


'''simple docstring'''


import functools
import operator

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


UpperCAmelCase_		:   Tuple							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   List[Any]							   =		{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class     lowercase__  (						a__					):





							'''simple docstring'''


							A_							:      List[str]      	=  """sew-d"""






							def __init__(    self    ,					__snake_case=32    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case=2    ,					__snake_case=512    ,					__snake_case=256    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=("p2c", "c2p")    ,					__snake_case="layer_norm"    ,					__snake_case="gelu_python"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=0.0    ,					__snake_case=0.1    ,					__snake_case=0.02    ,					__snake_case=1e-7    ,					__snake_case=1e-5    ,					__snake_case="group"    ,					__snake_case="gelu"    ,					__snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,					__snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,					__snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,					__snake_case=False    ,					__snake_case=128    ,					__snake_case=16    ,					__snake_case=True    ,					__snake_case=0.05    ,					__snake_case=10    ,					__snake_case=2    ,					__snake_case=0.0    ,					__snake_case=10    ,					__snake_case=0    ,					__snake_case="mean"    ,					__snake_case=False    ,					__snake_case=False    ,					__snake_case=256    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					**__snake_case    ,					):
											super().__init__(**lowerCAmelCase__    ,					pad_token_id=lowerCAmelCase__    ,					bos_token_id=lowerCAmelCase__    ,					eos_token_id=lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       int     		=      feat_extract_norm
											_SCREAMING_SNAKE_CASE :       Any     		=      feat_extract_activation
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      list(lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      list(lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       int     		=      list(lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       List[str]     		=      conv_bias
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_conv_pos_embeddings
											_SCREAMING_SNAKE_CASE :       str     		=      num_conv_pos_embedding_groups
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      len(self.conv_dim  )
											_SCREAMING_SNAKE_CASE :       int     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       int     		=      squeeze_factor
											_SCREAMING_SNAKE_CASE :       int     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      position_buckets
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      share_att_key
											_SCREAMING_SNAKE_CASE :       str     		=      relative_attention
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      norm_rel_ebd
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      list(lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       str     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_dropout
											_SCREAMING_SNAKE_CASE :       str     		=      activation_dropout
											_SCREAMING_SNAKE_CASE :       str     		=      feat_proj_dropout
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      final_dropout
											_SCREAMING_SNAKE_CASE :       List[str]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       Dict     		=      feature_layer_norm_eps
											_SCREAMING_SNAKE_CASE :       Any     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       List[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
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      apply_spec_augment
											_SCREAMING_SNAKE_CASE :       Tuple     		=      mask_time_prob
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      mask_time_length
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      mask_time_min_masks
											_SCREAMING_SNAKE_CASE :       str     		=      mask_feature_prob
											_SCREAMING_SNAKE_CASE :       Dict     		=      mask_feature_length
											_SCREAMING_SNAKE_CASE :       Tuple     		=      mask_feature_min_masks

											# ctc loss
											_SCREAMING_SNAKE_CASE :       List[str]     		=      ctc_loss_reduction
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE :       Tuple     		=      classifier_proj_size






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











"""simple docstring"""




import heapq as hq
import math
from collections.abc import Iterator


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		List[str]	=				str(id_      )
								a      :		Optional[Any]	=				None
								a      :		Tuple	=				None
								a      :		str	=				[]
								a      :		Any	=				{}  # {vertex:distance}






							def __lt__(   self      ,					lowerCAmelCase__      )	-> Any:
								return self.key < other.key






							def __repr__(   self      )	-> Optional[Any]:
								return self.id






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								self.neighbors.append(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								a      :		Optional[Any]	=				weight






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	graph[a - 1].add_neighbor(graph[b - 1]					)
	graph[b - 1].add_neighbor(graph[a - 1]					)
	# add the edges:
	graph[a - 1].add_edge(graph[b - 1]    ,       _lowercase					)
	graph[b - 1].add_edge(graph[a - 1]    ,       _lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->list:






	'''simple docstring'''






	a      :		int	=				[]
	for u in graph:
		a      :		List[str]	=				math.inf
		a      :		int	=				None
	a      :		str	=				0
	a      :		Union[str, Any]	=				graph[:]
	while q:
		a      :		List[Any]	=				min(_lowercase					)
		q.remove(_lowercase					)
		for v in u.neighbors:
			if (v in q) and (u.edges[v.id] < v.key):
				a      :		List[Any]	=				u
				a      :		Optional[int]	=				u.edges[v.id]
	for i in range(1    ,       len(_lowercase					)					):
		a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
	return a






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->Iterator[tuple]:






	'''simple docstring'''






	for u in graph:
		a      :		str	=				math.inf
		a      :		Dict	=				None
	a      :		Dict	=				0

	a      :		List[Any]	=				list(_lowercase					)
	hq.heapify(_lowercase					)

	while h:
		a      :		Dict	=				hq.heappop(_lowercase					)
		for v in u.neighbors:
			if (v in h) and (u.edges[v.id] < v.key):
				a      :		Dict	=				u
				a      :		Optional[Any]	=				u.edges[v.id]
				hq.heapify(_lowercase					)

	for i in range(1    ,       len(_lowercase					)					):
		yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)






def       _SCREAMING_SNAKE_CASE		(	) ->None:






	'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()


