

'''simple docstring'''



from typing import TYPE_CHECKING

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


__snake_case			:     Optional[int]										=      {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Dict										=      ['PLBartTokenizer']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Union[str, Any]										=      [
		    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PLBartForCausalLM',
		    'PLBartForConditionalGeneration',
		    'PLBartForSequenceClassification',
		    'PLBartModel',
		    'PLBartPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

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




'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						Any=None		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									if tokenize_kwargs is None:
															A__					:       List[Any]     ={}

									if truncation is not None:
															if "truncation" in tokenize_kwargs:
																					raise ValueError(
																					    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
															A__					:       str     =truncation

									A__					:       Optional[int]     =tokenize_kwargs

									A__					:       List[Any]     ={}
									if return_tensors is not None:
															A__					:       Any     =return_tensors

									return preprocess_params, {}, postprocess_params


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Union[str, Any]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict[str, GenericTensor]:





									'''simple docstring'''
									A__					:       List[str]     =self.framework
									A__					:       Union[str, Any]     =self.tokenizer(lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_			)
									return model_inputs


			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.model(**lowerCAmelCase_			)
									return model_outputs


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Any=False			)      ->		List[Any]:





									'''simple docstring'''
									# [0] is the first available tensor, logits or last_hidden_state.
									if return_tensors:
															return model_outputs[0]
									if self.framework == "pt":
															return model_outputs[0].tolist()
									elif self.framework == "tf":
															return model_outputs[0].numpy().tolist()



			def __call__(				self	:						int		,      *lowerCAmelCase_	:						Any		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									return super().__call__(*lowerCAmelCase_		,      **lowerCAmelCase_			)

