





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :str       =				['''BertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Dict       =				[
      '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BertForMaskedLM''',
      '''BertForMultipleChoice''',
      '''BertForNextSentencePrediction''',
      '''BertForPreTraining''',
      '''BertForQuestionAnswering''',
      '''BertForSequenceClassification''',
      '''BertForTokenClassification''',
      '''BertLayer''',
      '''BertLMHeadModel''',
      '''BertModel''',
      '''BertPreTrainedModel''',
      '''load_tf_weights_in_bert''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :List[Any]       =				[
      '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFBertEmbeddings''',
      '''TFBertForMaskedLM''',
      '''TFBertForMultipleChoice''',
      '''TFBertForNextSentencePrediction''',
      '''TFBertForPreTraining''',
      '''TFBertForQuestionAnswering''',
      '''TFBertForSequenceClassification''',
      '''TFBertForTokenClassification''',
      '''TFBertLMHeadModel''',
      '''TFBertMainLayer''',
      '''TFBertModel''',
      '''TFBertPreTrainedModel''',
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Any       =				['''TFBertTokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Optional[int]       =				[
      '''FlaxBertForCausalLM''',
      '''FlaxBertForMaskedLM''',
      '''FlaxBertForMultipleChoice''',
      '''FlaxBertForNextSentencePrediction''',
      '''FlaxBertForPreTraining''',
      '''FlaxBertForQuestionAnswering''',
      '''FlaxBertForSequenceClassification''',
      '''FlaxBertForTokenClassification''',
      '''FlaxBertModel''',
      '''FlaxBertPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''







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():
   A     ='pt'
elif is_tf_available():
   A     ='tf'
else:
   A     ='jax'


class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					PerceiverTokenizer
       __a				:     str   				=					False
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[str]=False   ,				lowercase						:	Union[str, Any]=20   ,				lowercase						:	Union[str, Any]=5				):



           '''simple docstring'''

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

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

           # Ensure consistency
           UpperCAmelCase			       =		tokenizer.decode(lowercase   ,				clean_up_tokenization_spaces=lowercase				)
           if " " not in output_txt and len(lowercase				) > 1:
               UpperCAmelCase			       =		(
                   tokenizer.decode([toks_ids[0]]   ,				clean_up_tokenization_spaces=lowercase				)
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]   ,				clean_up_tokenization_spaces=lowercase				)
               )
           if with_prefix_space:
               UpperCAmelCase			       =		''' ''' + output_txt
           UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
           return output_txt, output_ids
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		'''Unicode €.'''
           UpperCAmelCase			       =		tokenizer(lowercase				)
           UpperCAmelCase			       =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)

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

           UpperCAmelCase			       =		tokenizer('''e è é ê ë'''				)
           UpperCAmelCase			       =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)
           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[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     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCAmelCase			       =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           self.assertIsInstance(lowercase   ,				lowercase				)

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

           self.assertListEqual(lowercase   ,				lowercase				)

           self.assertEqual((2, 38)   ,				batch.input_ids.shape				)
           self.assertEqual((2, 38)   ,				batch.attention_mask.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCAmelCase			       =		tokenizer(
               text_target=lowercase   ,				max_length=32   ,				padding='''max_length'''   ,				truncation=lowercase   ,				return_tensors=lowercase				)
           self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   self.assertNotEqual(tokenizer.model_max_length   ,				42				)

        # Now let's start the test
           UpperCAmelCase			       =		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
                   UpperCAmelCase			       =		tempfile.mkdtemp()

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

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)

                   shutil.rmtree(lowercase				)

           UpperCAmelCase			       =		self.get_tokenizers(model_max_length=42				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCAmelCase			       =		tempfile.mkdtemp()

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

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)
                   self.assertIn('''new_additional_special_token'''   ,				after_tokenizer.additional_special_tokens				)
                   self.assertEqual(after_tokenizer.model_max_length   ,				42				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase   ,				model_max_length=43				)
                   self.assertEqual(tokenizer.model_max_length   ,				43				)

                   shutil.rmtree(lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           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(lowercase				)

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

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

                   UpperCAmelCase			       =		[f"<extra_id_{i}>" for i in range(125				)]

                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]
                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]

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

                   # 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
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				)
                   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
                   UpperCAmelCase			       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,				lstrip=lowercase				)]
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				additional_special_tokens=lowercase   ,				)

                   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     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)   ,				'''�'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

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

                   self.assertIsInstance(lowercase   ,				lowercase				)




