowercase    )			->Optional[int]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(lowercase    )    )    )    )
  				for row in range(len(lowercase    )    ):
  								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 							a_       (						lowercase							,						lowercase							,						lowercase							,						lowercase=True							,						lowercase=True							,						lowercase=None							,						lowercase=False    )			->List[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					model.config.num_hidden_layers, model.config.num_attention_heads
  				__lowerCamelCase		:      List[str]											=					torch.zeros(lowercase							,						lowercase    ).to(args.device    )
  				__lowerCamelCase		:      Union[str, Any]											=					torch.zeros(lowercase							,						lowercase    ).to(args.device    )
  
  				if head_mask is None:
  								__lowerCamelCase		:      str											=					torch.ones(lowercase							,						lowercase    ).to(args.device    )
  
  				head_mask.requires_grad_(requires_grad=lowercase    )
  				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  				if actually_pruned:
  								__lowerCamelCase		:      str											=					None
  
  				__lowerCamelCase		:      Union[str, Any]											=					None
  				__lowerCamelCase		:      Any											=					None
  				__lowerCamelCase		:      Tuple											=					0.0
  
  				for step, inputs in enumerate(tqdm(lowercase							,						desc="Iteration"							,						disable=args.local_rank not in [-1, 0]    )    ):
  								for k, v in inputs.items():
  												__lowerCamelCase		:      str											=					v.to(args.device    )
  
  								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
  								__lowerCamelCase		:      Optional[int]											=					model(**lowercase							,						head_mask=lowercase    )
  								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[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(lowercase    ):
  																__lowerCamelCase		:      Optional[int]											=					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:
  												__lowerCamelCase		:      Any											=					logits.detach().cpu().numpy()
  												__lowerCamelCase		:      int											=					inputs["labels"].detach().cpu().numpy()
  								else:
  												__lowerCamelCase		:      Dict											=					np.append(lowercase							,						logits.detach().cpu().numpy()							,						axis=0    )
  												__lowerCamelCase		:      List[str]											=					np.append(lowercase							,						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:
  								__lowerCamelCase		:      List[Any]											=					2
  								__lowerCamelCase		:      List[str]											=					torch.pow(torch.pow(lowercase							,						lowercase    ).sum(-1    )							,						1 / exponent    )
  								head_importance /= norm_by_layer.unsqueeze(-1    ) + 1E-20
  
  				if not args.dont_normalize_global_importance:
  								__lowerCamelCase		:      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(lowercase    )
  				logger.info("Head importance scores"    )
  				print_ad_tensor(lowercase    )
  				logger.info("Head ranked by importance scores"    )
  				__lowerCamelCase		:      Any											=					torch.zeros(head_importance.numel()							,						dtype=torch.long							,						device=args.device    )
  				__lowerCamelCase		:      Optional[int]											=					torch.arange(
  				    head_importance.numel()							,						device=args.device    )
  				__lowerCamelCase		:      Optional[int]											=					head_ranks.view_as(lowercase    )
  				print_ad_tensor(lowercase    )
  
  				return attn_entropy, head_importance, preds, labels
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					compute_heads_importance(lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase    )
  				__lowerCamelCase		:      Union[str, Any]											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
  				__lowerCamelCase		:      Optional[int]											=					glue_compute_metrics(args.task_name							,						lowercase							,						lowercase    )[args.metric_name]
  				logger.info("Pruning: original score: %f, threshold: %f"							,						lowercase							,						original_score * args.masking_threshold    )
  
  				__lowerCamelCase		:      Tuple											=					torch.ones_like(lowercase    )
  				__lowerCamelCase		:      str											=					max(1							,						int(new_head_mask.numel() * args.masking_amount    )    )
  
  				__lowerCamelCase		:      Any											=					original_score
  				while current_score >= original_score * args.masking_threshold:
  								__lowerCamelCase		:      List[str]											=					new_head_mask.clone()  # save current head mask
  								# heads from least important to most - keep only not-masked heads
  								__lowerCamelCase		:      Union[str, Any]											=					float("Inf"    )
  								__lowerCamelCase		:      str											=					head_importance.view(-1    ).sort()[1]
  
  								if len(lowercase    ) <= num_to_mask:
  												break
  
  								# mask heads
  								__lowerCamelCase		:      Optional[int]											=					current_heads_to_mask[:num_to_mask]
  								logger.info("Heads to mask: %s"							,						str(current_heads_to_mask.tolist()    )    )
  								__lowerCamelCase		:      Optional[int]											=					new_head_mask.view(-1    )
  								__lowerCamelCase		:      Union[str, Any]											=					0.0
  								__lowerCamelCase		:      Tuple											=					new_head_mask.view_as(lowercase    )
  								__lowerCamelCase		:      List[Any]											=					new_head_mask.clone().detach()
  								print_ad_tensor(lowercase    )
  
