






"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


_lowercase				:				List[str]   		=		logging.get_logger(__name__)


_lowercase				:				Optional[Any]   		=		{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
_lowercase				:				Optional[Any]   		=		{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

_lowercase				:				str   		=		{
    "allenai/led-base-16384": 1_6_3_8_4,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	int				=(
			    list(range(ord('''!'''			)							,	ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			)							,	ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			)							,	ord('''ÿ'''			) + 1			)			)
			)
			lowerCamelCase__	:	Union[str, Any]				=bs[:]
			lowerCamelCase__	:	Tuple				=0
			for b in range(2**8			):
						if b not in bs:
									bs.append(__A			)
									cs.append(2**8 + n			)
									n += 1
			lowerCamelCase__	:	Optional[Any]				=[chr(__A			) for n in cs]
			return dict(zip(__A							,	__A			)			)






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=set()
			lowerCamelCase__	:	Tuple				=word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						lowerCamelCase__	:	List[str]				=char
			return pairs

class  __SCREAMING_SNAKE_CASE			(   __A     ):


					'''simple docstring'''





					_a         =							VOCAB_FILES_NAMES
					_a         =							PRETRAINED_VOCAB_FILES_MAP
					_a         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a         =							['input_ids', 'attention_mask']



					def __init__( self							:						Tuple,						lowerCamelCase							:						int,						lowerCamelCase							:						str,						lowerCamelCase							:						str="replace",						lowerCamelCase							:						int="<s>",						lowerCamelCase							:						Optional[int]="</s>",						lowerCamelCase							:						Optional[int]="</s>",						lowerCamelCase							:						List[Any]="<s>",						lowerCamelCase							:						str="<unk>",						lowerCamelCase							:						Dict="<pad>",						lowerCamelCase							:						Union[str, Any]="<mask>",						lowerCamelCase							:						str=False,						**lowerCamelCase							:						int,						)->      Dict:
								lowerCamelCase__	:	int				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else bos_token
								lowerCamelCase__	:	Union[str, Any]				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else eos_token
								lowerCamelCase__	:	str				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else sep_token
								lowerCamelCase__	:	Optional[int]				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else cls_token
								lowerCamelCase__	:	Union[str, Any]				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else unk_token
								lowerCamelCase__	:	List[Any]				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__	:	Any				=AddedToken(lowerCamelCase,						lstrip=lowerCamelCase,						rstrip=lowerCamelCase  ) if isinstance(lowerCamelCase,						lowerCamelCase  ) else mask_token

								super().__init__(
								    errors=lowerCamelCase,						bos_token=lowerCamelCase,						eos_token=lowerCamelCase,						unk_token=lowerCamelCase,						sep_token=lowerCamelCase,						cls_token=lowerCamelCase,						pad_token=lowerCamelCase,						mask_token=lowerCamelCase,						add_prefix_space=lowerCamelCase,						**lowerCamelCase,						)

								with open(lowerCamelCase,						encoding='''utf-8'''  ) as vocab_handle:
											lowerCamelCase__	:	str				=json.load(lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				={v: k for k, v in self.encoder.items()}
								lowerCamelCase__	:	Optional[int]				=errors  # how to handle errors in decoding
								lowerCamelCase__	:	Tuple				=bytes_to_unicode()
								lowerCamelCase__	:	int				={v: k for k, v in self.byte_encoder.items()}
								with open(lowerCamelCase,						encoding='''utf-8'''  ) as merges_handle:
											lowerCamelCase__	:	Union[str, Any]				=merges_handle.read().split('''\n'''  )[1:-1]
								lowerCamelCase__	:	Optional[Any]				=[tuple(merge.split()  ) for merge in bpe_merges]
								lowerCamelCase__	:	Optional[int]				=dict(zip(lowerCamelCase,						range(len(lowerCamelCase  )  )  )  )
								lowerCamelCase__	:	Optional[int]				={}
								lowerCamelCase__	:	Any				=add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								lowerCamelCase__	:	str				=re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''  )



