


"""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 		__snake_case (		_lowerCamelCase							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					PerceiverTokenizer
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()
     __A   :						int									=					PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname  )

 @cached_property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=False ,						__lowerCamelCase=20 ,						__lowerCamelCase=5  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					[]
     for i in range(len(lowercase_  )  ):
         try:
             __A   :						int									=					tokenizer.decode([i] ,						clean_up_tokenization_spaces=lowercase_  )
         except UnicodeDecodeError:
             pass
         toks.append((i, tok)  )

     __A   :						Optional[Any]									=					list(filter(lambda __lowerCamelCase  : re.match(r'''^[ a-zA-Z]+$''' ,						t[1]  ) ,						lowercase_  )  )
     __A   :						str									=					list(filter(lambda __lowerCamelCase  : [t[0]] == tokenizer.encode(t[1] ,						add_special_tokens=lowercase_  ) ,						lowercase_  )  )
     if max_length is not None and len(lowercase_  ) > max_length:
         __A   :						Tuple									=					toks[:max_length]
     if min_length is not None and len(lowercase_  ) < min_length and len(lowercase_  ) > 0:
         while len(lowercase_  ) < min_length:
             __A   :						List[Any]									=					toks + toks
        # toks_str = [t[1] for t in toks]
     __A   :						Any									=					[t[0] for t in toks]

     # Ensure consistency
     __A   :						Optional[int]									=					tokenizer.decode(lowercase_ ,						clean_up_tokenization_spaces=lowercase_  )
     if " " not in output_txt and len(lowercase_  ) > 1:
         __A   :						List[Any]									=					(
             tokenizer.decode([toks_ids[0]] ,						clean_up_tokenization_spaces=lowercase_  )
             + ''' '''
             + tokenizer.decode(toks_ids[1:] ,						clean_up_tokenization_spaces=lowercase_  )
         )
     if with_prefix_space:
         __A   :						Dict									=					''' ''' + output_txt
     __A   :						List[Any]									=					tokenizer.encode(lowercase_ ,						add_special_tokens=lowercase_  )
     return output_txt, output_ids

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.perceiver_tokenizer
     __A   :						str									=					'''Unicode €.'''
     __A   :						int									=					tokenizer(lowercase_  )
     __A   :						Union[str, Any]									=					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids'''] ,						lowercase_  )

     # decoding
     __A   :						List[Any]									=					tokenizer.decode(lowercase_  )
     self.assertEqual(lowercase_ ,						'''[CLS]Unicode €.[SEP]'''  )

     __A   :						List[Any]									=					tokenizer('''e è é ê ë'''  )
     __A   :						Dict									=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids'''] ,						lowercase_  )
     # decoding
     __A   :						Optional[Any]									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.perceiver_tokenizer
     __A   :						int									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     __A   :						Union[str, Any]									=					[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
     __A   :						List[Any]									=					tokenizer(lowercase_ ,						padding=lowercase_ ,						return_tensors=lowercase_  )
     self.assertIsInstance(lowercase_ ,						lowercase_  )

     if FRAMEWORK != "jax":
         __A   :						Union[str, Any]									=					list(batch.input_ids.numpy()[0]  )
     else:
         __A   :						Optional[int]									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.perceiver_tokenizer
     __A   :						List[str]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						str									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.perceiver_tokenizer
     __A   :						List[Any]									=					[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     __A   :						int									=					tokenizer(
         text_target=lowercase_ ,						max_length=32 ,						padding='''max_length''' ,						truncation=lowercase_ ,						return_tensors=lowercase_  )
     self.assertEqual(32 ,						targets['''input_ids'''].shape[1]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					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
     __A   :						Union[str, Any]									=					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   :						List[Any]									=					tempfile.mkdtemp()

             __A   :						List[Any]									=					''' He is very happy, UNwant\u00E9d,running'''
             __A   :						Dict									=					tokenizer.encode(lowercase_ ,						add_special_tokens=lowercase_  )
             tokenizer.save_pretrained(lowercase_  )

             __A   :						str									=					tokenizer.__class__.from_pretrained(lowercase_  )
             __A   :						Union[str, Any]									=					after_tokenizer.encode(lowercase_ ,						add_special_tokens=lowercase_  )
             self.assertListEqual(lowercase_ ,						lowercase_  )

             shutil.rmtree(lowercase_  )

     __A   :						Optional[int]									=					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
             __A   :						Any									=					tempfile.mkdtemp()

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

             __A   :						Union[str, Any]									=					tokenizer.__class__.from_pretrained(lowercase_  )
             __A   :						List[str]									=					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  )

             __A   :						Optional[int]									=					tokenizer.__class__.from_pretrained(lowercase_ ,						model_max_length=43  )
             self.assertEqual(tokenizer.model_max_length ,						43  )

             shutil.rmtree(lowercase_  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					[]
     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:
                 __A   :						Union[str, Any]									=					json.load(lowercase_  )

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

             __A   :						int									=					[F"""<extra_id_{i}>""" for i in range(125  )]

             __A   :						Any									=					added_tokens_extra_ids + [
                 '''an_additional_special_token'''
             ]
             __A   :						Union[str, Any]									=					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
             __A   :						Dict									=					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
             __A   :						Tuple									=					added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,						lstrip=lowercase_  )]
             __A   :						Optional[Any]									=					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  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178]  ) ,						'''�'''  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.get_tokenizers(fast=lowercase_ ,						do_lower_case=lowercase_  )
     for tokenizer in tokenizers:
         with self.subTest(F"""{tokenizer.__class__.__name__}"""  ):
             __A   :						List[Any]									=					['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
             __A   :						Dict									=					tokenizer.convert_tokens_to_string(lowercase_  )

             self.assertIsInstance(lowercase_ ,						lowercase_  )











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




