



"""simple docstring"""






import unittest

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




class    __snake_case(					unittest.TestCase       ):




    @cached_property
    def   A   (   self			):





      '''simple docstring'''



      return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''			)




    @cached_property
    def   A   (   self			):





      '''simple docstring'''



      return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.tokenizer_ab
      _SCREAMING_SNAKE_CASE        =			' I am a small frog.'
      _SCREAMING_SNAKE_CASE        =			tok([src_text]    ,							padding=_UpperCAmelCase    ,							truncation=_UpperCAmelCase			)['input_ids']
      _SCREAMING_SNAKE_CASE        =			tok.batch_decode(_UpperCAmelCase    ,							skip_special_tokens=_UpperCAmelCase    ,							clean_up_tokenization_spaces=_UpperCAmelCase			)[0]
      assert src_text == decoded




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.rust_tokenizer_ab
      _SCREAMING_SNAKE_CASE        =			' I am a small frog.'
      _SCREAMING_SNAKE_CASE        =			tok([src_text]    ,							padding=_UpperCAmelCase    ,							truncation=_UpperCAmelCase			)['input_ids']
      _SCREAMING_SNAKE_CASE        =			tok.batch_decode(_UpperCAmelCase    ,							skip_special_tokens=_UpperCAmelCase    ,							clean_up_tokenization_spaces=_UpperCAmelCase			)[0]
      assert src_text == decoded




    def   A   (   self			):





      '''simple docstring'''



      assert self.tokenizer_ab.add_prefix_space
      assert self.tokenizer_ab([''' Sam''', '''Sam''']			).input_ids == [[5_502, 2], [5_502, 2]]




    def   A   (   self			):





      '''simple docstring'''



      assert self.rust_tokenizer_ab.add_prefix_space
      assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']			).input_ids == [[5_502, 2], [5_502, 2]]




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__  				=			{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'QDQBertForMaskedLM',
			    'QDQBertForMultipleChoice',
			    'QDQBertForNextSentencePrediction',
			    'QDQBertForQuestionAnswering',
			    'QDQBertForSequenceClassification',
			    'QDQBertForTokenClassification',
			    'QDQBertLayer',
			    'QDQBertLMHeadModel',
			    'QDQBertModel',
			    'QDQBertPreTrainedModel',
			    'load_tf_weights_in_qdqbert',
			]


if TYPE_CHECKING:
			from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_qdqbert import (
						    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    QDQBertForMaskedLM,
						    QDQBertForMultipleChoice,
						    QDQBertForNextSentencePrediction,
						    QDQBertForQuestionAnswering,
						    QDQBertForSequenceClassification,
						    QDQBertForTokenClassification,
						    QDQBertLayer,
						    QDQBertLMHeadModel,
						    QDQBertModel,
						    QDQBertPreTrainedModel,
						    load_tf_weights_in_qdqbert,
						)


else:
			import sys

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


