

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


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Any						=     {
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class 						__magic_name__					(							lowercase_				):
     UpperCamelCase_	:Tuple										=      """mctct"""






     def __init__(				self		,						_lowercase=8_065		,						_lowercase=1_536		,						_lowercase=36		,						_lowercase=6_144		,						_lowercase=4		,						_lowercase=384		,						_lowercase=920		,						_lowercase=1e-5		,						_lowercase=0.3		,						_lowercase="relu"		,						_lowercase=0.02		,						_lowercase=0.3		,						_lowercase=0.3		,						_lowercase=1		,						_lowercase=0		,						_lowercase=2		,						_lowercase=1		,						_lowercase=0.3		,						_lowercase=1		,						_lowercase=(7,)		,						_lowercase=(3,)		,						_lowercase=80		,						_lowercase=1		,						_lowercase=None		,						_lowercase="sum"		,						_lowercase=False		,						**_lowercase		,						)->     Any:
         super().__init__(**lowerCamelCase_		,						pad_token_id=lowerCamelCase_		,						bos_token_id=lowerCamelCase_		,						eos_token_id=lowerCamelCase_	)
         UpperCamelCase_			  =						vocab_size
         UpperCamelCase_			  =						hidden_size
         UpperCamelCase_			  =						num_hidden_layers
         UpperCamelCase_			  =						intermediate_size
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						attention_head_dim
         UpperCamelCase_			  =						max_position_embeddings
         UpperCamelCase_			  =						layer_norm_eps
         UpperCamelCase_			  =						layerdrop
         UpperCamelCase_			  =						hidden_act
         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						hidden_dropout_prob
         UpperCamelCase_			  =						attention_probs_dropout_prob
         UpperCamelCase_			  =						pad_token_id
         UpperCamelCase_			  =						bos_token_id
         UpperCamelCase_			  =						eos_token_id
         UpperCamelCase_			  =						conv_glu_dim
         UpperCamelCase_			  =						conv_dropout
         UpperCamelCase_			  =						num_conv_layers
         UpperCamelCase_			  =						input_feat_per_channel
         UpperCamelCase_			  =						input_channels
         UpperCamelCase_			  =						conv_channels
         UpperCamelCase_			  =						ctc_loss_reduction
         UpperCamelCase_			  =						ctc_zero_infinity

         # prevents config testing fail with exporting to json
         UpperCamelCase_			  =						list(lowerCamelCase_	)
         UpperCamelCase_			  =						list(lowerCamelCase_	)

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







'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """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      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

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


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			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
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



