





from typing import TYPE_CHECKING

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


lowerCAmelCase							:       Dict					 =	{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Dict					 =	[
							    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MCTCTForCTC""",
							    """MCTCTModel""",
							    """MCTCTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
							from .feature_extraction_mctct import MCTCTFeatureExtractor
							from .processing_mctct import MCTCTProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
							import sys

							lowerCAmelCase							:       List[str]					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = False			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.streaming:
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_streaming_dataset(split='''train'''					)
										# Build regular (map-style) dataset
										else:
																__SCREAMING_SNAKE_CASE:				str           =   None
																__SCREAMING_SNAKE_CASE:				List[Any]           =   None
																__SCREAMING_SNAKE_CASE:				Tuple           =   None
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None

																self.builder.download_and_prepare(
																    download_config=_lowerCAmelCase			, download_mode=_lowerCAmelCase			, verification_mode=_lowerCAmelCase			, base_path=_lowerCAmelCase			, num_proc=self.num_proc			, )
																__SCREAMING_SNAKE_CASE:				List[str]           =   self.builder.as_dataset(
																    split='''train'''			, verification_mode=_lowerCAmelCase			, in_memory=self.keep_in_memory					)
										return dataset