					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def 	snake_case ( self							:						Union[str, Any]  )->      Optional[int]:
								return len(self.encoder  )



					def 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								return dict(self.encoder,						**self.added_tokens_encoder  )



					def 	snake_case ( self							:						int,						lowerCamelCase							:						str  )->      Optional[Any]:

								if token in self.cache:
											return self.cache[token]
								lowerCamelCase__	:	List[str]				=tuple(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=get_pairs(lowerCamelCase  )

								if not pairs:
											return token

								while True:
											lowerCamelCase__	:	Tuple				=min(lowerCamelCase,						key=lambda lowerCamelCase  : self.bpe_ranks.get(lowerCamelCase,						float('''inf'''  )  )  )
											if bigram not in self.bpe_ranks:
														break
											lowerCamelCase__	:	Optional[int]				=bigram
											lowerCamelCase__	:	Union[str, Any]				=[]
											lowerCamelCase__	:	Optional[Any]				=0
											while i < len(lowerCamelCase  ):
														try:
																	lowerCamelCase__	:	Dict				=word.index(lowerCamelCase,						lowerCamelCase  )
														except ValueError:
																	new_word.extend(word[i:]  )
																	break
														else:
																	new_word.extend(word[i:j]  )
																	lowerCamelCase__	:	Optional[int]				=j

														if word[i] == first and i < len(lowerCamelCase  ) - 1 and word[i + 1] == second:
																	new_word.append(first + second  )
																	i += 2
														else:
																	new_word.append(word[i]  )
																	i += 1
											lowerCamelCase__	:	List[str]				=tuple(lowerCamelCase  )
											lowerCamelCase__	:	str				=new_word
											if len(lowerCamelCase  ) == 1:
														break
											else:
														lowerCamelCase__	:	Optional[Any]				=get_pairs(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=''' '''.join(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=word
								return word



					def 	snake_case ( self							:						int,						lowerCamelCase							:						Optional[Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Dict				=[]
								for token in re.findall(self.pat,						lowerCamelCase  ):
											lowerCamelCase__	:	Optional[int]				=''''''.join(
											    self.byte_encoder[b] for b in token.encode('''utf-8'''  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase  ).split(''' '''  )  )
								return bpe_tokens



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						Union[str, Any]  )->      List[str]:
								return self.encoder.get(lowerCamelCase,						self.encoder.get(self.unk_token  )  )



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Optional[int]  )->      Any:
								return self.decoder.get(lowerCamelCase  )



					def 	snake_case ( self							:						List[str],						lowerCamelCase							:						List[Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	str				=''''''.join(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=bytearray([self.byte_decoder[c] for c in text]  ).decode('''utf-8''',						errors=self.errors  )
								return text



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Optional[str] = None  )->      Tuple[str]:

								if not os.path.isdir(lowerCamelCase  ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
											return
								lowerCamelCase__	:	Optional[Any]				=os.path.join(
								    lowerCamelCase,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
								lowerCamelCase__	:	List[Any]				=os.path.join(
								    lowerCamelCase,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

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

								lowerCamelCase__	:	List[str]				=0
								with open(lowerCamelCase,						'''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 lowerCamelCase  : 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__	:	Any				=token_index
														writer.write(''' '''.join(lowerCamelCase  ) + '''\n'''  )
														index += 1

								return vocab_file, merge_file



					def 	snake_case ( self							:						str,						lowerCamelCase							:						List[int],						lowerCamelCase							:						Optional[List[int]] = None  )->      List[int]:

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__	:	Optional[int]				=[self.cls_token_id]
								lowerCamelCase__	:	List[Any]				=[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						List[int],						lowerCamelCase							:						Optional[List[int]] = None,						lowerCamelCase							:						bool = False  )->      List[int]:

								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCamelCase,						token_ids_a=lowerCamelCase,						already_has_special_tokens=lowerCamelCase  )

								if token_ids_a is None:
											return [1] + ([0] * len(lowerCamelCase  )) + [1]
								return [1] + ([0] * len(lowerCamelCase  )) + [1, 1] + ([0] * len(lowerCamelCase  )) + [1]



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						List[int],						lowerCamelCase							:						Optional[List[int]] = None  )->      List[int]:
								lowerCamelCase__	:	Dict				=[self.sep_token_id]
								lowerCamelCase__	:	Optional[int]				=[self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep  ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Optional[int]=False,						**lowerCamelCase							:						Optional[Any]  )->      str:
								lowerCamelCase__	:	Tuple				=kwargs.pop('''add_prefix_space''',						self.add_prefix_space  )
								if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase  ) > 0 and not text[0].isspace()):
											lowerCamelCase__	:	Union[str, Any]				=''' ''' + text
								return (text, kwargs)




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Union[Dict[str, EncodedInput], BatchEncoding],						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD,						lowerCamelCase							:						Optional[int] = None,						lowerCamelCase							:						Optional[bool] = None,						)->      dict:
								lowerCamelCase__	:	Optional[int]				=super()._pad(
								    encoded_inputs=lowerCamelCase,						max_length=lowerCamelCase,						padding_strategy=lowerCamelCase,						pad_to_multiple_of=lowerCamelCase,						return_attention_mask=lowerCamelCase,						)

