import math

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


lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)

lowerCAmelCase			:							int     ={
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'data2vec-audio'



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any]=32    ,  _UpperCamelCase     :				Optional[Any]=768    ,  _UpperCamelCase     :				List[str]=12    ,  _UpperCamelCase     :				Optional[int]=12    ,  _UpperCamelCase     :				List[Any]=3072    ,  _UpperCamelCase     :				Union[str, Any]="gelu"    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				Dict=0.1    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				List[str]=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				int=1E-5    ,  _UpperCamelCase     :				str="gelu"    ,  _UpperCamelCase     :				List[str]=(512, 512, 512, 512, 512, 512, 512)    ,  _UpperCamelCase     :				Any=(5, 2, 2, 2, 2, 2, 2)    ,  _UpperCamelCase     :				Dict=(10, 3, 3, 3, 3, 2, 2)    ,  _UpperCamelCase     :				List[Any]=False    ,  _UpperCamelCase     :				Optional[int]=16    ,  _UpperCamelCase     :				List[str]=19    ,  _UpperCamelCase     :				List[Any]=5    ,  _UpperCamelCase     :				str=0.0_5    ,  _UpperCamelCase     :				Union[str, Any]=10    ,  _UpperCamelCase     :				Dict=2    ,  _UpperCamelCase     :				Union[str, Any]=0.0    ,  _UpperCamelCase     :				str=10    ,  _UpperCamelCase     :				Any=0    ,  _UpperCamelCase     :				Any="sum"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				List[Any]=False    ,  _UpperCamelCase     :				List[Any]=256    ,  _UpperCamelCase     :				List[str]=(512, 512, 512, 512, 1500)    ,  _UpperCamelCase     :				Optional[Any]=(5, 3, 3, 1, 1)    ,  _UpperCamelCase     :				Optional[int]=(1, 2, 3, 1, 1)    ,  _UpperCamelCase     :				List[str]=512    ,  _UpperCamelCase     :				Tuple=0    ,  _UpperCamelCase     :				int=1    ,  _UpperCamelCase     :				Optional[int]=2    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				int=3    ,  _UpperCamelCase     :				str=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase    ,  pad_token_id=_UpperCamelCase    ,  bos_token_id=_UpperCamelCase    ,  eos_token_id=_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						hidden_size
          _lowerCamelCase     :		List[str] 						=						feat_extract_activation
          _lowerCamelCase     :		Union[str, Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		int 						=						conv_bias
          _lowerCamelCase     :		Union[str, Any] 						=						num_conv_pos_embeddings
          _lowerCamelCase     :		List[str] 						=						num_conv_pos_embedding_groups
          _lowerCamelCase     :		int 						=						conv_pos_kernel_size
          _lowerCamelCase     :		Dict 						=						len(self.conv_dim)
          _lowerCamelCase     :		List[str] 						=						num_hidden_layers
          _lowerCamelCase     :		Any 						=						intermediate_size
          _lowerCamelCase     :		Union[str, Any] 						=						hidden_act
          _lowerCamelCase     :		List[Any] 						=						num_attention_heads
          _lowerCamelCase     :		Optional[int] 						=						hidden_dropout
          _lowerCamelCase     :		List[str] 						=						attention_dropout
          _lowerCamelCase     :		Dict 						=						activation_dropout
          _lowerCamelCase     :		Tuple 						=						feat_proj_dropout
          _lowerCamelCase     :		Optional[Any] 						=						final_dropout
          _lowerCamelCase     :		str 						=						layerdrop
          _lowerCamelCase     :		Tuple 						=						layer_norm_eps
          _lowerCamelCase     :		Any 						=						initializer_range
          _lowerCamelCase     :		Union[str, Any] 						=						vocab_size
          _lowerCamelCase     :		Dict 						=						use_weighted_layer_sum

          if (
              (len(self.conv_stride) != self.num_feat_extract_layers)
              or (len(self.conv_kernel) != self.num_feat_extract_layers)
              or (len(self.conv_dim) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                  """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                  F""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                  F""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _lowerCamelCase     :		Optional[Any] 						=						mask_time_prob
          _lowerCamelCase     :		Any 						=						mask_time_length
          _lowerCamelCase     :		str 						=						mask_time_min_masks
          _lowerCamelCase     :		Optional[int] 						=						mask_feature_prob
          _lowerCamelCase     :		str 						=						mask_feature_length
          _lowerCamelCase     :		Dict 						=						mask_feature_min_masks

          # ctc loss
          _lowerCamelCase     :		int 						=						ctc_loss_reduction
          _lowerCamelCase     :		Union[str, Any] 						=						ctc_zero_infinity

          # adapter
          _lowerCamelCase     :		List[str] 						=						add_adapter
          _lowerCamelCase     :		Dict 						=						adapter_kernel_size
          _lowerCamelCase     :		str 						=						adapter_stride
          _lowerCamelCase     :		Union[str, Any] 						=						num_adapter_layers
          _lowerCamelCase     :		Optional[int] 						=						output_hidden_size or hidden_size

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          _lowerCamelCase     :		List[Any] 						=						classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          _lowerCamelCase     :		List[str] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						xvector_output_dim




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          return math.prod(self.conv_stride)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase			:							Any     =logging.get_logger(__name__)

lowerCAmelCase			:							List[Any]     ={
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'swin'

      _snake_case    		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				List[str]=224    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=96    ,  _UpperCamelCase     :				Any=[2, 2, 6, 2]    ,  _UpperCamelCase     :				Any=[3, 6, 12, 24]    ,  _UpperCamelCase     :				Tuple=7    ,  _UpperCamelCase     :				Tuple=4.0    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Dict=1E-5    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				List[Any]=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Tuple 						=						patch_size
          _lowerCamelCase     :		Dict 						=						num_channels
          _lowerCamelCase     :		Union[str, Any] 						=						embed_dim
          _lowerCamelCase     :		str 						=						depths
          _lowerCamelCase     :		str 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						window_size
          _lowerCamelCase     :		int 						=						mlp_ratio
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Tuple 						=						drop_path_rate
          _lowerCamelCase     :		List[str] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						use_absolute_embeddings
          _lowerCamelCase     :		int 						=						layer_norm_eps
          _lowerCamelCase     :		str 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _lowerCamelCase     :		int 						=						int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
          _lowerCamelCase     :		Dict 						=						["""stem"""] + [F"""stage{idx}""" for idx in range(1    ,  len(_UpperCamelCase) + 1)]
          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						get_aligned_output_features_output_indices(
              out_features=_UpperCamelCase    ,  out_indices=_UpperCamelCase    ,  stage_names=self.stage_names)





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       version.parse('1.11'		)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ])




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->float:


          """simple docstring"""


          return 1E-4




