






"""simple docstring"""


from typing import TYPE_CHECKING

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


lowercase_				     =     {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_				     =     [
           'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'DebertaForMaskedLM',
           'DebertaForQuestionAnswering',
           'DebertaForSequenceClassification',
           'DebertaForTokenClassification',
           'DebertaModel',
           'DebertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_				     =     [
           'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFDebertaForMaskedLM',
           'TFDebertaForQuestionAnswering',
           'TFDebertaForSequenceClassification',
           'TFDebertaForTokenClassification',
           'TFDebertaModel',
           'TFDebertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

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












"""simple docstring"""


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_				     =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = PegasusTokenizer
   A_						:	int						 = PegasusTokenizerFast
   A_						:	Optional[Any]						 = True
   A_						:	Union[str, Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= '''</s>'''
     __A									= 1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase  ),       _lowerCamelCase  )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase  ),       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= list(self.get_tokenizer().get_vocab().keys()  )

     self.assertEqual(vocab_keys[0],       '''<pad>'''  )
     self.assertEqual(vocab_keys[1],       '''</s>'''  )
     self.assertEqual(vocab_keys[-1],       '''v'''  )
     self.assertEqual(len(_lowerCamelCase  ),       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     self.assertEqual(self.get_tokenizer().vocab_size,       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
         ''' </s> <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # <mask_1> masks whole sentence while <mask_2> masks single word
     __A									= '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
     __A									= [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self._large_tokenizer
     # The tracebacks for the following asserts are **better** without messages or self.assertEqual
     assert tokenizer.vocab_size == 9_61_03
     assert tokenizer.pad_token_id == 0
     assert tokenizer.eos_token_id == 1
     assert tokenizer.offset == 1_03
     assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
     assert tokenizer.unk_token == "<unk>"
     assert tokenizer.model_max_length == 10_24
     __A									= '''To ensure a smooth flow of bank resolutions.'''
     __A									= [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 1_50, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 10_24)
     assert batch.attention_mask.shape == (2, 10_24)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # fmt: off
     __A									= {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCamelCase,       model_name='''google/bigbird-pegasus-large-arxiv''',       revision='''ba85d0851d708441f91440d509690f1ab6353415''',       )





@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = PegasusTokenizer
   A_						:	Union[str, Any]						 = PegasusTokenizerFast
   A_						:	Any						 = True
   A_						:	str						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= PegasusTokenizer(_lowerCamelCase,       offset=0,       mask_token_sent=_lowerCamelCase,       mask_token='''[MASK]'''  )
     tokenizer.save_pretrained(self.tmpdirname  )


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 10_00, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )

     assert batch.input_ids.shape == (2, 40_96)
     assert batch.attention_mask.shape == (2, 40_96)
     assert targets["input_ids"].shape == (2, 5)
     assert len(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= (
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A									= self._large_tokenizer(_lowerCamelCase  ).input_ids

     self.assertListEqual(
         _lowerCamelCase,       [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1],       )




