




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()
__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """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""",
}
__SCREAMING_SNAKE_CASE 						=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							for attribute in key.split('''.'''     ):
														SCREAMING_SNAKE_CASE_   	=    getattr(_lowerCAmelCase  ,    _lowerCAmelCase     )

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

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    []
							SCREAMING_SNAKE_CASE_   	=    fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE_   	=    hf_model.feature_extractor
							SCREAMING_SNAKE_CASE_   	=    hf_model.adapter

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE_   	=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    hf_model.config.feat_extract_norm == '''group'''  ,    )
																					SCREAMING_SNAKE_CASE_   	=    True
														elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']     ):
																					load_adapter(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
																																			SCREAMING_SNAKE_CASE_   	=    True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE_   	=    name.split(_lowerCAmelCase     )[0].split('''.'''     )[-2]
																																										SCREAMING_SNAKE_CASE_   	=    mapped_key.replace('''*'''  ,    _lowerCAmelCase     )
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight_g'''
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight_v'''
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''bias'''
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight'''
																																			else:
																																										SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    full_name.split('''conv_layers.'''     )[-1]
							SCREAMING_SNAKE_CASE_   	=    name.split('''.'''     )
							SCREAMING_SNAKE_CASE_   	=    int(items[0]     )
							SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
							else:
														unused_weights.append(_lowerCAmelCase     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    full_name.split('''adaptor.'''     )[-1]
							SCREAMING_SNAKE_CASE_   	=    name.split('''.'''     )

							if items[1].isdigit():
														SCREAMING_SNAKE_CASE_   	=    int(items[1]     )
							else:
														SCREAMING_SNAKE_CASE_   	=    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."
																												SCREAMING_SNAKE_CASE_   	=    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."
																												SCREAMING_SNAKE_CASE_   	=    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."
																												SCREAMING_SNAKE_CASE_   	=    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."
																												SCREAMING_SNAKE_CASE_   	=    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."
																					SCREAMING_SNAKE_CASE_   	=    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."
																					SCREAMING_SNAKE_CASE_   	=    value
																					logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."     )
							else:
														unused_weights.append(_lowerCAmelCase     )

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

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

							# load model
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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,
							    }  ,    )
							SCREAMING_SNAKE_CASE_   	=    model[0].eval()

							# load feature extractor
							SCREAMING_SNAKE_CASE_   	=    WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase  ,    use_auth_token=_lowerCAmelCase     )

							# set weights for wav2vec2 encoder
							SCREAMING_SNAKE_CASE_   	=    WavaVecaModel(_lowerCAmelCase     )

							recursively_load_weights_wavaveca(model.encoder  ,    _lowerCAmelCase     )

							# load decoder weights
							SCREAMING_SNAKE_CASE_   	=    MBartForCausalLM(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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}"     )

							SCREAMING_SNAKE_CASE_   	=    SpeechEncoderDecoderModel(encoder=_lowerCAmelCase  ,    decoder=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    False

							SCREAMING_SNAKE_CASE_   	=    MBartaaTokenizer(_lowerCAmelCase     )
							tokenizer.save_pretrained(_lowerCAmelCase     )

							SCREAMING_SNAKE_CASE_   	=    hf_wavavec.config.to_dict()
							SCREAMING_SNAKE_CASE_   	=    tokenizer.pad_token_id
							SCREAMING_SNAKE_CASE_   	=    tokenizer.bos_token_id
							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE_   	=    '''mbart50'''
							SCREAMING_SNAKE_CASE_   	=    '''wav2vec2'''

							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE_   	=    2_5_0_0_0_4
							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id

							SCREAMING_SNAKE_CASE_   	=    SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase     )

							hf_wavavec.save_pretrained(_lowerCAmelCase     )
							feature_extractor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=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_024, type=int, help="""encoder output dim""")
			parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

			__SCREAMING_SNAKE_CASE 						=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 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


