

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      __lowercase				      =						"""pt"""
elif is_tf_available():
      __lowercase				      =						"""tf"""
else:
      __lowercase				      =						"""jax"""

class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    PerceiverTokenizer
   _lowercase     :       Dict  					=    False

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Any:
         """simple docstring"""





         super().setUp()
         A_							       =		PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname       )

   @cached_property
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''       )

   def     UpperCamelCase					(				self	:    List[str]			,						**lowerCamelCase__	:    str       ) ->			PerceiverTokenizer:
         """simple docstring"""





         return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    List[str]=2_0			,						lowerCamelCase__	:    Union[str, Any]=5       ) ->			Tuple[str, list]:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               try:
                     A_							       =		tokenizer.decode([i]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
               except UnicodeDecodeError:
                     pass
               toks.append((i, tok)       )

         A_							       =		list(filter(lambda lowerCamelCase__       : re.match(R'''^[ a-zA-Z]+$'''			,						t[1]       )			,						lowerCamelCase__       )       )
         A_							       =		list(filter(lambda lowerCamelCase__       : [t[0]] == tokenizer.encode(t[1]			,						add_special_tokens=lowerCamelCase__       )			,						lowerCamelCase__       )       )
         if max_length is not None and len(lowerCamelCase__       ) > max_length:
               A_							       =		toks[:max_length]
         if min_length is not None and len(lowerCamelCase__       ) < min_length and len(lowerCamelCase__       ) > 0:
               while len(lowerCamelCase__       ) < min_length:
                     A_							       =		toks + toks
        # toks_str = [t[1] for t in toks]
         A_							       =		[t[0] for t in toks]

         # Ensure consistency
         A_							       =		tokenizer.decode(lowerCamelCase__			,						clean_up_tokenization_spaces=lowerCamelCase__       )
         if " " not in output_txt and len(lowerCamelCase__       ) > 1:
               A_							       =		(
                   tokenizer.decode([toks_ids[0]]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
               )
         if with_prefix_space:
               A_							       =		''' ''' + output_txt
         A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
         return output_txt, output_ids

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		'''Unicode €.'''
         A_							       =		tokenizer(lowerCamelCase__       )
         A_							       =		[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )

         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]Unicode €.[SEP]'''       )

         A_							       =		tokenizer('''e è é ê ë'''       )
         A_							       =		[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )
         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]e è é ê ë[SEP]'''       )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''       )       )			,						'''[CLS]e è é ê ë[SEP]'''       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         A_							       =		[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )

         if FRAMEWORK != "jax":
               A_							       =		list(batch.input_ids.numpy()[0]       )
         else:
               A_							       =		list(batch.input_ids.tolist()[0]       )

         self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

         self.assertEqual((2, 3_8)			,						batch.input_ids.shape       )
         self.assertEqual((2, 3_8)			,						batch.attention_mask.shape       )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('''input_ids'''			,						lowerCamelCase__       )
         self.assertIn('''attention_mask'''			,						lowerCamelCase__       )
         self.assertNotIn('''decoder_input_ids'''			,						lowerCamelCase__       )
         self.assertNotIn('''decoder_attention_mask'''			,						lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         A_							       =		tokenizer(
             text_target=lowerCamelCase__			,						max_length=3_2			,						padding='''max_length'''			,						truncation=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertEqual(3_2			,						targets['''input_ids'''].shape[1]       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     self.assertNotEqual(tokenizer.model_max_length			,						4_2       )

        # Now let's start the test
         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

                     A_							       =		''' He is very happy, UNwant\u00E9d,running'''
                     A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     tokenizer.save_pretrained(lowerCamelCase__       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

                     shutil.rmtree(lowerCamelCase__       )

         A_							       =		self.get_tokenizers(model_max_length=4_2       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

                     A_							       =		''' He is very happy, UNwant\u00E9d,running'''
                     tokenizer.add_tokens(['''bim''', '''bambam''']       )
                     A_							       =		tokenizer.additional_special_tokens
                     additional_special_tokens.append('''new_additional_special_token'''       )
                     tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                     A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     tokenizer.save_pretrained(lowerCamelCase__       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )
                     self.assertIn('''new_additional_special_token'''			,						after_tokenizer.additional_special_tokens       )
                     self.assertEqual(after_tokenizer.model_max_length			,						4_2       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__			,						model_max_length=4_3       )
                     self.assertEqual(tokenizer.model_max_length			,						4_3       )

                     shutil.rmtree(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		[]
         if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

         if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                     tokenizer_utils.save_pretrained(lowerCamelCase__       )

                     with open(os.path.join(lowerCamelCase__			,						'''special_tokens_map.json'''       )			,						encoding='''utf-8'''       ) as json_file:
                           A_							       =		json.load(lowerCamelCase__       )

                     with open(os.path.join(lowerCamelCase__			,						'''tokenizer_config.json'''       )			,						encoding='''utf-8'''       ) as json_file:
                           A_							       =		json.load(lowerCamelCase__       )

