



"""simple docstring"""






import unittest

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

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					@cached_property
					def 						snake_case			(			self				:    int	)->   Optional[int]:
								return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''	)



					@cached_property
					def 						snake_case			(			self				:    int	)->   str:
								return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''	)



					def 						snake_case			(			self				:    List[Any]	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.tokenizer_ab
								lowerCamelCase__						:						Tuple						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						str						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						Any						      =self.rust_tokenizer_ab
								lowerCamelCase__						:						str						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						Optional[Any]						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    List[str]	)->   Union[str, Any]:
								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]




					def 						snake_case			(			self				:    Optional[Any]	)->   str:
								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					str 		=		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					List[Any] 		=		["OpenAIGPTTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Union[str, Any] 		=		[
		    "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OpenAIGPTDoubleHeadsModel",
		    "OpenAIGPTForSequenceClassification",
		    "OpenAIGPTLMHeadModel",
		    "OpenAIGPTModel",
		    "OpenAIGPTPreTrainedModel",
		    "load_tf_weights_in_openai_gpt",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Optional[Any] 		=		[
		    "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFOpenAIGPTDoubleHeadsModel",
		    "TFOpenAIGPTForSequenceClassification",
		    "TFOpenAIGPTLMHeadModel",
		    "TFOpenAIGPTMainLayer",
		    "TFOpenAIGPTModel",
		    "TFOpenAIGPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
		from .tokenization_openai import OpenAIGPTTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_openai_fast import OpenAIGPTTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_openai import (
				    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OpenAIGPTDoubleHeadsModel,
				    OpenAIGPTForSequenceClassification,
				    OpenAIGPTLMHeadModel,
				    OpenAIGPTModel,
				    OpenAIGPTPreTrainedModel,
				    load_tf_weights_in_openai_gpt,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTMainLayer,
				    TFOpenAIGPTModel,
				    TFOpenAIGPTPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					Dict 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