  								# Compute metric and head importance again
  								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      str											=					compute_heads_importance(
  								    lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase							,						head_mask=lowercase    )
  								__lowerCamelCase		:      Optional[Any]											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
  								__lowerCamelCase		:      Tuple											=					glue_compute_metrics(args.task_name							,						lowercase							,						lowercase    )[args.metric_name]
  								logger.info(
  								    "Masking: current score: %f, remaining heads %d (%.1f percents)"							,						lowercase							,						new_head_mask.sum()							,						new_head_mask.sum() / new_head_mask.numel() * 100							,						)
  
  				logger.info("Final head mask"    )
  				print_ad_tensor(lowercase    )
  				np.save(os.path.join(args.output_dir							,						"head_mask.npy"    )							,						head_mask.detach().cpu().numpy()    )
  
  				return head_mask
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase							,						lowercase    )			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Union[str, Any]											=					datetime.now()
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					compute_heads_importance(
  				    lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase							,						compute_importance=lowercase							,						head_mask=lowercase    )
  				__lowerCamelCase		:      List[Any]											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
  				__lowerCamelCase		:      List[str]											=					glue_compute_metrics(args.task_name							,						lowercase							,						lowercase    )[args.metric_name]
  				__lowerCamelCase		:      Union[str, Any]											=					datetime.now() - before_time
  
  				__lowerCamelCase		:      Optional[int]											=					sum(p.numel() for p in model.parameters()    )
  				__lowerCamelCase		:      Any											=					{
  				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowercase    )    )
  				}
  
  				assert sum(len(lowercase    ) for h in heads_to_prune.values()    ) == (1 - head_mask.long()).sum().item()
  				model.prune_heads(lowercase    )
  				__lowerCamelCase		:      Union[str, Any]											=					sum(p.numel() for p in model.parameters()    )
  
  				__lowerCamelCase		:      Optional[int]											=					datetime.now()
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					compute_heads_importance(
  				    lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase							,						compute_importance=lowercase							,						head_mask=lowercase							,						actually_pruned=lowercase							,						)
  				__lowerCamelCase		:      Dict											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
  				__lowerCamelCase		:      Tuple											=					glue_compute_metrics(args.task_name							,						lowercase							,						lowercase    )[args.metric_name]
  				__lowerCamelCase		:      str											=					datetime.now() - before_time
  
  				logger.info(
  				    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"							,						lowercase							,						lowercase							,						pruned_num_params / original_num_params * 100							,						)
  				logger.info("Pruning: score with masking: %f score with pruning: %f"							,						lowercase							,						lowercase    )
  				logger.info("Pruning: speed ratio (new timing / original timing): %f percents"							,						original_time / new_time * 100    )
  
  
  
  
  
  def 							a_       (						)			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[Any]											=					argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    "--data_dir"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						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=lowercase							,						type=lowercase							,						required=lowercase							,						help="Path to pretrained model or model identifier from huggingface.co/models"							,						)
  				parser.add_argument(
  				    "--task_name"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()    )							,						)
  				parser.add_argument(
  				    "--output_dir"							,						default=lowercase							,						type=lowercase							,						required=lowercase							,						help="The output directory where the model predictions and checkpoints will be written."							,						)
  
  				# Other parameters
  				parser.add_argument(
  				    "--config_name"							,						default=""							,						type=lowercase							,						help="Pretrained config name or path if not the same as model_name_or_path"							,						)
  				parser.add_argument(
  				    "--tokenizer_name"							,						default=""							,						type=lowercase							,						help="Pretrained tokenizer name or path if not the same as model_name_or_path"							,						)
  				parser.add_argument(
  				    "--cache_dir"							,						default=lowercase							,						type=lowercase							,						help="Where do you want to store the pre-trained models downloaded from huggingface.co"							,						)
  				parser.add_argument(
  				    "--data_subset"							,						type=lowercase							,						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=lowercase							,						help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."							,						)
  				parser.add_argument(
  				    "--masking_amount"							,						default=0.1							,						type=lowercase							,						help="Amount to heads to masking at each masking step."    )
  				parser.add_argument("--metric_name"							,						default="acc"							,						type=lowercase							,						help="Metric to use for head masking."    )
  
  				parser.add_argument(
  				    "--max_seq_length"							,						default=128							,						type=lowercase							,						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=lowercase							,						help="Batch size."    )
  
  				parser.add_argument("--seed"							,						type=lowercase							,						default=42    )
  				parser.add_argument("--local_rank"							,						type=lowercase							,						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=lowercase							,						default=""							,						help="Can be used for distant debugging."    )
  				parser.add_argument("--server_port"							,						type=lowercase							,						default=""							,						help="Can be used for distant debugging."    )
  				__lowerCamelCase		:      Tuple											=					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=lowercase    )
  								ptvsd.wait_for_attach()
  
