
"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


__A       						=       logging.get_logger(__name__)

__A       						=       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A       						=       {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
}

__A       						=       {
    'openai-gpt': 512,
}



def     _lowerCamelCase(__UpperCamelCase       )				-> Tuple:
	_lowerCAmelCase								=text.strip()
	if not text:
		return []
	_lowerCAmelCase								=text.split()
	return tokens







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=True     ,     ) ->     List[str]:
				if never_split is None:
					_lowerCAmelCase								=[]
				_lowerCAmelCase								=do_lower_case
				_lowerCAmelCase								=set(__UpperCAmelCase  )
				_lowerCAmelCase								=tokenize_chinese_chars
				_lowerCAmelCase								=strip_accents
				_lowerCAmelCase								=do_split_on_punc



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Any:
				_lowerCAmelCase								=self.never_split.union(set(__UpperCAmelCase  )  ) if never_split else self.never_split
				_lowerCAmelCase								=self._clean_text(__UpperCAmelCase  )

				# This was added on November 1st, 2018 for the multilingual and Chinese
				# models. This is also applied to the English models now, but it doesn't
				# matter since the English models were not trained on any Chinese data
				# and generally don't have any Chinese data in them (there are Chinese
				# characters in the vocabulary because Wikipedia does have some Chinese
				# words in the English Wikipedia.).
				if self.tokenize_chinese_chars:
					_lowerCAmelCase								=self._tokenize_chinese_chars(__UpperCAmelCase  )
				# prevents treating the same character with different unicode codepoints as different characters
				_lowerCAmelCase								=unicodedata.normalize("""NFC"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=whitespace_tokenize(__UpperCAmelCase  )
				_lowerCAmelCase								=[]
				for token in orig_tokens:
					if token not in never_split:
						if self.do_lower_case:
							_lowerCAmelCase								=token.lower()
							if self.strip_accents is not False:
								_lowerCAmelCase								=self._run_strip_accents(__UpperCAmelCase  )
						elif self.strip_accents:
							_lowerCAmelCase								=self._run_strip_accents(__UpperCAmelCase  )
					split_tokens.extend(self._run_split_on_punc(__UpperCAmelCase     ,     __UpperCAmelCase  )  )

				_lowerCAmelCase								=whitespace_tokenize(""" """.join(__UpperCAmelCase  )  )
				return output_tokens



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=unicodedata.normalize("""NFD"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=unicodedata.category(__UpperCAmelCase  )
					if cat == "Mn":
						continue
					output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Any:
				if not self.do_split_on_punc or (never_split is not None and text in never_split):
					return [text]
				_lowerCAmelCase								=list(__UpperCAmelCase  )
				_lowerCAmelCase								=0
				_lowerCAmelCase								=True
				_lowerCAmelCase								=[]
				while i < len(__UpperCAmelCase  ):
					_lowerCAmelCase								=chars[i]
					if _is_punctuation(__UpperCAmelCase  ):
						output.append([char]  )
						_lowerCAmelCase								=True
					else:
						if start_new_word:
							output.append([]  )
						_lowerCAmelCase								=False
						output[-1].append(__UpperCAmelCase  )
					i += 1

				return ["".join(__UpperCAmelCase  ) for x in output]



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     str:
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=ord(__UpperCAmelCase  )
					if self._is_chinese_char(__UpperCAmelCase  ):
						output.append(""" """  )
						output.append(__UpperCAmelCase  )
						output.append(""" """  )
					else:
						output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				# This defines a "chinese character" as anything in the CJK Unicode block:
				#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
				#
				# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
				# despite its name. The modern Korean Hangul alphabet is a different block,
				# as is Japanese Hiragana and Katakana. Those alphabets are used to write
				# space-separated words, so they are not treated specially and handled
				# like the all of the other languages.
				if (
				    (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
				    or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
				    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
				    or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
				    or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
				    or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
				    or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
				    or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
				):  #
					return True

