



from typing import TYPE_CHECKING

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


__snake_case						:Optional[Any]      		=     {
    '''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:
 __snake_case						:str      		=     [
     '''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

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










from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     NestedDataStructureLike[PathLike]     ,		__SCREAMING_SNAKE_CASE							:     Optional[NamedSplit] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Features] = None     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		split=__SCREAMING_SNAKE_CASE     ,		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     ,		)
           __a						       =				path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a						       =				Text(
               cache_dir=__SCREAMING_SNAKE_CASE     ,		data_files=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           if self.streaming:
               __a						       =				self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a						       =				None
               __a						       =				None
               __a						       =				None
               __a						       =				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     ,		)
               __a						       =				self.builder.as_dataset(
                   split=self.split     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		in_memory=self.keep_in_memory)
           return dataset





