"""simple docstring"""




from typing import TYPE_CHECKING

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


_a      :		Union[str, Any]           =   {
    '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:
     _a      :		Dict           =   ['WhisperTokenizerFast']

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a      :		Dict           =   [
         '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

     _a      :		Dict           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