				return False



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Optional[Any]:
				_lowerCAmelCase								=[]
				for char in text:
					_lowerCAmelCase								=ord(__UpperCAmelCase  )
					if cp == 0 or cp == 0xf_f_f_d or _is_control(__UpperCAmelCase  ):
						continue
					if _is_whitespace(__UpperCAmelCase  ):
						output.append(""" """  )
					else:
						output.append(__UpperCAmelCase  )
				return "".join(__UpperCAmelCase  )







def     _lowerCamelCase(__UpperCamelCase       )				-> Union[str, Any]:
	_lowerCAmelCase								=set()
	_lowerCAmelCase								=word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowerCAmelCase								=char
	return pairs



def     _lowerCamelCase(__UpperCamelCase       )				-> Dict:
	_lowerCAmelCase								=text.replace("""—"""    , """-"""       )
	_lowerCAmelCase								=text.replace("""–"""    , """-"""       )
	_lowerCAmelCase								=text.replace("""―"""    , """-"""       )
	_lowerCAmelCase								=text.replace("""…"""    , """..."""       )
	_lowerCAmelCase								=text.replace("""´"""    , """'"""       )
	_lowerCAmelCase								=re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""    , R""" \1 """    , __UpperCamelCase       )
	_lowerCAmelCase								=re.sub(R"""\s*\n\s*"""    , """ \n """    , __UpperCamelCase       )
	_lowerCAmelCase								=re.sub(R"""[^\S\n]+"""    , """ """    , __UpperCamelCase       )
	return text.strip()







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase="<unk>"     ,     **__UpperCAmelCase  ) ->     Union[str, Any]:
				super().__init__(unk_token=__UpperCAmelCase     ,     **__UpperCAmelCase  )

				try:
					import ftfy
					from spacy.lang.en import English

					_lowerCAmelCase								=English()
					_lowerCAmelCase								=_nlp.tokenizer
					_lowerCAmelCase								=ftfy.fix_text
				except ImportError:
					logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""  )
					_lowerCAmelCase								=BasicTokenizer(do_lower_case=__UpperCAmelCase  )
					_lowerCAmelCase								=None

				with open(__UpperCAmelCase     ,     encoding="""utf-8"""  ) as vocab_handle:
					_lowerCAmelCase								=json.load(__UpperCAmelCase  )
				_lowerCAmelCase								={v: k for k, v in self.encoder.items()}
				with open(__UpperCAmelCase     ,     encoding="""utf-8"""  ) as merges_handle:
					_lowerCAmelCase								=merges_handle.read().split("""\n"""  )[1:-1]
				_lowerCAmelCase								=[tuple(merge.split()  ) for merge in merges]
				_lowerCAmelCase								=dict(zip(__UpperCAmelCase     ,     range(len(__UpperCAmelCase  )  )  )  )
				_lowerCAmelCase								={}



			@property
			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				return True



			@property
			def 					_lowerCAmelCase		(							self  ) ->     int:
				return len(self.encoder  )



			def 					_lowerCAmelCase		(							self  ) ->     int:
				return dict(self.encoder     ,     **self.added_tokens_encoder  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=tuple(token[:-1]  ) + (token[-1] + """</w>""",)
				if token in self.cache:
					return self.cache[token]
				_lowerCAmelCase								=get_pairs(__UpperCAmelCase  )

				if not pairs:
					return token + "</w>"

				while True:
					_lowerCAmelCase								=min(__UpperCAmelCase     ,     key=lambda __UpperCAmelCase  : self.bpe_ranks.get(__UpperCAmelCase     ,     float("""inf"""  )  )  )
					if bigram not in self.bpe_ranks:
						break
					_lowerCAmelCase						,							_lowerCAmelCase								=bigram
					_lowerCAmelCase								=[]
					_lowerCAmelCase								=0
					while i < len(__UpperCAmelCase  ):
						try:
							_lowerCAmelCase								=word.index(__UpperCAmelCase     ,     __UpperCAmelCase  )
						except ValueError:
							new_word.extend(word[i:]  )
							break
						else:
							new_word.extend(word[i:j]  )
							_lowerCAmelCase								=j

