






"""simple docstring"""
import functools
import operator

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Any    =				"""sew"""





	def __init__(self     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=12     ,     lowerCAmelCase_=3072     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1e-5     ,     lowerCAmelCase_="group"     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,     lowerCAmelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,     lowerCAmelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=128     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=0.05     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=10     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_="mean"     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=2     ,     **lowerCAmelCase_     ,     ):
		super().__init__(**lowerCAmelCase_     ,     pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     eos_token_id=lowerCAmelCase_							)
		A_		:						int	    =   hidden_size
		A_		:						List[str]	    =   feat_extract_norm
		A_		:						Union[str, Any]	    =   feat_extract_activation
		A_		:						int	    =   list(lowerCAmelCase_							)
		A_		:						Tuple	    =   list(lowerCAmelCase_							)
		A_		:						int	    =   list(lowerCAmelCase_							)
		A_		:						Tuple	    =   conv_bias
		A_		:						Union[str, Any]	    =   num_conv_pos_embeddings
		A_		:						Optional[int]	    =   num_conv_pos_embedding_groups
		A_		:						Any	    =   len(self.conv_dim							)
		A_		:						Optional[Any]	    =   num_hidden_layers
		A_		:						int	    =   intermediate_size
		A_		:						Dict	    =   squeeze_factor
		A_		:						Tuple	    =   hidden_act
		A_		:						Any	    =   num_attention_heads
		A_		:						List[Any]	    =   hidden_dropout
		A_		:						Union[str, Any]	    =   attention_dropout
		A_		:						List[Any]	    =   activation_dropout
		A_		:						Optional[Any]	    =   feat_proj_dropout
		A_		:						Any	    =   final_dropout
		A_		:						Tuple	    =   layerdrop
		A_		:						Optional[Any]	    =   layer_norm_eps
		A_		:						Dict	    =   initializer_range
		A_		:						str	    =   vocab_size

		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)`,"""
			    f"""but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)"""
			    f"""= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

		# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
		A_		:						str	    =   apply_spec_augment
		A_		:						List[str]	    =   mask_time_prob
		A_		:						Union[str, Any]	    =   mask_time_length
		A_		:						Any	    =   mask_time_min_masks
		A_		:						List[Any]	    =   mask_feature_prob
		A_		:						List[Any]	    =   mask_feature_length
		A_		:						Union[str, Any]	    =   mask_feature_min_masks

		# ctc loss
		A_		:						Any	    =   ctc_loss_reduction
		A_		:						Optional[Any]	    =   ctc_zero_infinity

		# sequence classification
		A_		:						Any	    =   use_weighted_layer_sum
		A_		:						List[str]	    =   classifier_proj_size





	@property
	def    lowerCamelCase(self							):
		return functools.reduce(operator.mul     ,     self.conv_stride     ,     1							)









"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

