



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =      {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''ReformerTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''ReformerTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ReformerAttention''',
							    '''ReformerForMaskedLM''',
							    '''ReformerForQuestionAnswering''',
							    '''ReformerForSequenceClassification''',
							    '''ReformerLayer''',
							    '''ReformerModel''',
							    '''ReformerModelWithLMHead''',
							    '''ReformerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_reformer import ReformerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_reformer_fast import ReformerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_reformer import (
														    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ReformerAttention,
														    ReformerForMaskedLM,
														    ReformerForQuestionAnswering,
														    ReformerForSequenceClassification,
														    ReformerLayer,
														    ReformerModel,
														    ReformerModelWithLMHead,
														    ReformerPreTrainedModel,
														)

else:
							import sys

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









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								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__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								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					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