						if word[i] == first and i < len(__UpperCAmelCase  ) - 1 and word[i + 1] == second:
							new_word.append(first + second  )
							i += 2
						else:
							new_word.append(word[i]  )
							i += 1
					_lowerCAmelCase								=tuple(__UpperCAmelCase  )
					_lowerCAmelCase								=new_word
					if len(__UpperCAmelCase  ) == 1:
						break
					else:
						_lowerCAmelCase								=get_pairs(__UpperCAmelCase  )
				_lowerCAmelCase								=""" """.join(__UpperCAmelCase  )
				if word == "\n  </w>":
					_lowerCAmelCase								="""\n</w>"""
				_lowerCAmelCase								=word
				return word



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=[]
				if self.fix_text is None:
					# Using BERT's BasicTokenizer
					_lowerCAmelCase								=self.nlp.tokenize(__UpperCAmelCase  )
					for token in text:
						split_tokens.extend(list(self.bpe(__UpperCAmelCase  ).split(""" """  )  )  )
				else:
					# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
					_lowerCAmelCase								=self.nlp(text_standardize(self.fix_text(__UpperCAmelCase  )  )  )
					for token in text:
						split_tokens.extend(list(self.bpe(token.text.lower()  ).split(""" """  )  )  )
				return split_tokens



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Union[str, Any]:
				return self.encoder.get(__UpperCAmelCase     ,     self.encoder.get(self.unk_token  )  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     List[Any]:
				return self.decoder.get(__UpperCAmelCase     ,     self.unk_token  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Dict:
				_lowerCAmelCase								="""""".join(__UpperCAmelCase  ).replace("""</w>"""     ,     """ """  ).strip()
				return out_string



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				if not os.path.isdir(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

				with open(__UpperCAmelCase     ,     """w"""     ,     encoding="""utf-8"""  ) as f:
					f.write(json.dumps(self.encoder     ,     indent=2     ,     sort_keys=__UpperCAmelCase     ,     ensure_ascii=__UpperCAmelCase  ) + """\n"""  )

				_lowerCAmelCase								=0
				with open(__UpperCAmelCase     ,     """w"""     ,     encoding="""utf-8"""  ) as writer:
					writer.write("""#version: 0.2\n"""  )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,     key=lambda __UpperCAmelCase  : kv[1]  ):
						if index != token_index:
							logger.warning(
							    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
							    """ Please check that the tokenizer is not corrupted!"""  )
							_lowerCAmelCase								=token_index
						writer.write(""" """.join(__UpperCAmelCase  ) + """\n"""  )
						index += 1

				return vocab_file, merge_file








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


__A				    =						logging.getLogger(__name__)



def        __a     (    lowerCAmelCase_   :					str				)      -> List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   p * torch.log(lowerCAmelCase_				)
						UpperCAmelCase_=   0
						return -plogp.sum(dim=-1				)



def        __a     (    lowerCAmelCase_   :					List[str]				)      -> int:






						'''simple docstring'''



						logger.info("""lv, h >\t""" + """\t""".join(F"""{x + 1}""" for x in range(len(lowerCAmelCase_				)				)				)				)
						for row in range(len(lowerCAmelCase_				)				):
												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     (    lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Tuple=True			,lowerCAmelCase_   :					Optional[int]=True			,lowerCAmelCase_   :					int=None			,lowerCAmelCase_   :					Optional[int]=False				)      -> List[str]:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   model.config.num_hidden_layers, model.config.num_attention_heads
						UpperCAmelCase_=   torch.zeros(lowerCAmelCase_			,lowerCAmelCase_				).to(args.device				)
						UpperCAmelCase_=   torch.zeros(lowerCAmelCase_			,lowerCAmelCase_				).to(args.device				)

						if head_mask is None:
												UpperCAmelCase_=   torch.ones(lowerCAmelCase_			,lowerCAmelCase_				).to(args.device				)

						head_mask.requires_grad_(requires_grad=lowerCAmelCase_				)
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												UpperCAmelCase_=   None

