



'''simple docstring'''





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
_lowercase:				int       =	logging.get_logger(__name__)

_lowercase:				Union[str, Any]       =	{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_lowercase:				Dict       =	[
    'lm_head',
]






def 					a(   A :					Tuple				,  A :					Tuple				,  A :					Dict				,  A :					Tuple				,  A :					Optional[Any] )	-> Optional[int]:
					"""simple docstring"""





					for attribute in key.split("." ):
										a        =     getattr(UpperCamelCase__				,  UpperCamelCase__ )

					if weight_type is not None:
										a        =     getattr(UpperCamelCase__				,  UpperCamelCase__ ).shape
					else:
										a        =     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":
										a        =     value
					elif weight_type == "weight_g":
										a        =     value
					elif weight_type == "weight_v":
										a        =     value
					elif weight_type == "bias":
										a        =     value
					else:
										a        =     value

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






def 					a(   A :					Tuple				,  A :					Dict				,  A :					Dict )	-> Optional[Any]:
					"""simple docstring"""





					a        =     []
					a        =     fairseq_model.state_dict()

					if not is_headless:
										a        =     hf_model.dataavec_audio.feature_extractor
										a        =     hf_model.dataavec_audio.encoder.pos_conv_embed

					else:
										a        =     hf_model.feature_extractor
										a        =     hf_model.encoder.pos_conv_embed

					for name, value in fairseq_dict.items():
										a        =     False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  )
															a        =     True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  )
															a        =     True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									a        =     '''data2vec_audio.''' + 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]:
																									a        =     True
																									if "*" in mapped_key:
																														a        =     name.split(UpperCamelCase__ )[0].split("." )[-2]
																														a        =     mapped_key.replace("*"				,  UpperCamelCase__ )
																									if "weight_g" in name:
																														a        =     '''weight_g'''
																									elif "weight_v" in name:
																														a        =     '''weight_v'''
																									elif "bias" in name:
																														a        =     '''bias'''
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														a        =     '''weight'''
																									else:
																														a        =     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(   A :					List[str]				,  A :					List[Any] )	-> int:
					"""simple docstring"""





					a        =     path.split("." )
					return reduce(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__ )






def 					a(   A :					Tuple				,  A :					List[str]				,  A :					Tuple				,  A :					List[str] )	-> Dict:
					"""simple docstring"""





					a        =     access_by_string(UpperCamelCase__				,  UpperCamelCase__ )
					a        =     hf_weight.data

					if fsq_value.shape != hf_value.shape:
										raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.''' )
					a        =     fsq_value
					logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.''' )






def 					a(   A :					Union[str, Any]				,  A :					Optional[Any]				,  A :					Optional[Any]				,  A :					int )	-> List[Any]:
					"""simple docstring"""





					a        =     full_name.split("conv_layers." )[-1]
					a        =     name.split("." )
					a        =     int(items[0] )
					a        =     int(items[1] )

					a        =     name.split("." )[-1]
					if type_id == 0:
										a        =     '''conv'''
					elif type_id == 2:
										a        =     '''layer_norm'''
					else:
										unused_weights.append(UpperCamelCase__ )
										return

					set_weights(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  f'''conv_layers.{layer_id}.{layer_type}.{weight_type}''' )






def 					a(   A :					Optional[int]				,  A :					Tuple				,  A :					int				,  A :					Union[str, Any] )	-> Optional[int]:
					"""simple docstring"""





					a        =     full_name.split("pos_conv." )[-1]
					a        =     name.split("." )
					a        =     int(items[0] )
					a        =     int(items[1] )

					a        =     name.split("." )[-1]
					if type_id != 0:
										unused_weights.append(UpperCamelCase__ )
										return
					else:
										a        =     '''conv'''

					set_weights(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  f'''layers.{layer_id}.{layer_type}.{weight_type}''' )






@torch.no_grad()
def 					a(   A :					Optional[int]				,  A :					List[str]				,  A :					Tuple=None				,  A :					Union[str, Any]=None				,  A :					str=True )	-> str:
					"""simple docstring"""





					if config_path is not None:
										a        =     DataaVecAudioConfig.from_pretrained(UpperCamelCase__ )
					else:
										a        =     DataaVecAudioConfig()

					if not is_finetuned:
										# Modify final_proj layer name
										a        =     DataaVecAudioModel(UpperCamelCase__ )
										a        =     os.path.dirname(UpperCamelCase__ )

										a        =     torch.load(UpperCamelCase__ )
										a        =     state_dict['''model'''].pop("final_proj.0.weight" )
										a        =     state_dict['''model'''].pop("final_proj.0.bias" )
										a        =     os.path.join(UpperCamelCase__				,  "converted.pt" )
										torch.save(UpperCamelCase__				,  UpperCamelCase__ )
					else:
										a        =     DataaVecAudioForCTC(UpperCamelCase__ )
										a        =     checkpoint_path

