



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

UpperCAmelCase      :							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',
    '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',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase      :							Dict							       =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):





					for attribute in key.split("."):
										UpperCamelCase_						     =      getattr(_lowerCamelCase      ,		_lowerCamelCase)

					if weight_type is not None:
										UpperCamelCase_						     =      getattr(_lowerCamelCase      ,		_lowerCamelCase).shape
					else:
										UpperCamelCase_						     =      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_						     =      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       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase):
					UpperCamelCase_						     =      []
					UpperCamelCase_						     =      fairseq_model.state_dict()

					UpperCamelCase_						     =      hf_model.feature_extractor
					UpperCamelCase_						     =      hf_model.adapter

					for name, value in fairseq_dict.items():
										UpperCamelCase_						     =      False
										if "conv_layers" in name:
															load_conv_layer(
															    _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		hf_model.config.feat_extract_norm == "group"      ,		)
															UpperCamelCase_						     =      True
										elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]):
															load_adapter(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase)
															UpperCamelCase_						     =      True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																									UpperCamelCase_						     =      True
																									if "*" in mapped_key:
																														UpperCamelCase_						     =      name.split(_lowerCamelCase)[0].split(".")[-2]
																														UpperCamelCase_						     =      mapped_key.replace("*"      ,		_lowerCamelCase)
																									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:
																														UpperCamelCase_						     =      "weight"
																									else:
																														UpperCamelCase_						     =      None
																									set_recursively(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase)
																				continue
										if not is_used:
															unused_weights.append(_lowerCamelCase)

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



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					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:
															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_						     =      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_						     =      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_						     =      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_						     =      value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
					else:
										unused_weights.append(_lowerCamelCase)



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase):
					UpperCamelCase_						     =      full_name.split("adaptor.")[-1]
					UpperCamelCase_						     =      name.split(".")

					if items[1].isdigit():
										UpperCamelCase_						     =      int(items[1])
					else:
										UpperCamelCase_						     =      None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																				UpperCamelCase_						     =      value
																				logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}.""")
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																				UpperCamelCase_						     =      value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																				UpperCamelCase_						     =      value
																				logger.info(f"""Adapter proj layer bias was initialized from {full_name}.""")
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																				UpperCamelCase_						     =      value
																				logger.info(f"""Adapter proj layer weight was initialized from {full_name}.""")
					elif isinstance(_lowerCamelCase      ,		_lowerCamelCase):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
															UpperCamelCase_						     =      value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""")
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
															UpperCamelCase_						     =      value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""")
					else:
										unused_weights.append(_lowerCamelCase)



def       _lowerCAmelCase			(_lowerCAmelCase):
					UpperCamelCase_						     =      emb.weight.shape
					UpperCamelCase_						     =      nn.Linear(_lowerCamelCase      ,		_lowerCamelCase      ,		bias=_lowerCamelCase)
					UpperCamelCase_						     =      emb.weight.data
					return lin_layer



@torch.no_grad()
def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		_lowerCAmelCase      ,		):
					UpperCamelCase_						     =      WavaVecaConfig.from_pretrained(
					    _lowerCamelCase      ,		add_adapter=_lowerCamelCase      ,		adapter_stride=_lowerCamelCase      ,		adapter_kernel_size=_lowerCamelCase      ,		use_auth_token=_lowerCamelCase      ,		output_hidden_size=_lowerCamelCase      ,		)
					UpperCamelCase_						     =      MBartConfig.from_pretrained(_lowerCamelCase)

					# load model
					UpperCamelCase_						     =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]      ,		arg_overrides={
					        "config_yaml": config_yaml_path,
					        "data": "/".join(dict_path.split("/")[:-1]),
					        "w2v_path": checkpoint_path,
					        "load_pretrained_decoder_from": None,
					    }      ,		)
					UpperCamelCase_						     =      model[0].eval()

					# load feature extractor
					UpperCamelCase_						     =      WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase      ,		use_auth_token=_lowerCamelCase)

					# set weights for wav2vec2 encoder
					UpperCamelCase_						     =      WavaVecaModel(_lowerCamelCase)

					recursively_load_weights_wavaveca(model.encoder      ,		_lowerCamelCase)

					# load decoder weights
					UpperCamelCase_						     =      MBartForCausalLM(_lowerCamelCase)
					UpperCamelCase_						     =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,		strict=_lowerCamelCase)
					logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""")
					logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""")

					UpperCamelCase_						     =      SpeechEncoderDecoderModel(encoder=_lowerCamelCase      ,		decoder=_lowerCamelCase)
					UpperCamelCase_						     =      False

					UpperCamelCase_						     =      MBartaaTokenizer(_lowerCamelCase)
					tokenizer.save_pretrained(_lowerCamelCase)

					UpperCamelCase_						     =      hf_wavavec.config.to_dict()
					UpperCamelCase_						     =      tokenizer.pad_token_id
					UpperCamelCase_						     =      tokenizer.bos_token_id
					UpperCamelCase_						     =      tokenizer.eos_token_id
					UpperCamelCase_						     =      "mbart50"
					UpperCamelCase_						     =      "wav2vec2"

					UpperCamelCase_						     =      tokenizer.eos_token_id
					UpperCamelCase_						     =      25_00_04
					UpperCamelCase_						     =      tokenizer.eos_token_id

					UpperCamelCase_						     =      SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase)

					hf_wavavec.save_pretrained(_lowerCamelCase)
					feature_extractor.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
	UpperCAmelCase      :							List[str]							       =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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
	parser.add_argument(
	    """--encoder_config_path""",
	    default="""facebook/wav2vec2-xls-r-1b""",
	    type=str,
	    help="""Path to hf encoder wav2vec2 checkpoint config""",
	)
	parser.add_argument(
	    """--decoder_config_path""",
	    default="""facebook/mbart-large-50-one-to-many-mmt""",
	    type=str,
	    help="""Path to hf decoder checkpoint config""",
	)
	parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
	parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
	parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
	parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
	parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

	UpperCAmelCase      :							Union[str, Any]							       =parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    args.config_yaml_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    add_adapter=args.add_adapter,
	    adapter_kernel_size=args.adapter_kernel_size,
	    adapter_stride=args.adapter_stride,
	    decoder_start_token_id=args.start_token_id,
	    encoder_output_dim=args.encoder_output_dim,
	)


class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3