						UpperCAmelCase_=   None
						UpperCAmelCase_=   None
						UpperCAmelCase_=   0.0

						for step, inputs in enumerate(tqdm(lowerCAmelCase_			,desc="""Iteration"""			,disable=args.local_rank not in [-1, 0]				)				):
												for k, v in inputs.items():
																		UpperCAmelCase_=   v.to(args.device				)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												UpperCAmelCase_=   model(**lowerCAmelCase_			,head_mask=lowerCAmelCase_				)
												UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   (
												    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(lowerCAmelCase_				):
																								UpperCAmelCase_=   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:
																		UpperCAmelCase_=   logits.detach().cpu().numpy()
																		UpperCAmelCase_=   inputs["""labels"""].detach().cpu().numpy()
												else:
																		UpperCAmelCase_=   np.append(lowerCAmelCase_			,logits.detach().cpu().numpy()			,axis=0				)
																		UpperCAmelCase_=   np.append(lowerCAmelCase_			,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:
												UpperCAmelCase_=   2
												UpperCAmelCase_=   torch.pow(torch.pow(lowerCAmelCase_			,lowerCAmelCase_				).sum(-1				)			,1 / exponent				)
												head_importance /= norm_by_layer.unsqueeze(-1				) + 1E-20

						if not args.dont_normalize_global_importance:
												UpperCAmelCase_=   (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(lowerCAmelCase_				)
						logger.info("""Head importance scores"""				)
						print_ad_tensor(lowerCAmelCase_				)
						logger.info("""Head ranked by importance scores"""				)
						UpperCAmelCase_=   torch.zeros(head_importance.numel()			,dtype=torch.long			,device=args.device				)
						UpperCAmelCase_=   torch.arange(
						    head_importance.numel()			,device=args.device				)
						UpperCAmelCase_=   head_ranks.view_as(lowerCAmelCase_				)
						print_ad_tensor(lowerCAmelCase_				)

						return attn_entropy, head_importance, preds, labels



def        __a     (    lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					Any				)      -> Dict:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   compute_heads_importance(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,compute_entropy=lowerCAmelCase_				)
						UpperCAmelCase_=   np.argmax(lowerCAmelCase_			,axis=1				) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_				)
						UpperCAmelCase_=   glue_compute_metrics(args.task_name			,lowerCAmelCase_			,lowerCAmelCase_				)[args.metric_name]
						logger.info("""Pruning: original score: %f, threshold: %f"""			,lowerCAmelCase_			,original_score * args.masking_threshold				)

						UpperCAmelCase_=   torch.ones_like(lowerCAmelCase_				)
						UpperCAmelCase_=   max(1			,int(new_head_mask.numel() * args.masking_amount				)				)

						UpperCAmelCase_=   original_score
						while current_score >= original_score * args.masking_threshold:
												UpperCAmelCase_=   new_head_mask.clone()  # save current head mask
												# heads from least important to most - keep only not-masked heads
												UpperCAmelCase_=   float("""Inf"""				)
												UpperCAmelCase_=   head_importance.view(-1				).sort()[1]

												if len(lowerCAmelCase_				) <= num_to_mask:
																		break

												# mask heads
												UpperCAmelCase_=   current_heads_to_mask[:num_to_mask]
												logger.info("""Heads to mask: %s"""			,str(current_heads_to_mask.tolist()				)				)
												UpperCAmelCase_=   new_head_mask.view(-1				)
												UpperCAmelCase_=   0.0
												UpperCAmelCase_=   new_head_mask.view_as(lowerCAmelCase_				)
												UpperCAmelCase_=   new_head_mask.clone().detach()
												print_ad_tensor(lowerCAmelCase_				)

