import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property







class 			__snake_case   (     unittest.TestCase   ):
 @cached_property
 def 	UpperCAmelCase__       (  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''						)
 @cached_property
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''						)
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =self.tokenizer_ab
       UpperCAmelCase    : Optional[Any]      =''' I am a small frog.'''
       UpperCAmelCase    : List[str]      =tok([src_text]		,   padding=snake_case__		,   truncation=snake_case__						)['''input_ids''']
       UpperCAmelCase    : List[Any]      =tok.batch_decode(snake_case__		,   skip_special_tokens=snake_case__		,   clean_up_tokenization_spaces=snake_case__						)[0]
       assert src_text == decoded
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : str      =self.rust_tokenizer_ab
       UpperCAmelCase    : Optional[Any]      =''' I am a small frog.'''
       UpperCAmelCase    : Tuple      =tok([src_text]		,   padding=snake_case__		,   truncation=snake_case__						)['''input_ids''']
       UpperCAmelCase    : List[Any]      =tok.batch_decode(snake_case__		,   skip_special_tokens=snake_case__		,   clean_up_tokenization_spaces=snake_case__						)[0]
       assert src_text == decoded
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       assert self.tokenizer_ab.add_prefix_space
       assert self.tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[5502, 2], [5502, 2]]






 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       assert self.rust_tokenizer_ab.add_prefix_space
       assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[5502, 2], [5502, 2]]

from typing import TYPE_CHECKING

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


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