                     A_							       =		[F"<extra_id_{i}>" for i in range(1_2_5       )]

                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]
                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]

                     with open(os.path.join(lowerCamelCase__			,						'''special_tokens_map.json'''       )			,						'''w'''			,						encoding='''utf-8'''       ) as outfile:
                           json.dump(lowerCamelCase__			,						lowerCamelCase__       )
                     with open(os.path.join(lowerCamelCase__			,						'''tokenizer_config.json'''       )			,						'''w'''			,						encoding='''utf-8'''       ) as outfile:
                           json.dump(lowerCamelCase__			,						lowerCamelCase__       )

                     # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                     # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                     # "special_tokens_map.json" files
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						)
                     self.assertIn(
                         '''an_additional_special_token'''			,						tokenizer_without_change_in_init.additional_special_tokens       )
                     self.assertEqual(
                         ['''an_additional_special_token''']			,						tokenizer_without_change_in_init.convert_ids_to_tokens(
                             tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']       )       )			,						)

                     # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                     A_							       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,						lstrip=lowerCamelCase__       )]
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						additional_special_tokens=lowerCamelCase__			,						)

                     self.assertIn('''a_new_additional_special_token'''			,						tokenizer.additional_special_tokens       )
                     self.assertEqual(
                         ['''a_new_additional_special_token''']			,						tokenizer.convert_ids_to_tokens(
                             tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']       )       )			,						)

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]       )			,						'''�'''       )

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Dict:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.get_tokenizers(fast=lowerCamelCase__			,						do_lower_case=lowerCamelCase__       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     A_							       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                     A_							       =		tokenizer.convert_tokens_to_string(lowerCamelCase__       )

                     self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )








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				      =						logging.getLogger(__name__)







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      if os.path.exists(SCREAMING_SNAKE_CASE   ):
            if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE		,				'''config.json'''   )   ) and os.path.isfile(
                os.path.join(SCREAMING_SNAKE_CASE		,				'''config.json'''   )   ):
                  os.remove(os.path.join(SCREAMING_SNAKE_CASE		,				'''config.json'''   )   )
            if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE		,				'''pytorch_model.bin'''   )   ) and os.path.isfile(
                os.path.join(SCREAMING_SNAKE_CASE		,				'''pytorch_model.bin'''   )   ):
                  os.remove(os.path.join(SCREAMING_SNAKE_CASE		,				'''pytorch_model.bin'''   )   )
      else:
            os.makedirs(SCREAMING_SNAKE_CASE   )
      model.save_pretrained(SCREAMING_SNAKE_CASE   )







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False   ):
      '''simple docstring'''







      A_							       =		2
      if unlogit:
            A_							       =		torch.pow(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      A_							       =		p * torch.log(SCREAMING_SNAKE_CASE   )
      A_							       =		0
      return -plogp.sum(dim=-1   )







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      logger.info('''lv, h >\t''' + '''\t'''.join(f"{x + 1}" for x in range(len(SCREAMING_SNAKE_CASE   )   )   )   )
      for row in range(len(SCREAMING_SNAKE_CASE   )   ):
            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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=False   ):
      '''simple docstring'''







      A_       ,A_							       =		model.config.num_hidden_layers, model.config.num_attention_heads
      A_							       =		torch.zeros(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )
      A_							       =		torch.zeros(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )

      if head_mask is None:
            A_							       =		torch.ones(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )

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

      A_							       =		0.0
      A_							       =		0.0
      for step, inputs in enumerate(tqdm(SCREAMING_SNAKE_CASE		,				desc='''Iteration'''		,				disable=args.local_rank not in [-1, 0]   )   ):
            A_							       =		tuple(t.to(args.device   ) for t in inputs   )
            ((A_)       ,)							       =		inputs

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            A_							       =		model(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE   )
            #  (loss), lm_logits, presents, (all hidden_states), (attentions)
            A_       ,A_       ,A_							       =		(
                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(SCREAMING_SNAKE_CASE   ):
                        A_							       =		entropy(attn.detach()		,				SCREAMING_SNAKE_CASE   )
                        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(SCREAMING_SNAKE_CASE   ).float().detach().sum().data

      # Normalize
      attn_entropy /= tot_tokens
      head_importance /= tot_tokens
      # Layerwise importance normalization
      if not args.dont_normalize_importance_by_layer:
            A_							       =		2
            A_							       =		torch.pow(torch.pow(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).sum(-1   )		,				1 / exponent   )
            head_importance /= norm_by_layer.unsqueeze(-1   ) + 1E-20

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

      # Print matrices
      if compute_entropy:
            logger.info('''Attention entropies'''   )
            print_ad_tensor(SCREAMING_SNAKE_CASE   )
      if compute_importance:
            logger.info('''Head importance scores'''   )
            print_ad_tensor(SCREAMING_SNAKE_CASE   )
      logger.info('''Head ranked by importance scores'''   )
      A_							       =		torch.zeros(head_importance.numel()		,				dtype=torch.long		,				device=args.device   )
      A_							       =		torch.arange(
          head_importance.numel()		,				device=args.device   )
      A_							       =		head_ranks.view_as(SCREAMING_SNAKE_CASE   )
      print_ad_tensor(SCREAMING_SNAKE_CASE   )
      return attn_entropy, head_importance, total_loss







