



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()
__A       =						logging.get_logger(__name__)

__A       =						{
    '''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''',
}
__A       =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Tuple    )				->			Union[str, Any]:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : int    )				->			Tuple:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[int]    )				->			Union[str, Any]:






						'''simple docstring'''



						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 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Any:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Optional[Any]      ,)				->			Optional[Any]:






						'''simple docstring'''



						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_,       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(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_,       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__":
	__A       =						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''')

	__A       =						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,
	)









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       Namespace(**checkpoint["""cfg"""]["""model"""]    )
						UpperCAmelCase_=       checkpoint["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						UpperCAmelCase_=       state_dict["""decoder.embed_tokens.weight"""].shape[0]

						UpperCAmelCase_=       {key.replace("""decoder"""      ,"""model"""    ): val for key, val in state_dict.items()}

						UpperCAmelCase_=       XGLMConfig(
						    vocab_size=lowerCAmelCase_      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="""gelu"""      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	__A       =						parser.parse_args()
	__A       =						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)




