


'''simple docstring'''






import unittest

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






class      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):




			@cached_property
			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






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




			@cached_property
			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






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




			def 			_snake_case   (      self       )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							str			   = self.tokenizer_ab
								__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' I am a small frog.'''
								__SCREAMING_SNAKE_CASE					:							int			   = tok([src_text]					,      padding=lowercase					,      truncation=lowercase       )['''input_ids''']
								__SCREAMING_SNAKE_CASE					:							Any			   = tok.batch_decode(lowercase					,      skip_special_tokens=lowercase					,      clean_up_tokenization_spaces=lowercase       )[0]
								assert src_text == decoded




			def 			_snake_case   (      self       )			->						List[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = self.rust_tokenizer_ab
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = ''' I am a small frog.'''
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = tok([src_text]					,      padding=lowercase					,      truncation=lowercase       )['''input_ids''']
								__SCREAMING_SNAKE_CASE					:							str			   = tok.batch_decode(lowercase					,      skip_special_tokens=lowercase					,      clean_up_tokenization_spaces=lowercase       )[0]
								assert src_text == decoded




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([''' Sam''', '''Sam''']       ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']       ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A      =     {
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A      =     [
						    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DecisionTransformerGPT2Model""",
						    """DecisionTransformerGPT2PreTrainedModel""",
						    """DecisionTransformerModel""",
						    """DecisionTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_decision_transformer import (
						    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    DecisionTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_decision_transformer import (
												    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DecisionTransformerGPTaModel,
												    DecisionTransformerGPTaPreTrainedModel,
												    DecisionTransformerModel,
												    DecisionTransformerPreTrainedModel,
												)


else:
						import sys

						_A      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


