
"""simple docstring"""






import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__SCREAMING_SNAKE_CASE							=     logging.getLogger(__name__)





@dataclass
class 				a__					(						A__       ):
     UpperCAmelCase__         =      field(
         default=0.0   ,							metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}       )
     UpperCAmelCase__         =      field(default=A__   ,							metadata={'''help''': '''Whether to SortishSamler or not.'''}       )
     UpperCAmelCase__         =      field(
         default=A__   ,							metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}       )
     UpperCAmelCase__         =      field(default=A__   ,							metadata={'''help''': '''whether to use adafactor'''}       )
     UpperCAmelCase__         =      field(
         default=A__   ,							metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}       )
     UpperCAmelCase__         =      field(
         default=A__   ,							metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}       )
     UpperCAmelCase__         =      field(default=A__   ,							metadata={'''help''': '''Dropout probability. Goes into model.config.'''}       )
     UpperCAmelCase__         =      field(
         default=A__   ,							metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}       )
     UpperCAmelCase__         =      field(
         default='''linear'''   ,							metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()       )}'''}   ,							)


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     ['WhisperTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'WhisperForConditionalGeneration',
     'WhisperModel',
     'WhisperPreTrainedModel',
     'WhisperForAudioClassification',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFWhisperForConditionalGeneration',
     'TFWhisperModel',
     'TFWhisperPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'FlaxWhisperForConditionalGeneration',
     'FlaxWhisperModel',
     'FlaxWhisperPreTrainedModel',
     'FlaxWhisperForAudioClassification',
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
