



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
UpperCAmelCase__       =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Union[str, Any]   )    ->			List[Any]:







			'''simple docstring'''
			for attribute in key.split('.'   ):
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   )

			if weight_type is not None:
						_UpperCAmelCase			=       getattr(_UpperCAmelCase							,       _UpperCAmelCase   ).shape
			else:
						_UpperCAmelCase			=       hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    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			=       value
			elif weight_type == "weight_g":
						_UpperCAmelCase			=       value
			elif weight_type == "weight_v":
						_UpperCAmelCase			=       value
			elif weight_type == "bias":
						_UpperCAmelCase			=       value
			else:
						_UpperCAmelCase			=       value

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





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       fairseq_model.state_dict()

			_UpperCAmelCase			=       hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCAmelCase			=       False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       hf_model.config.feat_extract_norm == 'group'							,       )
									_UpperCAmelCase			=       True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCAmelCase			=       'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
															if "layer_norm_for_extract" in name and (".".join(name.split('.'   )[:-1]   ) != key):
																		# special case since naming is very similar
																		continue
															_UpperCAmelCase			=       True
															if "*" in mapped_key:
																		_UpperCAmelCase			=       name.split(_UpperCAmelCase   )[0].split('.'   )[-2]
																		_UpperCAmelCase			=       mapped_key.replace('*'							,       _UpperCAmelCase   )
															if "weight_g" in name:
																		_UpperCAmelCase			=       'weight_g'
															elif "weight_v" in name:
																		_UpperCAmelCase			=       'weight_v'
															elif "bias" in name:
																		_UpperCAmelCase			=       'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		_UpperCAmelCase			=       'weight'
															else:
																		_UpperCAmelCase			=       None
															set_recursively(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
												continue
						if not is_used:
									unused_weights.append(_UpperCAmelCase   )

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





def 							A     (					_UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Tuple   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       full_name.split('conv_layers.'   )[-1]
			_UpperCAmelCase			=       name.split('.'   )
			_UpperCAmelCase			=       int(items[0]   )
			_UpperCAmelCase			=       int(items[1]   )

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."   )
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."   )
									_UpperCAmelCase			=       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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."   )
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    F"{full_name} has size {value.shape}, but"
												    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."   )
									_UpperCAmelCase			=       value
									logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
			else:
						unused_weights.append(_UpperCAmelCase   )





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[str]=None							,       _UpperCAmelCase    :       int=None							,       _UpperCAmelCase    :       Dict=True   )    ->			Union[str, Any]:







			'''simple docstring'''
			if config_path is not None:
						_UpperCAmelCase			=       UniSpeechSatConfig.from_pretrained(_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       UniSpeechSatConfig()

			_UpperCAmelCase			=       ''

			if is_finetuned:
						_UpperCAmelCase			=       UniSpeechSatForCTC(_UpperCAmelCase   )
			else:
						_UpperCAmelCase			=       UniSpeechSatForPreTraining(_UpperCAmelCase   )

			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]							,       arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
			_UpperCAmelCase			=       model[0].eval()

			recursively_load_weights(_UpperCAmelCase							,       _UpperCAmelCase   )

			hf_wavavec.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							UpperCAmelCase__       =	parser.parse_args()
							convert_unispeech_sat_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
