






"""simple docstring"""


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class __a ( snake_case_			,unittest.TestCase					):
  __lowercase     :     int       						=       BartTokenizer
  __lowercase     :     Tuple       						=       BartTokenizerFast
  __lowercase     :     Optional[Any]       						=       True
  __lowercase     :     Any       						=       filter_roberta_detectors



  def     A_					(self	:		Union[str, Any] )		->				str:
   super().setUp()
   __A						:							Any      =						[
       'l',
       'o',
       'w',
       'e',
       'r',
       's',
       't',
       'i',
       'd',
       'n',
       '\u0120',
       '\u0120l',
       '\u0120n',
       '\u0120lo',
       '\u0120low',
       'er',
       '\u0120lowest',
       '\u0120newer',
       '\u0120wider',
       '<unk>',
   ]
   __A						:							Tuple      =						dict(zip(lowercase_					,      range(len(lowercase_ ) ) ) )
   __A						:							List[str]      =						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
   __A						:							List[str]      =						{'unk_token': '<unk>'}

   __A						:							Union[str, Any]      =						os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['vocab_file'] )
   __A						:							Dict      =						os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['merges_file'] )
   with open(self.vocab_file					,      'w'					,      encoding='utf-8' ) as fp:
    fp.write(json.dumps(lowercase_ ) + '\n' )
   with open(self.merges_file					,      'w'					,      encoding='utf-8' ) as fp:
    fp.write('\n'.join(lowercase_ ) )



  def     A_					(self	:		int					,      **lowercase_	:		Tuple )		->				Any:
   kwargs.update(self.special_tokens_map )
   return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **lowercase_ )



  def     A_					(self	:		Optional[int]					,      **lowercase_	:		int )		->				Optional[int]:
   kwargs.update(self.special_tokens_map )
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,      **lowercase_ )



  def     A_					(self	:		int					,      lowercase_	:		Any )		->				Dict:
   return "lower newer", "lower newer"



  @cached_property
  def     A_					(self	:		Optional[int] )		->				int:
   return BartTokenizer.from_pretrained('facebook/bart-large' )



  @cached_property
  def     A_					(self	:		Optional[int] )		->				str:
   return BartTokenizerFast.from_pretrained('facebook/bart-large' )



  @require_torch
  def     A_					(self	:		Optional[int] )		->				Optional[Any]:
   __A						:							Optional[Any]      =						['A long paragraph for summarization.', 'Another paragraph for summarization.']
   __A						:							List[Any]      =						[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    __A						:							List[Any]      =						tokenizer(lowercase_					,      max_length=len(lowercase_ )					,      padding=lowercase_					,      return_tensors='pt' )
    self.assertIsInstance(lowercase_					,      lowercase_ )

    self.assertEqual((2, 9)					,      batch.input_ids.shape )
    self.assertEqual((2, 9)					,      batch.attention_mask.shape )
    __A						:							List[Any]      =						batch.input_ids.tolist()[0]
    self.assertListEqual(lowercase_					,      lowercase_ )
    # Test that special tokens are reset



  @require_torch
  def     A_					(self	:		str )		->				List[Any]:
   __A						:							Tuple      =						['A long paragraph for summarization.', 'Another paragraph for summarization.']
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    __A						:							Optional[int]      =						tokenizer(lowercase_					,      padding=lowercase_					,      return_tensors='pt' )
    # check if input_ids are returned and no labels
    self.assertIn('input_ids'					,      lowercase_ )
    self.assertIn('attention_mask'					,      lowercase_ )
    self.assertNotIn('labels'					,      lowercase_ )
    self.assertNotIn('decoder_attention_mask'					,      lowercase_ )



  @require_torch
  def     A_					(self	:		List[str] )		->				Union[str, Any]:
   __A						:							Dict      =						[
       'Summary of the text.',
       'Another summary.',
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    __A						:							str      =						tokenizer(text_target=lowercase_					,      max_length=32					,      padding='max_length'					,      return_tensors='pt' )
    self.assertEqual(32					,      targets['input_ids'].shape[1] )



  @require_torch
  def     A_					(self	:		Optional[int] )		->				Any:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    __A						:							int      =						tokenizer(
        ['I am a small frog' * 1_024, 'I am a small frog']					,      padding=lowercase_					,      truncation=lowercase_					,      return_tensors='pt' )
    self.assertIsInstance(lowercase_					,      lowercase_ )
    self.assertEqual(batch.input_ids.shape					,      (2, 1_024) )



  @require_torch
  def     A_					(self	:		Union[str, Any] )		->				Optional[int]:
   __A						:							List[str]      =						['A long paragraph for summarization.']
   __A						:							Any      =						[
       'Summary of the text.',
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
    __A						:							Optional[Any]      =						tokenizer(lowercase_					,      return_tensors='pt' )
    __A						:							Optional[Any]      =						tokenizer(text_target=lowercase_					,      return_tensors='pt' )
    __A						:							Optional[int]      =						inputs['input_ids']
    __A						:							Optional[Any]      =						targets['input_ids']
    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )



  def     A_					(self	:		Optional[int] )		->				List[str]:
   pass



  def     A_					(self	:		Any )		->				Optional[int]:
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
     __A						:							List[str]      =						self.rust_tokenizer_class.from_pretrained(lowercase_					,      **lowercase_ )
     __A						:							str      =						self.tokenizer_class.from_pretrained(lowercase_					,      **lowercase_ )
     __A						:							str      =						'A, <mask> AllenNLP sentence.'
     __A						:							List[Any]      =						tokenizer_r.encode_plus(lowercase_					,      add_special_tokens=lowercase_					,      return_token_type_ids=lowercase_ )
     __A						:							Any      =						tokenizer_p.encode_plus(lowercase_					,      add_special_tokens=lowercase_					,      return_token_type_ids=lowercase_ )

     # token_type_ids should put 0 everywhere
     self.assertEqual(sum(tokens_r['token_type_ids'] )					,      sum(tokens_p['token_type_ids'] ) )

     # attention_mask should put 1 everywhere, so sum over length should be 1
     self.assertEqual(
         sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )					,      sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )					,      )

     __A						:							Optional[Any]      =						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
     __A						:							Optional[int]      =						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

     # Rust correctly handles the space before the mask while python doesnt
     self.assertSequenceEqual(tokens_p['input_ids']					,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
     self.assertSequenceEqual(tokens_r['input_ids']					,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

     self.assertSequenceEqual(
         lowercase_					,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
     self.assertSequenceEqual(
         lowercase_					,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(inputs > threshold).type(inputs.type() )
   if mask.sum() < nb_min:
    # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							List[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Tuple      =						1
   return mask