												# Compute metric and head importance again
												UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   compute_heads_importance(
												    lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,compute_entropy=lowerCAmelCase_			,head_mask=lowerCAmelCase_				)
												UpperCAmelCase_=   np.argmax(lowerCAmelCase_			,axis=1				) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_				)
												UpperCAmelCase_=   glue_compute_metrics(args.task_name			,lowerCAmelCase_			,lowerCAmelCase_				)[args.metric_name]
												logger.info(
												    """Masking: current score: %f, remaining heads %d (%.1f percents)"""			,lowerCAmelCase_			,new_head_mask.sum()			,new_head_mask.sum() / new_head_mask.numel() * 1_00			,)

						logger.info("""Final head mask"""				)
						print_ad_tensor(lowerCAmelCase_				)
						np.save(os.path.join(args.output_dir			,"""head_mask.npy"""				)			,head_mask.detach().cpu().numpy()				)

						return head_mask



def        __a     (    lowerCAmelCase_   :					str			,lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					List[Any]				)      -> List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   datetime.now()
						UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   compute_heads_importance(
						    lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,compute_entropy=lowerCAmelCase_			,compute_importance=lowerCAmelCase_			,head_mask=lowerCAmelCase_				)
						UpperCAmelCase_=   np.argmax(lowerCAmelCase_			,axis=1				) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_				)
						UpperCAmelCase_=   glue_compute_metrics(args.task_name			,lowerCAmelCase_			,lowerCAmelCase_				)[args.metric_name]
						UpperCAmelCase_=   datetime.now() - before_time

						UpperCAmelCase_=   sum(p.numel() for p in model.parameters()				)
						UpperCAmelCase_=   {
						    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase_				)				)
						}

						assert sum(len(lowerCAmelCase_				) for h in heads_to_prune.values()				) == (1 - head_mask.long()).sum().item()
						model.prune_heads(lowerCAmelCase_				)
						UpperCAmelCase_=   sum(p.numel() for p in model.parameters()				)

						UpperCAmelCase_=   datetime.now()
						UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   compute_heads_importance(
						    lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,compute_entropy=lowerCAmelCase_			,compute_importance=lowerCAmelCase_			,head_mask=lowerCAmelCase_			,actually_pruned=lowerCAmelCase_			,)
						UpperCAmelCase_=   np.argmax(lowerCAmelCase_			,axis=1				) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_				)
						UpperCAmelCase_=   glue_compute_metrics(args.task_name			,lowerCAmelCase_			,lowerCAmelCase_				)[args.metric_name]
						UpperCAmelCase_=   datetime.now() - before_time

						logger.info(
						    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""			,lowerCAmelCase_			,lowerCAmelCase_			,pruned_num_params / original_num_params * 1_00			,)
						logger.info("""Pruning: score with masking: %f score with pruning: %f"""			,lowerCAmelCase_			,lowerCAmelCase_				)
						logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""			,original_time / new_time * 1_00				)



def        __a     (    )      -> Tuple:






						'''simple docstring'''



						UpperCAmelCase_=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--data_dir"""			,default=lowerCAmelCase_			,type=lowerCAmelCase_			,required=lowerCAmelCase_			,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=lowerCAmelCase_			,type=lowerCAmelCase_			,required=lowerCAmelCase_			,help="""Path to pretrained model or model identifier from huggingface.co/models"""			,)
						parser.add_argument(
						    """--task_name"""			,default=lowerCAmelCase_			,type=lowerCAmelCase_			,required=lowerCAmelCase_			,help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()				)			,)
						parser.add_argument(
						    """--output_dir"""			,default=lowerCAmelCase_			,type=lowerCAmelCase_			,required=lowerCAmelCase_			,help="""The output directory where the model predictions and checkpoints will be written."""			,)