					def load_dataavec(A :					Optional[Any] ):
										a        =     fairseq.checkpoint_utils.load_model_ensemble_and_task([path] )
										return model[0].eval()

					a        =     load_dataavec(UpperCamelCase__ )

					recursively_load_weights(UpperCamelCase__				,  UpperCamelCase__				,  not is_finetuned )

					a        =     WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60" )

					a        =     load_dataset("patrickvonplaten/librispeech_asr_dummy"				,  "clean"				,  split="validation" )
					a        =     [x['''array'''] for x in ds[:4]['''audio''']]

					a        =     processor(UpperCamelCase__				,  return_tensors="pt"				,  padding=UpperCamelCase__ )

					a        =     inputs.input_values
					a        =     inputs.attention_mask
					#    input_values = inputs.input_values[:, :-1]
					#    attention_mask = inputs.attention_mask[:, :-1]

					hf_wavavec.eval()
					model.eval()
					if is_finetuned:
										a        =     model(source=UpperCamelCase__				,  padding_mask=(1 - attention_mask)				,  mask=UpperCamelCase__				,  features_only=UpperCamelCase__ )[
										    '''encoder_out'''
										].transpose(0				,  1 )
										a        =     hf_wavavec(UpperCamelCase__				,  attention_mask=UpperCamelCase__ )['''logits''']

										a        =     torch.argmax(UpperCamelCase__				,  dim=-1 )
										a        =     processor.batch_decode(UpperCamelCase__ )

										print(f'''Expected Output: {ds[:4]['text']}, Pred: {output_string}''' )
					else:
										a        =     model(source=UpperCamelCase__				,  padding_mask=(1 - attention_mask)				,  mask=UpperCamelCase__				,  features_only=UpperCamelCase__ )[
										    '''layer_results'''
										][-1][0].transpose(0				,  1 )
										a        =     hf_wavavec(UpperCamelCase__				,  attention_mask=UpperCamelCase__ )['''last_hidden_state''']

					print(our_output.shape				,  their_output.shape )
					a        =     torch.max(torch.abs(our_output - their_output ) ).item()
					print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
					a        =     torch.allclose(UpperCamelCase__				,  UpperCamelCase__				,  atol=1e-3 )
					print("Do both models output the same tensors?"				,  "🔥" if success else "💩" )
					if not success:
										raise Exception("Something went wRoNg" )

					hf_wavavec.save_pretrained(UpperCamelCase__ )

					if is_finetuned:
										processor.save_pretrained(UpperCamelCase__ )
					else:
										processor.feature_extractor.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				_lowercase:				Union[str, Any]       =	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"
				)
				_lowercase:				Optional[Any]       =	parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)





def 					a(   A :					Optional[Any]				,  A :					List[str] )	-> List[Any]:
					"""simple docstring"""





					print("\nVertex Distance" )
					for i in range(A ):
										if dist[i] != float("inf" ):
															print(A				,  "\t"				,  int(dist[i] )				,  end="\t" )
										else:
															print(A				,  "\t"				,  "INF"				,  end="\t" )
										print()






def 					a(   A :					str				,  A :					int				,  A :					int )	-> Optional[int]:
					"""simple docstring"""





					a        =     float("inf" )
					a        =     -1
					for i in range(A ):
										if (not vset[i]) and mdist[i] < min_val:
															a        =     i
															a        =     mdist[i]
					return min_ind






def 					a(   A :					List[str]				,  A :					Union[str, Any]				,  A :					Dict )	-> Tuple:
					"""simple docstring"""





					a        =     [float("inf" ) for _ in range(A )]
					a        =     [False for _ in range(A )]
					a        =     0.0

					for _ in range(v - 1 ):
										a        =     min_dist(A				,  A				,  A )
										a        =     True

										for i in range(A ):
															if (
															    (not vset[i])
															    and graph[u][i] != float("inf" )
															    and mdist[u] + graph[u][i] < mdist[i]
															):
																				a        =     mdist[u] + graph[u][i]

					print_dist(A				,  A )


if __name__ == "__main__":
				_lowercase:				Any       =	int(input("Enter number of vertices: ").strip())
				_lowercase:				Optional[Any]       =	int(input("Enter number of edges: ").strip())

				_lowercase:				List[str]       =	[[float("inf") for i in range(V)] for j in range(V)]

				for i in range(V):
								_lowercase:				List[Any]       =	0.0

				for i in range(E):
								print("\nEdge ", i + 1)
								_lowercase:				Tuple       =	int(input("Enter source:").strip())
								_lowercase:				Tuple       =	int(input("Enter destination:").strip())
								_lowercase:				int       =	float(input("Enter weight:").strip())
								_lowercase:				str       =	weight

				_lowercase:				Union[str, Any]       =	int(input("\nEnter shortest path source:").strip())
				dijkstra(graph, V, gsrc)
