


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    		=       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',
    '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    		=       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def        UpperCamelCase_(				_A   :List[str]				,     _A   :int				,     _A   :Any				,     _A   :List[str]				,     _A   :str  )->		Optional[Any]:
			for attribute in key.split("."  ):
						UpperCamelCase__					=       getattr(_A				,     _A  )

			if weight_type is not None:
						UpperCamelCase__					=       getattr(_A				,     _A  ).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        UpperCamelCase_(				_A   :List[str]				,     _A   :Optional[int]  )->		Dict:
			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(
									    _A				,     _A				,     _A				,     _A				,     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(_A				,     _A				,     _A				,     _A  )
									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(_A  )[0].split("."  )[-2]
																		UpperCamelCase__					=       mapped_key.replace("*"				,     _A  )
															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(_A				,     _A				,     _A				,     _A				,     _A  )
												continue
						if not is_used:
									unused_weights.append(_A  )

			logger.warning(F'''Unused weights: {unused_weights}'''  )
def        UpperCamelCase_(				_A   :Optional[int]				,     _A   :List[str]				,     _A   :Optional[Any]				,     _A   :str				,     _A   :List[Any]  )->		Tuple:
			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(_A  )
def        UpperCamelCase_(				_A   :Tuple				,     _A   :List[str]				,     _A   :Dict				,     _A   :List[Any]  )->		int:
			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(_A				,     _A  ):
						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(_A  )
def        UpperCamelCase_(				_A   :Tuple  )->		Tuple:
			UpperCamelCase__,      UpperCamelCase__					=       emb.weight.shape
			UpperCamelCase__					=       nn.Linear(_A				,     _A				,     bias=_A  )
			UpperCamelCase__					=       emb.weight.data
			return lin_layer
@torch.no_grad()
def        UpperCamelCase_(				_A   :List[str]				,     _A   :int				,     _A   :str				,     _A   :Any				,     _A   :Union[str, Any]				,     _A   :Optional[Any]				,     _A   :Dict				,     _A   :Tuple				,     _A   :Optional[int]				,     _A   :Optional[Any]				,     _A   :List[Any]				,     )->		str:
			UpperCamelCase__					=       WavaVecaConfig.from_pretrained(
			    _A				,     add_adapter=_A				,     adapter_stride=_A				,     adapter_kernel_size=_A				,     use_auth_token=_A				,     output_hidden_size=_A				,     )
			UpperCamelCase__					=       MBartConfig.from_pretrained(_A  )

			# load model
			UpperCamelCase__,      UpperCamelCase__,      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(_A				,     use_auth_token=_A  )

			# set weights for wav2vec2 encoder
			UpperCamelCase__					=       WavaVecaModel(_A  )

			recursively_load_weights_wavaveca(model.encoder				,     _A  )

			# load decoder weights
			UpperCamelCase__					=       MBartForCausalLM(_A  )
			UpperCamelCase__,      UpperCamelCase__					=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,     strict=_A  )
			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=_A				,     decoder=_A  )
			UpperCamelCase__					=       False

			UpperCamelCase__					=       MBartaaTokenizer(_A  )
			tokenizer.save_pretrained(_A  )

			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(_A  )

			hf_wavavec.save_pretrained(_A  )
			feature_extractor.save_pretrained(_A  )


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_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=1_0_2_4, type=int, help='encoder output dim')
						parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config')

						__UpperCamelCase    		=       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,
						)






def        UpperCamelCase_(				_A   :str  )->		int:
			UpperCamelCase__					=       hex_num.strip()
			if not hex_num:
						raise ValueError("No value was passed to the function"  )

			UpperCamelCase__					=       hex_num[0] == "-"
			if is_negative:
						UpperCamelCase__					=       hex_num[1:]

			try:
						UpperCamelCase__					=       int(_A				,     16  )
			except ValueError:
						raise ValueError("Invalid value was passed to the function"  )

			UpperCamelCase__					=       ""
			while int_num > 0:
						UpperCamelCase__					=       str(int_num % 2  ) + bin_str
						int_num >>= 1

			return int(("-" + bin_str) if is_negative else bin_str  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