						# Other parameters
						parser.add_argument(
						    """--config_name"""			,default=""""""			,type=lowerCAmelCase_			,help="""Pretrained config name or path if not the same as model_name_or_path"""			,)
						parser.add_argument(
						    """--tokenizer_name"""			,default=""""""			,type=lowerCAmelCase_			,help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""			,)
						parser.add_argument(
						    """--cache_dir"""			,default=lowerCAmelCase_			,type=lowerCAmelCase_			,help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""			,)
						parser.add_argument(
						    """--data_subset"""			,type=lowerCAmelCase_			,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=lowerCAmelCase_			,help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""			,)
						parser.add_argument(
						    """--masking_amount"""			,default=0.1			,type=lowerCAmelCase_			,help="""Amount to heads to masking at each masking step."""				)
						parser.add_argument("""--metric_name"""			,default="""acc"""			,type=lowerCAmelCase_			,help="""Metric to use for head masking."""				)

						parser.add_argument(
						    """--max_seq_length"""			,default=1_28			,type=lowerCAmelCase_			,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=lowerCAmelCase_			,help="""Batch size."""				)

						parser.add_argument("""--seed"""			,type=lowerCAmelCase_			,default=42				)
						parser.add_argument("""--local_rank"""			,type=lowerCAmelCase_			,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=lowerCAmelCase_			,default=""""""			,help="""Can be used for distant debugging."""				)
						parser.add_argument("""--server_port"""			,type=lowerCAmelCase_			,default=""""""			,help="""Can be used for distant debugging."""				)
						UpperCAmelCase_=   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=lowerCAmelCase_				)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												UpperCAmelCase_=   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""				)
												UpperCAmelCase_=   0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank				)
												UpperCAmelCase_=   torch.device("""cuda"""			,args.local_rank				)
												UpperCAmelCase_=   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
						UpperCAmelCase_=   args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError("""Task not found: %s""" % (args.task_name)				)
						UpperCAmelCase_=   glue_processors[args.task_name]()
						UpperCAmelCase_=   glue_output_modes[args.task_name]
						UpperCAmelCase_=   processor.get_labels()
						UpperCAmelCase_=   len(lowerCAmelCase_				)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						UpperCAmelCase_=   AutoConfig.from_pretrained(
						    args.config_name if args.config_name else args.model_name_or_path			,num_labels=lowerCAmelCase_			,finetuning_task=args.task_name			,output_attentions=lowerCAmelCase_			,cache_dir=args.cache_dir			,)
						UpperCAmelCase_=   AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path			,cache_dir=args.cache_dir			,)
						UpperCAmelCase_=   AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path			,from_tf=bool(""".ckpt""" in args.model_name_or_path				)			,config=lowerCAmelCase_			,cache_dir=args.cache_dir			,)

						# Distributed and parallel training
						model.to(args.device				)
						if args.local_rank != -1:
												UpperCAmelCase_=   nn.parallel.DistributedDataParallel(
												    lowerCAmelCase_			,device_ids=[args.local_rank]			,output_device=args.local_rank			,find_unused_parameters=lowerCAmelCase_				)
						elif args.n_gpu > 1:
												UpperCAmelCase_=   nn.DataParallel(lowerCAmelCase_				)

						# Print/save training arguments
						os.makedirs(args.output_dir			,exist_ok=lowerCAmelCase_				)
						torch.save(lowerCAmelCase_			,os.path.join(args.output_dir			,"""run_args.bin"""				)				)
						logger.info("""Training/evaluation parameters %s"""			,lowerCAmelCase_				)

						# Prepare dataset for the GLUE task
						UpperCAmelCase_=   GlueDataset(lowerCAmelCase_			,tokenizer=lowerCAmelCase_			,mode="""dev"""				)
						if args.data_subset > 0:
												UpperCAmelCase_=   Subset(lowerCAmelCase_			,list(range(min(args.data_subset			,len(lowerCAmelCase_				)				)				)				)				)
						UpperCAmelCase_=   SequentialSampler(lowerCAmelCase_				) if args.local_rank == -1 else DistributedSampler(lowerCAmelCase_				)
						UpperCAmelCase_=   DataLoader(
						    lowerCAmelCase_			,sampler=lowerCAmelCase_			,batch_size=args.batch_size			,collate_fn=lowerCAmelCase_				)

						# Compute head entropy and importance score
						compute_heads_importance(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)

						# 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:
												UpperCAmelCase_=   mask_heads(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)
												prune_heads(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				)


if __name__ == "__main__":
	main()




