




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
     import tensorflow as tf

if is_tensorflow_text_available():
     from transformers.models.bert import TFBertTokenizer


__a          =			['bert-base-uncased', 'bert-base-cased']
__a          =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():

     class   __a(					tf.keras.Model       ):






         """simple docstring"""





         def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
              super().__init__()
              UpperCAmelCase_      :							Tuple       =      tokenizer
              UpperCAmelCase_      :							List[Any]       =      AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      TFAutoModel.from_config(_SCREAMING_SNAKE_CASE  )





         def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      self.bert(**_SCREAMING_SNAKE_CASE  )
              return out["pooler_output"]

@require_tf
@require_tensorflow_text
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Any:
         super().setUp()

         UpperCAmelCase_      :							Optional[Any]       =      [
             BertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
         ]  # repeat for when fast_bert_tokenizer=false
         UpperCAmelCase_      :							Optional[int]       =      [TFBertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
             TFBertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,use_fast_bert_tokenizer=_SCREAMING_SNAKE_CASE  )
             for checkpoint in TOKENIZER_CHECKPOINTS
         ]
         assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

         UpperCAmelCase_      :							Optional[int]       =      [
             '''This is a straightforward English test sentence.''',
             '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
             '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
             '''And some much more rare Chinese: 齉 堃 齉堃''',
             '''Je vais aussi écrire en français pour tester les accents''',
             '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
         ]
         UpperCAmelCase_      :							Optional[int]       =      list(zip(self.test_sentences       ,self.test_sentences[::-1]  )  )





    def 							a__				(				self  )			->		Optional[Any]:
         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,self.tf_tokenizers  ):
              for test_inputs in (self.test_sentences, self.paired_sentences):
                   UpperCAmelCase_      :							Optional[Any]       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_tensors='''tf'''       ,padding='''longest'''  )
                   UpperCAmelCase_      :							int       =      tf_tokenizer(_SCREAMING_SNAKE_CASE  )

                   for key in python_outputs.keys():
                        self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape  )  )
                        self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,tf.intaa  ) == tf_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		Any:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							Union[str, Any]       =      tf_tokenizer(self.paired_sentences  )
              UpperCAmelCase_      :							Any       =      tf_tokenizer(
                  text=[sentence[0] for sentence in self.paired_sentences]       ,text_pair=[sentence[1] for sentence in self.paired_sentences]       ,)
              for key in merged_outputs.keys():
                   self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,tf.intaa  ) == separated_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		str:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							List[str]       =      tf.function(_SCREAMING_SNAKE_CASE  )
              for test_inputs in (self.test_sentences, self.paired_sentences):
                   UpperCAmelCase_      :							Tuple       =      tf.constant(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Tuple       =      compiled_tokenizer(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Union[str, Any]       =      tf_tokenizer(_SCREAMING_SNAKE_CASE  )

                   for key in eager_outputs.keys():
                        self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]  )  )





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_      :							Union[str, Any]       =      ModelToSave(tokenizer=_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							List[str]       =      tf.convert_to_tensor(self.test_sentences  )
              UpperCAmelCase_      :							Tuple       =      model(_SCREAMING_SNAKE_CASE  )  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                   UpperCAmelCase_      :							Any       =      Path(_SCREAMING_SNAKE_CASE  ) / '''saved.model'''
                   model.save(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      tf.keras.models.load_model(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      loaded_model(_SCREAMING_SNAKE_CASE  )
              # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
              self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output  )  )       ,1e-5  )





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __a          =			None


__a          =			logging.get_logger(__name__)


__a          =			'▁'

__a          =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a          =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__a          =			{
    'google/pegasus-xsum': 512,
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				PegasusTokenizer
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<mask_2>"       ,_SCREAMING_SNAKE_CASE="<mask_1>"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=103       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      offset

         if additional_special_tokens is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   raise TypeError(
                       f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE  )}, but is'''
                       f''' {type(_SCREAMING_SNAKE_CASE  )}'''  )

              UpperCAmelCase_      :							str       =      (
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE  )       ,self.offset - 1  )
              ]

              if len(set(_SCREAMING_SNAKE_CASE  )  ) != len(_SCREAMING_SNAKE_CASE  ):
                   raise ValueError(
                       '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                       f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
              UpperCAmelCase_      :							int       =      additional_special_tokens_extended
         else:
              UpperCAmelCase_      :							Any       =      [mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [f'''<unk_{i}>''' for i in range(2       ,self.offset  )]

         super().__init__(
             _SCREAMING_SNAKE_CASE       ,tokenizer_file=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,mask_token_sent=_SCREAMING_SNAKE_CASE       ,offset=_SCREAMING_SNAKE_CASE       ,additional_special_tokens=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							str       =      vocab_file
         UpperCAmelCase_      :							Dict       =      False if not self.vocab_file else True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Dict       =      set(self.all_special_ids  )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
              raise ValueError(
                  '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
                  f''' {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}'''  )

         return [1 if x in all_special_ids else 0 for x in seq]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:
         if already_has_special_tokens:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  )
         elif token_ids_a is None:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  ) + [1]
         else:
              return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''  )

         if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Optional[int]       =      os.path.join(
             _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

         if os.path.abspath(self.vocab_file  ) != os.path.abspath(_SCREAMING_SNAKE_CASE  ):
              copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE  )

         return (out_vocab_file,)