



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


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


@require_sentencepiece
@require_tokenizers
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				PegasusTokenizer
       _lowerCamelCase			    =				PegasusTokenizerFast
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_  :	Optional[int]				      =		PegasusTokenizer(lowercase)
             tokenizer.save_pretrained(self.tmpdirname)



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return PegasusTokenizer.from_pretrained("google/pegasus-large")



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return PegasusTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return ("This is a test", "This is a test")



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		"</s>"
             UpperCAmelCase_  :	List[str]				      =		1

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		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(lowercase)     ,1103)



       def  A_   (     self):

             """simple docstring"""





             self.assertEqual(self.get_tokenizer().vocab_size     ,1103)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
             UpperCAmelCase_  :	int				      =		self.tokenizer_class.from_pretrained(self.tmpdirname)
             UpperCAmelCase_  :	Union[str, Any]				      =		(
                 "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>"
             )
             UpperCAmelCase_  :	str				      =		rust_tokenizer([raw_input_str]     ,return_tensors=lowercase     ,add_special_tokens=lowercase).input_ids[0]
             UpperCAmelCase_  :	Optional[int]				      =		py_tokenizer([raw_input_str]     ,return_tensors=lowercase     ,add_special_tokens=lowercase).input_ids[0]
             self.assertListEqual(lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self._large_tokenizer
             # <mask_1> masks whole sentence while <mask_2> masks single word
             UpperCAmelCase_  :	Any				      =		"<mask_1> To ensure a <mask_2> flow of bank resolutions."
             UpperCAmelCase_  :	Optional[int]				      =		[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
             UpperCAmelCase_  :	int				      =		tokenizer([raw_input_str]     ,return_tensors=lowercase).input_ids[0]
             self.assertListEqual(lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		self._large_tokenizer
             # The tracebacks for the following asserts are **better** without messages or self.assertEqual
             assert tokenizer.vocab_size == 96103
             assert tokenizer.pad_token_id == 0
             assert tokenizer.eos_token_id == 1
             assert tokenizer.offset == 103
             assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
             assert tokenizer.unk_token == "<unk>"
             assert tokenizer.model_max_length == 1024
             UpperCAmelCase_  :	int				      =		"To ensure a smooth flow of bank resolutions."
             UpperCAmelCase_  :	List[Any]				      =		[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer([raw_input_str]     ,return_tensors=lowercase).input_ids[0]
             self.assertListEqual(lowercase     ,lowercase)
             assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		["This is going to be way too long." * 150, "short example"]
             UpperCAmelCase_  :	Union[str, Any]				      =		["not super long but more than 5 tokens", "tiny"]
             UpperCAmelCase_  :	Dict				      =		self._large_tokenizer(lowercase     ,padding=lowercase     ,truncation=lowercase     ,return_tensors="pt")
             UpperCAmelCase_  :	Any				      =		self._large_tokenizer(
                 text_target=lowercase     ,max_length=5     ,padding=lowercase     ,truncation=lowercase     ,return_tensors="pt")

             assert batch.input_ids.shape == (2, 1024)
             assert batch.attention_mask.shape == (2, 1024)
             assert targets["input_ids"].shape == (2, 5)
             assert len(lowercase) == 2  # input_ids, attention_mask.



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		{"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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=lowercase     ,model_name="google/bigbird-pegasus-large-arxiv"     ,revision="ba85d0851d708441f91440d509690f1ab6353415"     ,)


@require_sentencepiece
@require_tokenizers
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				PegasusTokenizer
       _lowerCamelCase			    =				PegasusTokenizerFast
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_  :	Dict				      =		PegasusTokenizer(lowercase     ,offset=0     ,mask_token_sent=lowercase     ,mask_token="[MASK]")
             tokenizer.save_pretrained(self.tmpdirname)



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return PegasusTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return ("This is a test", "This is a test")



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
             UpperCAmelCase_  :	Any				      =		self.tokenizer_class.from_pretrained(self.tmpdirname)
             UpperCAmelCase_  :	Optional[Any]				      =		(
                 "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
                 " <pad> <pad> <pad>"
             )
             UpperCAmelCase_  :	Dict				      =		rust_tokenizer([raw_input_str]     ,return_tensors=lowercase     ,add_special_tokens=lowercase).input_ids[0]
             UpperCAmelCase_  :	int				      =		py_tokenizer([raw_input_str]     ,return_tensors=lowercase     ,add_special_tokens=lowercase).input_ids[0]
             self.assertListEqual(lowercase     ,lowercase)



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		["This is going to be way too long." * 1000, "short example"]
             UpperCAmelCase_  :	Any				      =		["not super long but more than 5 tokens", "tiny"]
             UpperCAmelCase_  :	List[Any]				      =		self._large_tokenizer(lowercase     ,padding=lowercase     ,truncation=lowercase     ,return_tensors="pt")
             UpperCAmelCase_  :	int				      =		self._large_tokenizer(
                 text_target=lowercase     ,max_length=5     ,padding=lowercase     ,truncation=lowercase     ,return_tensors="pt")

             assert batch.input_ids.shape == (2, 4096)
             assert batch.attention_mask.shape == (2, 4096)
             assert targets["input_ids"].shape == (2, 5)
             assert len(lowercase) == 2  # input_ids, attention_mask.



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		(
                 "This is an example string that is used to test the original TF implementation against the HF"
                 " implementation"
             )

             UpperCAmelCase_  :	List[Any]				      =		self._large_tokenizer(lowercase).input_ids

             self.assertListEqual(
                 lowercase     ,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]     ,)





from typing import TYPE_CHECKING

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


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

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