



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''ClapFeatureExtractor'''
				__lowerCamelCase =				('''RobertaTokenizer''', '''RobertaTokenizerFast''')




				def __init__(							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									super().__init__(_snake_case						,				_snake_case		)




				def __call__(							self						,				_snake_case=None						,				_snake_case=None						,				_snake_case=None						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    kwargs.pop("""sampling_rate"""						,				_snake_case		)

									if text is None and audios is None:
														raise ValueError("""You have to specify either text or audios. Both cannot be none."""		)

									if text is not None:
														_lowerCAmelCase						=    self.tokenizer(_snake_case						,				return_tensors=_snake_case						,				**_snake_case		)

									if audios is not None:
														_lowerCAmelCase						=    self.feature_extractor(
														    _snake_case						,				sampling_rate=_snake_case						,				return_tensors=_snake_case						,				**_snake_case		)

									if text is not None and audios is not None:
														_lowerCAmelCase						=    audio_features.input_features
														return encoding
									elif text is not None:
														return encoding
									else:
														return BatchEncoding(data=dict(**_snake_case		)						,				tensor_type=_snake_case		)




				def 							snake_case   (							self						,				*_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return self.tokenizer.batch_decode(*_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				*_snake_case						,				**_snake_case		):



									"""simple docstring"""





									return self.tokenizer.decode(*_snake_case						,				**_snake_case		)




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.tokenizer.model_input_names
									_lowerCAmelCase						=    self.feature_extractor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)









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					:       str    					=      logging.get_logger(__name__)

__UpperCamelCase					:       List[str]    					=      {
    "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					:       Union[str, Any]    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	if weight_type is not None:
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	else:
		a										=							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":
		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		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       int:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.feature_extractor
	a										=							hf_model.adapter

	for name, value in fairseq_dict.items():
		a										=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]    ):
			load_adapter(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			a										=							True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
					a										=							True
					if "*" in mapped_key:
						a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
						a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
					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:
						a										=							"""weight"""
					else:
						a										=							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    )      ->       Tuple:
	a										=							full_name.split("""conv_layers."""    )[-1]
	a										=							name.split("""."""    )
	a										=							int(items[0]    )
	a										=							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.'
			)
			a										=							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.'
			)
			a										=							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."
			)
			a										=							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.'
			)
			a										=							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    )      ->       Any:
	a										=							full_name.split("""adaptor."""    )[-1]
	a										=							name.split("""."""    )

	if items[1].isdigit():
		a										=							int(items[1]    )
	else:
		a										=							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.'
				a										=							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.'
				a										=							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.'
				a										=							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.'
				a										=							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.'
			a										=							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.'
			a										=							value
			logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





def 		__A		(	__lowerCamelCase    )      ->       Tuple:
	a				, a										=							emb.weight.shape
	a										=							nn.Linear(__lowerCamelCase							,			__lowerCamelCase							,			bias=__lowerCamelCase    )
	a										=							emb.weight.data
	return lin_layer





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			)      ->       Optional[int]:
	a										=							WavaVecaConfig.from_pretrained(
	    __lowerCamelCase							,			add_adapter=__lowerCamelCase							,			adapter_stride=__lowerCamelCase							,			adapter_kernel_size=__lowerCamelCase							,			use_auth_token=__lowerCamelCase							,			output_hidden_size=__lowerCamelCase							,			)
	a										=							MBartConfig.from_pretrained(__lowerCamelCase    )

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

	# load feature extractor
	a										=							WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase							,			use_auth_token=__lowerCamelCase    )

	# set weights for wav2vec2 encoder
	a										=							WavaVecaModel(__lowerCamelCase    )

	recursively_load_weights_wavaveca(model.encoder							,			__lowerCamelCase    )

	# load decoder weights
	a										=							MBartForCausalLM(__lowerCamelCase    )
	a				, a										=							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}'    )

	a										=							SpeechEncoderDecoderModel(encoder=__lowerCamelCase							,			decoder=__lowerCamelCase    )
	a										=							False

	a										=							MBartaaTokenizer(__lowerCamelCase    )
	tokenizer.save_pretrained(__lowerCamelCase    )

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

	a										=							tokenizer.eos_token_id
	a										=							25_0004
	a										=							tokenizer.eos_token_id

	a										=							SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )
	feature_extractor.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       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_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")

		__UpperCamelCase					:       int    					=      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,
		)

