





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_           =	{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "WavLMForAudioFrameClassification",
       "WavLMForCTC",
       "WavLMForSequenceClassification",
       "WavLMForXVector",
       "WavLMModel",
       "WavLMPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavlm import (
          WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavLMForAudioFrameClassification,
          WavLMForCTC,
          WavLMForSequenceClassification,
          WavLMForXVector,
          WavLMModel,
          WavLMPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_           =	_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