								# Load from model defaults
								if return_attention_mask is None:
											lowerCamelCase__	:	Tuple				='''attention_mask''' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
											lowerCamelCase__	:	Optional[Any]				=encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											lowerCamelCase__	:	str				=len(encoded_inputs['''global_attention_mask''']  ) != len(lowerCamelCase  )

											if needs_to_be_padded:
														lowerCamelCase__	:	Tuple				=len(lowerCamelCase  ) - len(encoded_inputs['''global_attention_mask''']  )

														if self.padding_side == "right":
																	# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																	lowerCamelCase__	:	List[str]				=(
																	    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																	)
														elif self.padding_side == "left":
																	lowerCamelCase__	:	Any				=[-1] * difference + encoded_inputs[
																	    '''global_attention_mask'''
																	]
														else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side  )  )

								return encoded_inputs









"""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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_lowercase				:				List[str]   		=		logging.getLogger(__name__)






def   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					str			):
			"""simple docstring"""
			# save results
			if os.path.exists(__lowerCamelCase			):
						if os.path.exists(os.path.join(__lowerCamelCase							,	'''config.json'''			)			) and os.path.isfile(
						    os.path.join(__lowerCamelCase							,	'''config.json'''			)			):
									os.remove(os.path.join(__lowerCamelCase							,	'''config.json'''			)			)
						if os.path.exists(os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			) and os.path.isfile(
						    os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			):
									os.remove(os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			)
			else:
						os.makedirs(__lowerCamelCase			)
			model.save_pretrained(__lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Dict=False			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=2
			if unlogit:
						lowerCamelCase__	:	Any				=torch.pow(__lowerCamelCase							,	__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=p * torch.log(__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=0
			return -plogp.sum(dim=-1			)






def   snake_case__     (	__lowerCamelCase       :					Any			):
			"""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   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Any=True							,	__lowerCamelCase       :					List[Any]=True							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=False			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=model.config.num_hidden_layers, model.config.num_attention_heads
			lowerCamelCase__	:	Optional[Any]				=torch.zeros(__lowerCamelCase							,	__lowerCamelCase			).to(args.device			)
			lowerCamelCase__	:	Optional[Any]				=torch.zeros(__lowerCamelCase							,	__lowerCamelCase			).to(args.device			)

			if head_mask is None:
						lowerCamelCase__	:	List[Any]				=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:
						lowerCamelCase__	:	Union[str, Any]				=None

			lowerCamelCase__	:	List[str]				=0.0
			lowerCamelCase__	:	Union[str, Any]				=0.0
			for step, inputs in enumerate(tqdm(__lowerCamelCase							,	desc='''Iteration'''							,	disable=args.local_rank not in [-1, 0]			)			):
						lowerCamelCase__	:	Any				=tuple(t.to(args.device			) for t in inputs			)
						((lowerCamelCase__)			,     )	:	Any				=inputs

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						lowerCamelCase__	:	Dict				=model(__lowerCamelCase							,	labels=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
						#  (loss), lm_logits, presents, (all hidden_states), (attentions)
						lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=(
						    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
						total_loss += loss.detach().cpu().numpy()
						if compute_entropy:
									for layer, attn in enumerate(__lowerCamelCase			):
												lowerCamelCase__	:	Any				=entropy(attn.detach()							,	__lowerCamelCase			)
												attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).sum(0			).detach()

						if compute_importance:
									head_importance += head_mask.grad.abs().detach()
						tot_tokens += torch.ones_like(__lowerCamelCase			).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__	:	int				=2
						lowerCamelCase__	:	List[str]				=torch.pow(torch.pow(__lowerCamelCase							,	__lowerCamelCase			).sum(-1			)							,	1 / exponent			)
						head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-2_0

			if not args.dont_normalize_global_importance:
						lowerCamelCase__	:	int				=(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print matrices
			if compute_entropy:
						logger.info('''Attention entropies'''			)
						print_ad_tensor(__lowerCamelCase			)
			if compute_importance:
						logger.info('''Head importance scores'''			)
						print_ad_tensor(__lowerCamelCase			)
			logger.info('''Head ranked by importance scores'''			)
			lowerCamelCase__	:	Optional[int]				=torch.zeros(head_importance.numel()							,	dtype=torch.long							,	device=args.device			)
			lowerCamelCase__	:	Dict				=torch.arange(
			    head_importance.numel()							,	device=args.device			)
			lowerCamelCase__	:	Any				=head_ranks.view_as(__lowerCamelCase			)
			print_ad_tensor(__lowerCamelCase			)
			return attn_entropy, head_importance, total_loss






