





'''simple docstring'''







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_  :							Any            =	logging.get_logger(__name__)

snake_case_  :							List[Any]            =	{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      int )			->						List[Any]:
						for attribute in key.split('''.''' ):
												UpperCAmelCase_      :   int      =	getattr(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						if weight_type is not None:
												UpperCAmelCase_      :   Dict      =	getattr(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ).shape
						else:
												UpperCAmelCase_      :   Union[str, Any]      =	hf_pointer.shape

						assert hf_shape == value.shape, (
						    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												UpperCAmelCase_      :   Tuple      =	value
						elif weight_type == "weight_g":
												UpperCAmelCase_      :   Union[str, Any]      =	value
						elif weight_type == "weight_v":
												UpperCAmelCase_      :   Any      =	value
						elif weight_type == "bias":
												UpperCAmelCase_      :   int      =	value
						else:
												UpperCAmelCase_      :   Dict      =	value

						logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Any      =	[]
						UpperCAmelCase_      :   List[Any]      =	fairseq_model.state_dict()

						UpperCAmelCase_      :   Tuple      =	hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase_      :   str      =	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      hf_model.config.feat_extract_norm == '''group''',      )
																		UpperCAmelCase_      :   List[Any]      =	True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase_      :   Optional[Any]      =	mapped_key

																								if key in name:
																														UpperCAmelCase_      :   int      =	True
																														if "*" in mapped_key:
																																				UpperCAmelCase_      :   str      =	name.split(SCREAMING_SNAKE_CASE__ )[0].split('''.''' )[-2]
																																				UpperCAmelCase_      :   Union[str, Any]      =	mapped_key.replace('''*''',      SCREAMING_SNAKE_CASE__ )
																														if "weight_g" in name:
																																				UpperCAmelCase_      :   Union[str, Any]      =	'''weight_g'''
																														elif "weight_v" in name:
																																				UpperCAmelCase_      :   Union[str, Any]      =	'''weight_v'''
																														elif "weight" in name:
																																				UpperCAmelCase_      :   str      =	'''weight'''
																														elif "bias" in name:
																																				UpperCAmelCase_      :   Optional[int]      =	'''bias'''
																														else:
																																				UpperCAmelCase_      :   List[str]      =	None
																														set_recursively(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																								continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__ )

						logger.warning(F"""Unused weights: {unused_weights}""" )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Any:
						UpperCAmelCase_      :   str      =	full_name.split('''conv_layers.''' )[-1]
						UpperCAmelCase_      :   Optional[Any]      =	name.split('''.''' )
						UpperCAmelCase_      :   Any      =	int(items[0] )
						UpperCAmelCase_      :   List[str]      =	int(items[1] )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		UpperCAmelCase_      :   Optional[int]      =	value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		UpperCAmelCase_      :   int      =	value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		UpperCAmelCase_      :   Optional[int]      =	value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		UpperCAmelCase_      :   Optional[int]      =	value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Optional[int]      =	HubertConfig()
						UpperCAmelCase_      :   Dict      =	model.config

						UpperCAmelCase_      :   str      =	fs_config.activation_dropout
						UpperCAmelCase_      :   List[Any]      =	False
						UpperCAmelCase_      :   str      =	fs_config.attention_dropout
						UpperCAmelCase_      :   Optional[int]      =	False
						UpperCAmelCase_      :   str      =	eval(fs_config.extractor_conv_feature_layers )
						UpperCAmelCase_      :   Dict      =	[x[0] for x in conv_layers]
						UpperCAmelCase_      :   List[Any]      =	[x[1] for x in conv_layers]
						UpperCAmelCase_      :   Any      =	[x[2] for x in conv_layers]
						UpperCAmelCase_      :   Union[str, Any]      =	'''gelu'''
						UpperCAmelCase_      :   Optional[Any]      =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						UpperCAmelCase_      :   str      =	False
						UpperCAmelCase_      :   Tuple      =	0.0
						UpperCAmelCase_      :   Optional[int]      =	0.0
						UpperCAmelCase_      :   Union[str, Any]      =	fs_config.activation_fn
						UpperCAmelCase_      :   Optional[Any]      =	fs_config.dropout
						UpperCAmelCase_      :   Any      =	fs_config.encoder_embed_dim
						UpperCAmelCase_      :   int      =	0.02
						UpperCAmelCase_      :   List[str]      =	fs_config.encoder_ffn_embed_dim
						UpperCAmelCase_      :   List[str]      =	1E-5
						UpperCAmelCase_      :   Union[str, Any]      =	0.0
						UpperCAmelCase_      :   List[str]      =	fs_config.encoder_attention_heads
						UpperCAmelCase_      :   str      =	fs_config.conv_pos_groups
						UpperCAmelCase_      :   str      =	fs_config.conv_pos
						UpperCAmelCase_      :   Tuple      =	len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   int      =	fs_config.encoder_layers

						return config





@torch.no_grad()
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      List[str]=None )			->						Union[str, Any]:
						UpperCAmelCase_      :   Optional[Any]      =	distilhubert().model.model

						if config_path is not None:
												UpperCAmelCase_      :   str      =	HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
						else:
												UpperCAmelCase_      :   Any      =	convert_config(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	model.eval()

						UpperCAmelCase_      :   str      =	WavaVecaFeatureExtractor(
						    feature_size=1,      sampling_rate=16000,      padding_value=0,      do_normalize=SCREAMING_SNAKE_CASE__,      return_attention_mask=SCREAMING_SNAKE_CASE__,      )
						UpperCAmelCase_      :   str      =	HubertModel(SCREAMING_SNAKE_CASE__ )

						recursively_load_weights(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							Any            =	argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					snake_case_  :							List[str]            =	parser.parse_args()
					convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''
import heapq







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							Any:





  """simple docstring"""

  __A		:  Dict       				=		[(0, start)]  # cost from start node,end node
  __A		:  Union[str, Any]       				=		set()
  while heap:
    ((__A)				,       (__A))		:  int       				=		heapq.heappop(__SCREAMING_SNAKE_CASE			)
    if u in visited:
      continue
    visited.add(__SCREAMING_SNAKE_CASE			)
    if u == end:
      return cost
    for v, c in graph[u]:
      if v in visited:
        continue
      __A		:  List[str]       				=		cost + c
      heapq.heappush(__SCREAMING_SNAKE_CASE      ,  (next_item, v)			)
  return -1


A__ :		Any					={
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
A__ :		List[str]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
A__ :		List[Any]					={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

A__ :		List[str]					=dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A__ :		Optional[Any]					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

A__ :		Any					=dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
     import doctest

     doctest.testmod()



