from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     a_			(						_A						):






    '''simple docstring'''






    def __init__(      self :  Dict      ,   lowercase__ :  Callable      ,   lowercase__ :  Optional[Features] = None      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[dict] = None      ,   lowercase__ :  Optional[int] = None      ,   **lowercase__ :  str      ,   ):







           '''simple docstring'''



           super().__init__(
               features=__SCREAMING_SNAKE_CASE      ,   cache_dir=__SCREAMING_SNAKE_CASE      ,   keep_in_memory=__SCREAMING_SNAKE_CASE      ,   streaming=__SCREAMING_SNAKE_CASE      ,   num_proc=__SCREAMING_SNAKE_CASE      ,   **__SCREAMING_SNAKE_CASE      ,   )
           lowerCAmelCase__					     = Generator(
               cache_dir=__SCREAMING_SNAKE_CASE      ,   features=__SCREAMING_SNAKE_CASE      ,   generator=__SCREAMING_SNAKE_CASE      ,   gen_kwargs=__SCREAMING_SNAKE_CASE      ,   **__SCREAMING_SNAKE_CASE      ,   )




    def 		__snake_case						(      self :  List[str]):







           '''simple docstring'''



           if self.streaming:
                  lowerCAmelCase__					     = self.builder.as_streaming_dataset(split='train')
           # Build regular (map-style) dataset
           else:
                  lowerCAmelCase__					     = None
                  lowerCAmelCase__					     = None
                  lowerCAmelCase__					     = None
                  lowerCAmelCase__					     = None

                  self.builder.download_and_prepare(
                      download_config=__SCREAMING_SNAKE_CASE      ,   download_mode=__SCREAMING_SNAKE_CASE      ,   verification_mode=__SCREAMING_SNAKE_CASE      ,   base_path=__SCREAMING_SNAKE_CASE      ,   num_proc=self.num_proc      ,   )
                  lowerCAmelCase__					     = self.builder.as_dataset(
                      split='train'      ,   verification_mode=__SCREAMING_SNAKE_CASE      ,   in_memory=self.keep_in_memory)
           return dataset

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

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