



"""simple docstring"""







import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCamelCase     =							logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__      )
class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					bool							  =     field(default=_lowerCAmelCase       ,		metadata={"help": "Whether to use SortishSampler or not."}      )
			A__	:					bool							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}      )
			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": (
			            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
			            "to the `max_length` value of the model configuration."
			        )
			    }       ,		)
			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": (
			            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
			            "to the `num_beams` value of the model configuration."
			        )
			    }       ,		)
			A__	:					Optional[Union[str, Path, GenerationConfig]]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
			    }       ,		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					super().to_dict()
				for k, v in d.items():
					if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
						A__											=					v.to_dict()
				return d









"""simple docstring"""







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__)




