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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							Union[str, 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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'mctct'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=8065     ,	SCREAMING_SNAKE_CASE_=1536     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=6144     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=384     ,	SCREAMING_SNAKE_CASE_=920     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=(7,)     ,	SCREAMING_SNAKE_CASE_=(3,)     ,	SCREAMING_SNAKE_CASE_=80     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="sum"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_			)
													__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(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)

													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}`."			)






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 ..auto import CONFIG_MAPPING


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							int									=			{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'table-transformer'
							_snake_case     		=  ['past_key_values']
							_snake_case     		=  {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													if backbone_config is not None and use_timm_backbone:
																			raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''			)

													if not use_timm_backbone:
																			if backbone_config is None:
																									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
																			elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			backbone_config.get('''model_type'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING[backbone_model_type]
																									__UpperCamelCase       =			config_class.from_dict(SCREAMING_SNAKE_CASE_			)
																			# set timm attributes to None
																			__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			None, None, None

													__UpperCamelCase       =			use_timm_backbone
													__UpperCamelCase       =			backbone_config
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			d_model
													__UpperCamelCase       =			encoder_ffn_dim
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			encoder_attention_heads
													__UpperCamelCase       =			decoder_ffn_dim
													__UpperCamelCase       =			decoder_layers
													__UpperCamelCase       =			decoder_attention_heads
													__UpperCamelCase       =			dropout
													__UpperCamelCase       =			attention_dropout
													__UpperCamelCase       =			activation_dropout
													__UpperCamelCase       =			activation_function
													__UpperCamelCase       =			init_std
													__UpperCamelCase       =			init_xavier_std
													__UpperCamelCase       =			encoder_layerdrop
													__UpperCamelCase       =			decoder_layerdrop
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			auxiliary_loss
													__UpperCamelCase       =			position_embedding_type
													__UpperCamelCase       =			backbone
													__UpperCamelCase       =			use_pretrained_backbone
													__UpperCamelCase       =			dilation
													# Hungarian matcher
													__UpperCamelCase       =			class_cost
													__UpperCamelCase       =			bbox_cost
													__UpperCamelCase       =			giou_cost
													# Loss coefficients
													__UpperCamelCase       =			mask_loss_coefficient
													__UpperCamelCase       =			dice_loss_coefficient
													__UpperCamelCase       =			bbox_loss_coefficient
													__UpperCamelCase       =			giou_loss_coefficient
													__UpperCamelCase       =			eos_coefficient
													super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



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





													'''simple docstring'''






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



							@property
							def     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