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_       ,A_       ,A_							       =		compute_heads_importance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE   )
      A_							       =		1 / loss  # instead of downsteam score use the LM loss
      logger.info('''Pruning: original score: %f, threshold: %f'''		,				SCREAMING_SNAKE_CASE		,				original_score * args.masking_threshold   )

      A_							       =		torch.ones_like(SCREAMING_SNAKE_CASE   )
      A_							       =		max(1		,				int(new_head_mask.numel() * args.masking_amount   )   )

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

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

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

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

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

      return head_mask







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		datetime.now()
      A_       ,A_       ,A_							       =		compute_heads_importance(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE		,				compute_importance=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE   )
      A_							       =		1 / loss
      A_							       =		datetime.now() - before_time

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

      for k, v in heads_to_prune.items():
            if isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
                  A_							       =		[
                      v,
                  ]

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

      A_							       =		datetime.now()
      A_       ,A_       ,A_							       =		compute_heads_importance(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE		,				compute_importance=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE		,				actually_pruned=SCREAMING_SNAKE_CASE		,				)

      A_							       =		1 / loss
      A_							       =		datetime.now() - before_time

      logger.info(
          '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				pruned_num_params / original_num_params * 100		,				)
      logger.info('''Pruning: score with masking: %f score with pruning: %f'''		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''		,				original_time / new_time * 100   )
      save_model(SCREAMING_SNAKE_CASE		,				args.output_dir   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--data_dir'''		,				default=SCREAMING_SNAKE_CASE		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				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=SCREAMING_SNAKE_CASE		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help='''Path to pretrained model or model identifier from huggingface.co/models'''		,				)
      parser.add_argument(
          '''--output_dir'''		,				default=SCREAMING_SNAKE_CASE		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help='''The output directory where the model predictions and checkpoints will be written.'''		,				)

      # Other parameters
      parser.add_argument(
          '''--config_name'''		,				default=''''''		,				type=SCREAMING_SNAKE_CASE		,				help='''Pretrained config name or path if not the same as model_name_or_path'''		,				)
      parser.add_argument(
          '''--tokenizer_name'''		,				default=''''''		,				type=SCREAMING_SNAKE_CASE		,				help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''		,				)
      parser.add_argument(
          '''--cache_dir'''		,				default=SCREAMING_SNAKE_CASE		,				type=SCREAMING_SNAKE_CASE		,				help='''Where do you want to store the pre-trained models downloaded from s3'''		,				)
      parser.add_argument(
          '''--data_subset'''		,				type=SCREAMING_SNAKE_CASE		,				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=SCREAMING_SNAKE_CASE		,				help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''		,				)
      parser.add_argument(
          '''--masking_amount'''		,				default=0.1		,				type=SCREAMING_SNAKE_CASE		,				help='''Amount to heads to masking at each masking step.'''   )
      parser.add_argument('''--metric_name'''		,				default='''acc'''		,				type=SCREAMING_SNAKE_CASE		,				help='''Metric to use for head masking.'''   )

      parser.add_argument(
          '''--max_seq_length'''		,				default=128		,				type=SCREAMING_SNAKE_CASE		,				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=SCREAMING_SNAKE_CASE		,				help='''Batch size.'''   )

      parser.add_argument('''--seed'''		,				type=SCREAMING_SNAKE_CASE		,				default=42   )
      parser.add_argument('''--local_rank'''		,				type=SCREAMING_SNAKE_CASE		,				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=SCREAMING_SNAKE_CASE		,				default=''''''		,				help='''Can be used for distant debugging.'''   )
      parser.add_argument('''--server_port'''		,				type=SCREAMING_SNAKE_CASE		,				default=''''''		,				help='''Can be used for distant debugging.'''   )
      A_							       =		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=SCREAMING_SNAKE_CASE   )
            ptvsd.wait_for_attach()

      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
            A_							       =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )
            A_							       =		0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank   )
            A_							       =		torch.device('''cuda'''		,				args.local_rank   )
            A_							       =		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   )   )   )

      A_							       =		GPTaLMHeadModel.from_pretrained(args.model_name_or_path   )

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

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

      # Prepare dataset
      A_							       =		np.concatenate(
          [
              np.loadtxt(args.data_dir		,				dtype=np.intaa   ),
          ]   )
      A_							       =		(torch.from_numpy(SCREAMING_SNAKE_CASE   ),)
      A_							       =		TensorDataset(*SCREAMING_SNAKE_CASE   )
      A_							       =		RandomSampler(SCREAMING_SNAKE_CASE   )
      A_							       =		DataLoader(SCREAMING_SNAKE_CASE		,				sampler=SCREAMING_SNAKE_CASE		,				batch_size=args.batch_size   )

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

      # 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:
            A_							       =		mask_heads(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            prune_heads(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()





