


'''simple docstring'''




import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowercase  :      List[str]      		=				"""src/diffusers"""
__lowercase  :      Dict      		=				"""docs/source/en"""
__lowercase  :      Any      		=				"""."""





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Tuple:






				'''simple docstring'''

				with open(lowercase							,						"r"							,						encoding="utf-8"							,						newline="\n"    ) as f:
								__lowerCamelCase		:      Dict											=					f.readlines()
				# Find the start prompt.
				__lowerCamelCase		:      Tuple											=					0
				while not lines[start_index].startswith(lowercase    ):
								start_index += 1
				start_index += 1

				__lowerCamelCase		:      Optional[Any]											=					start_index
				while not lines[end_index].startswith(lowercase    ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]    ) <= 1:
								start_index += 1
				while len(lines[end_index]    ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]    ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__lowercase  :      Optional[int]      		=				"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowercase  :      str      		=				re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowercase  :      Optional[Any]      		=				re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowercase  :      List[Any]      		=				re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
__lowercase  :      Any      		=				importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowercase  :      str      		=				spec.loader.load_module()





def 							a_       (						lowercase    )			->List[Any]:






				'''simple docstring'''

				__lowerCamelCase		:      Any											=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,						lowercase    )
				return [m.group(0    ) for m in matches]





def 							a_       (						lowercase							,						lowercase    )			->Optional[int]:






				'''simple docstring'''

				__lowerCamelCase		:      Dict											=					2 if text == "✅" or text == "❌" else len(lowercase    )
				__lowerCamelCase		:      int											=					(width - text_length) // 2
				__lowerCamelCase		:      int											=					width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 							a_       (						)			->Any:






				'''simple docstring'''

				__lowerCamelCase		:      Any											=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				__lowerCamelCase		:      List[Any]											=					{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				__lowerCamelCase		:      Tuple											=					{name: config.replace("ConfigMixin"							,						""    ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				__lowerCamelCase		:      int											=					collections.defaultdict(lowercase    )
				__lowerCamelCase		:      Optional[int]											=					collections.defaultdict(lowercase    )
				__lowerCamelCase		:      Tuple											=					collections.defaultdict(lowercase    )
				__lowerCamelCase		:      Dict											=					collections.defaultdict(lowercase    )
				__lowerCamelCase		:      Optional[int]											=					collections.defaultdict(lowercase    )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowercase    ):
								__lowerCamelCase		:      List[str]											=					None
								if attr_name.endswith("Tokenizer"    ):
												__lowerCamelCase		:      int											=					slow_tokenizers
												__lowerCamelCase		:      List[str]											=					attr_name[:-9]
								elif attr_name.endswith("TokenizerFast"    ):
												__lowerCamelCase		:      Optional[int]											=					fast_tokenizers
												__lowerCamelCase		:      Optional[int]											=					attr_name[:-13]
								elif _re_tf_models.match(lowercase    ) is not None:
												__lowerCamelCase		:      Tuple											=					tf_models
												__lowerCamelCase		:      Optional[int]											=					_re_tf_models.match(lowercase    ).groups()[0]
								elif _re_flax_models.match(lowercase    ) is not None:
												__lowerCamelCase		:      str											=					flax_models
												__lowerCamelCase		:      str											=					_re_flax_models.match(lowercase    ).groups()[0]
								elif _re_pt_models.match(lowercase    ) is not None:
												__lowerCamelCase		:      Optional[Any]											=					pt_models
												__lowerCamelCase		:      int											=					_re_pt_models.match(lowercase    ).groups()[0]

								if lookup_dict is not None:
												while len(lowercase    ) > 0:
																if attr_name in model_name_to_prefix.values():
																				__lowerCamelCase		:      Optional[int]											=					True
																				break
																# Try again after removing the last word in the name
																__lowerCamelCase		:      List[Any]											=					"".join(camel_case_split(lowercase    )[:-1]    )

    # Let's build that table!
				__lowerCamelCase		:      int											=					list(model_name_to_config.keys()    )
				model_names.sort(key=str.lower    )
				__lowerCamelCase		:      Optional[int]											=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				__lowerCamelCase		:      int											=					[len(lowercase    ) + 2 for c in columns]
				__lowerCamelCase		:      int											=					max([len(lowercase    ) for name in model_names]    ) + 2

				# Build the table per se
				__lowerCamelCase		:      str											=					"|" + "|".join([_center_text(lowercase							,						lowercase    ) for c, w in zip(lowercase							,						lowercase    )]    ) + "|\n"
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]    ) + "|\n"

				__lowerCamelCase		:      Union[str, Any]											=					{True: "✅", False: "❌"}
				for name in model_names:
								__lowerCamelCase		:      Optional[int]											=					model_name_to_prefix[name]
								__lowerCamelCase		:      List[Any]											=					[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowercase							,						lowercase    ) for l, w in zip(lowercase							,						lowercase    )]    ) + "|\n"
				return table





def 							a_       (						lowercase=False    )			->int:






				'''simple docstring'''

				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      int											=					_find_text_in_file(
				    filename=os.path.join(lowercase							,						"index.mdx"    )							,						start_prompt="<!--This table is updated automatically from the auto modules"							,						end_prompt="<!-- End table-->"							,						)
				__lowerCamelCase		:      Union[str, Any]											=					get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowercase							,						"index.mdx"    )							,						"w"							,						encoding="utf-8"							,						newline="\n"    ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:]    )
								else:
												raise ValueError(
												    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."    )


if __name__ == "__main__":
				__lowercase  :      Optional[Any]      		=				argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				__lowercase  :      List[str]      		=				parser.parse_args()

				check_model_table(args.fix_and_overwrite)









'''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()





