






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






class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				PerceiverTokenizer
  lowercase							=				False

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






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

  @cached_property
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






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

  def   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				PerceiverTokenizer:



        """simple docstring"""






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

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=20      ,		_SCREAMING_SNAKE_CASE=5				)  ->				Tuple[str, list]:



        """simple docstring"""






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

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: re.match(R"""^[ a-zA-Z]+$"""      ,		t[1]				)      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: [t[0]] == tokenizer.encode(t[1]      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE				)				)
        if max_length is not None and len(_SCREAMING_SNAKE_CASE				) > max_length:
              UpperCamelCase      = toks[:max_length]
        if min_length is not None and len(_SCREAMING_SNAKE_CASE				) < min_length and len(_SCREAMING_SNAKE_CASE				) > 0:
              while len(_SCREAMING_SNAKE_CASE				) < 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(_SCREAMING_SNAKE_CASE      ,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
        if " " not in output_txt and len(_SCREAMING_SNAKE_CASE				) > 1:
              UpperCamelCase      = (
                  tokenizer.decode([toks_ids[0]]      ,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
                  + """ """
                  + tokenizer.decode(toks_ids[1:]      ,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
              )
        if with_prefix_space:
              UpperCamelCase      = """ """ + output_txt
        UpperCamelCase      = tokenizer.encode(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
        return output_txt, output_ids

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






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

        # decoding
        UpperCamelCase      = tokenizer.decode(_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		"""[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"""]      ,		_SCREAMING_SNAKE_CASE				)
        # decoding
        UpperCamelCase      = tokenizer.decode(_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		"""[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				)  ->				int:



        """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(_SCREAMING_SNAKE_CASE      ,		padding=_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE				)
        self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

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

        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        self.assertEqual((2, 38)      ,		batch.input_ids.shape				)
        self.assertEqual((2, 38)      ,		batch.attention_mask.shape				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






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

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = self.perceiver_tokenizer
        UpperCamelCase      = [
            """Summary of the text.""",
            """Another summary.""",
        ]
        UpperCamelCase      = tokenizer(
            text_target=_SCREAMING_SNAKE_CASE      ,		max_length=32      ,		padding="""max_length"""      ,		truncation=_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE				)
        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(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
                    tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = after_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
                    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

                    shutil.rmtree(_SCREAMING_SNAKE_CASE				)

        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(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
                    tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE				)
                    UpperCamelCase      = after_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
                    self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
                    self.assertIn("""new_additional_special_token"""      ,		after_tokenizer.additional_special_tokens				)
                    self.assertEqual(after_tokenizer.model_max_length      ,		42				)

                    UpperCamelCase      = tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE      ,		model_max_length=43				)
                    self.assertEqual(tokenizer.model_max_length      ,		43				)

                    shutil.rmtree(_SCREAMING_SNAKE_CASE				)

  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(_SCREAMING_SNAKE_CASE				)

                    with open(os.path.join(_SCREAMING_SNAKE_CASE      ,		"""special_tokens_map.json"""				)      ,		encoding="""utf-8"""				) as json_file:
                          UpperCamelCase      = json.load(_SCREAMING_SNAKE_CASE				)

                    with open(os.path.join(_SCREAMING_SNAKE_CASE      ,		"""tokenizer_config.json"""				)      ,		encoding="""utf-8"""				) as json_file:
                          UpperCamelCase      = json.load(_SCREAMING_SNAKE_CASE				)

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

                    # 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(
                        _SCREAMING_SNAKE_CASE      ,		)
                    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=_SCREAMING_SNAKE_CASE				)]
                    UpperCamelCase      = tokenizer_class.from_pretrained(
                        _SCREAMING_SNAKE_CASE      ,		additional_special_tokens=_SCREAMING_SNAKE_CASE      ,		)

                    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				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]				)      ,		"""�"""				)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        pass


  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE      ,		do_lower_case=_SCREAMING_SNAKE_CASE				)
        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(_SCREAMING_SNAKE_CASE				)

                    self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