def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=compute_heads_importance(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase			)
			lowerCamelCase__	:	int				=1 / loss  # instead of downsteam score use the LM loss
			logger.info('''Pruning: original score: %f, threshold: %f'''							,	__lowerCamelCase							,	original_score * args.masking_threshold			)

			lowerCamelCase__	:	Dict				=torch.ones_like(__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=max(1							,	int(new_head_mask.numel() * args.masking_amount			)			)

			lowerCamelCase__	:	List[Any]				=original_score
			while current_score >= original_score * args.masking_threshold:
						lowerCamelCase__	:	List[Any]				=new_head_mask.clone().detach()  # save current head mask
						# heads from least important to most - keep only not-masked heads
						lowerCamelCase__	:	int				=float('''Inf'''			)
						lowerCamelCase__	:	Union[str, Any]				=head_importance.view(-1			).sort()[1]

						if len(__lowerCamelCase			) <= num_to_mask:
									print('''BREAK BY num_to_mask'''			)
									break

						# mask heads
						lowerCamelCase__	:	List[str]				=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__	:	Optional[Any]				=0.0
						lowerCamelCase__	:	Dict				=new_head_mask.view_as(__lowerCamelCase			)
						lowerCamelCase__	:	Tuple				=new_head_mask.clone().detach()
						print_ad_tensor(__lowerCamelCase			)

						# Compute metric and head importance again
						lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=compute_heads_importance(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
						lowerCamelCase__	:	Any				=1 / loss
						logger.info(
						    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''							,	__lowerCamelCase							,	new_head_mask.sum()							,	new_head_mask.sum() / new_head_mask.numel() * 100							,	)

			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   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Optional[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=datetime.now()
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=compute_heads_importance(
			    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	compute_importance=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=1 / loss
			lowerCamelCase__	:	Optional[Any]				=datetime.now() - before_time

			lowerCamelCase__	:	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(__lowerCamelCase			)			)
			}

			for k, v in heads_to_prune.items():
						if isinstance(__lowerCamelCase							,	__lowerCamelCase			):
									lowerCamelCase__	:	Optional[int]				=[
									    v,
									]

			assert sum(len(__lowerCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
			model.prune_heads(__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=sum(p.numel() for p in model.parameters()			)

			lowerCamelCase__	:	Any				=datetime.now()
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=compute_heads_importance(
			    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	compute_importance=__lowerCamelCase							,	head_mask=__lowerCamelCase							,	actually_pruned=__lowerCamelCase							,	)

			lowerCamelCase__	:	str				=1 / loss
			lowerCamelCase__	:	Union[str, Any]				=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 * 100							,	)
			logger.info('''Pruning: score with masking: %f score with pruning: %f'''							,	__lowerCamelCase							,	__lowerCamelCase			)
			logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''							,	original_time / new_time * 100			)
			save_model(__lowerCamelCase							,	args.output_dir			)






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=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(
			    '''--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 s3'''							,	)
			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=128							,	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.'''			)
			lowerCamelCase__	:	List[Any]				=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:
						lowerCamelCase__	:	Dict				=torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
						lowerCamelCase__	:	Dict				=0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank			)
						lowerCamelCase__	:	str				=torch.device('''cuda'''							,	args.local_rank			)
						lowerCamelCase__	:	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			)			)			)

			lowerCamelCase__	:	Union[str, Any]				=GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

			# Distributed and parallel training
			model.to(args.device			)
			if args.local_rank != -1:
						lowerCamelCase__	:	List[Any]				=nn.parallel.DistributedDataParallel(
						    __lowerCamelCase							,	device_ids=[args.local_rank]							,	output_device=args.local_rank							,	find_unused_parameters=__lowerCamelCase			)
			elif args.n_gpu > 1:
						lowerCamelCase__	:	int				=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
			lowerCamelCase__	:	Union[str, Any]				=np.concatenate(
			    [
			        np.loadtxt(args.data_dir							,	dtype=np.intaa			),
			    ]			)
			lowerCamelCase__	:	Any				=(torch.from_numpy(__lowerCamelCase			),)
			lowerCamelCase__	:	List[Any]				=TensorDataset(*__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=RandomSampler(__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=DataLoader(__lowerCamelCase							,	sampler=__lowerCamelCase							,	batch_size=args.batch_size			)

			# 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:
						lowerCamelCase__	:	Optional[int]				=mask_heads(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
						prune_heads(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		main()