  				# Setup devices and distributed training
  				if args.local_rank == -1 or args.no_cuda:
  								__lowerCamelCase		:      int											=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"    )
  								__lowerCamelCase		:      List[str]											=					0 if args.no_cuda else torch.cuda.device_count()
  				else:
  								torch.cuda.set_device(args.local_rank    )
  								__lowerCamelCase		:      Union[str, Any]											=					torch.device("cuda"							,						args.local_rank    )
  								__lowerCamelCase		:      Optional[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
  				__lowerCamelCase		:      Tuple											=					args.task_name.lower()
  				if args.task_name not in glue_processors:
  								raise ValueError("Task not found: %s" % (args.task_name)    )
  				__lowerCamelCase		:      int											=					glue_processors[args.task_name]()
  				__lowerCamelCase		:      Union[str, Any]											=					glue_output_modes[args.task_name]
  				__lowerCamelCase		:      Dict											=					processor.get_labels()
  				__lowerCamelCase		:      str											=					len(lowercase    )
  
  				# Load pretrained model and tokenizer
  				#
  				# Distributed training:
  				# The .from_pretrained methods guarantee that only one local process can concurrently
  				# download model & vocab.
  
  				__lowerCamelCase		:      Union[str, Any]											=					AutoConfig.from_pretrained(
  				    args.config_name if args.config_name else args.model_name_or_path							,						num_labels=lowercase							,						finetuning_task=args.task_name							,						output_attentions=lowercase							,						cache_dir=args.cache_dir							,						)
  				__lowerCamelCase		:      Dict											=					AutoTokenizer.from_pretrained(
  				    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,						cache_dir=args.cache_dir							,						)
  				__lowerCamelCase		:      Optional[int]											=					AutoModelForSequenceClassification.from_pretrained(
  				    args.model_name_or_path							,						from_tf=bool(".ckpt" in args.model_name_or_path    )							,						config=lowercase							,						cache_dir=args.cache_dir							,						)
  
  				# Distributed and parallel training
  				model.to(args.device    )
  				if args.local_rank != -1:
  								__lowerCamelCase		:      Optional[int]											=					nn.parallel.DistributedDataParallel(
  								    lowercase							,						device_ids=[args.local_rank]							,						output_device=args.local_rank							,						find_unused_parameters=lowercase    )
  				elif args.n_gpu > 1:
  								__lowerCamelCase		:      Union[str, Any]											=					nn.DataParallel(lowercase    )
  
  				# Print/save training arguments
  				os.makedirs(args.output_dir							,						exist_ok=lowercase    )
  				torch.save(lowercase							,						os.path.join(args.output_dir							,						"run_args.bin"    )    )
  				logger.info("Training/evaluation parameters %s"							,						lowercase    )
  
  				# Prepare dataset for the GLUE task
  				__lowerCamelCase		:      Tuple											=					GlueDataset(lowercase							,						tokenizer=lowercase							,						mode="dev"    )
  				if args.data_subset > 0:
  								__lowerCamelCase		:      str											=					Subset(lowercase							,						list(range(min(args.data_subset							,						len(lowercase    )    )    )    )    )
  				__lowerCamelCase		:      Optional[Any]											=					SequentialSampler(lowercase    ) if args.local_rank == -1 else DistributedSampler(lowercase    )
  				__lowerCamelCase		:      Dict											=					DataLoader(
  				    lowercase							,						sampler=lowercase							,						batch_size=args.batch_size							,						collate_fn=lowercase    )
  
  				# Compute head entropy and importance score
  				compute_heads_importance(lowercase							,						lowercase							,						lowercase    )
  
  				# 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:
  								__lowerCamelCase		:      List[str]											=					mask_heads(lowercase							,						lowercase							,						lowercase    )
  								prune_heads(lowercase							,						lowercase							,						lowercase							,						lowercase    )
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  