


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__							:						List[Any]							     =   {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """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

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





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

