


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


SCREAMING_SNAKE_CASE		:int     						=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Any     						=   {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}


class 			__lowerCAmelCase   (       _a    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				"""donut-swin"""

  _SCREAMING_SNAKE_CASE									=				{
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }



  def __init__(   self				:				List[Any]			, _lowerCAmelCase				:				Union[str, Any]=2_2_4			, _lowerCAmelCase				:				Optional[int]=4			, _lowerCAmelCase				:				str=3			, _lowerCAmelCase				:				Dict=9_6			, _lowerCAmelCase				:				Dict=[2, 2, 6, 2]			, _lowerCAmelCase				:				List[Any]=[3, 6, 1_2, 2_4]			, _lowerCAmelCase				:				int=7			, _lowerCAmelCase				:				int=4.0			, _lowerCAmelCase				:				Dict=True			, _lowerCAmelCase				:				Dict=0.0			, _lowerCAmelCase				:				Any=0.0			, _lowerCAmelCase				:				List[Any]=0.1			, _lowerCAmelCase				:				Optional[Any]="gelu"			, _lowerCAmelCase				:				Union[str, Any]=False			, _lowerCAmelCase				:				Dict=0.02			, _lowerCAmelCase				:				Dict=1e-5			, **_lowerCAmelCase				:				Dict			, )      ->       int:




     """simple docstring"""
     super().__init__(**__lowerCamelCase     )

     snake_case_    			=    image_size
     snake_case_    			=    patch_size
     snake_case_    			=    num_channels
     snake_case_    			=    embed_dim
     snake_case_    			=    depths
     snake_case_    			=    len(__lowerCamelCase     )
     snake_case_    			=    num_heads
     snake_case_    			=    window_size
     snake_case_    			=    mlp_ratio
     snake_case_    			=    qkv_bias
     snake_case_    			=    hidden_dropout_prob
     snake_case_    			=    attention_probs_dropout_prob
     snake_case_    			=    drop_path_rate
     snake_case_    			=    hidden_act
     snake_case_    			=    use_absolute_embeddings
     snake_case_    			=    layer_norm_eps
     snake_case_    			=    initializer_range
     # 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
     snake_case_    			=    int(embed_dim * 2 ** (len(__lowerCamelCase     ) - 1)     )












import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate

@dataclass(frozen=_a      )
class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     str     	=  field(default="""automatic-speech-recognition"""		,   metadata={"""include_in_asdict_even_if_is_default""": True}      )
	snake_case__							:     ClassVar[Features]     	=  Features({"""audio""": Audio()}      )
	snake_case__							:     ClassVar[Features]     	=  Features({"""transcription""": Value("""string"""      )}      )
	snake_case__							:     str     	=  "audio"
	snake_case__							:     str     	=  "transcription"





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Dict	):
		if self.audio_column not in features:
			raise ValueError(F"""Column {self.audio_column} is not present in features."""	)
		if not isinstance(features[self.audio_column] ,  __lowerCamelCase	):
			raise ValueError(F"""Column {self.audio_column} is not an Audio type."""	)
		UpperCamelCase     :int          =	copy.deepcopy(self	)
		UpperCamelCase     :Any          =	self.input_schema.copy()
		UpperCamelCase     :List[str]          =	features[self.audio_column]
		UpperCamelCase     :List[Any]          =	input_schema
		return task_template





	@property
	def 						_A (		self				:					Optional[int]	):
		return {self.audio_column: "audio", self.transcription_column: "transcription"}
