


'''simple docstring'''
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					:				Optional[int]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				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''',
}
__SCREAMING_SNAKE_CASE					:				List[str]									=		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							for attribute in key.split("." ):
														A_			    =		getattr(UpperCamelCase_						,  UpperCamelCase_ )

							if weight_type is not None:
														A_			    =		getattr(UpperCamelCase_						,  UpperCamelCase_ ).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_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							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(
																					    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  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(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
																					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(UpperCamelCase_ )[0].split("." )[-2]
																																										A_			    =		mapped_key.replace("*"						,  UpperCamelCase_ )
																																			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(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase_ )

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							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(UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							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(UpperCamelCase_						,  UpperCamelCase_ ):
														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(UpperCamelCase_ )







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







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

							# 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(UpperCamelCase_						,  use_auth_token=UpperCamelCase_ )

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

							recursively_load_weights_wavaveca(model.encoder						,  UpperCamelCase_ )

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

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

							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_			    =		2_5_0_0_0_4
							A_			    =		tokenizer.eos_token_id

							A_			    =		SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_ )

							hf_wavavec.save_pretrained(UpperCamelCase_ )
							feature_extractor.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		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=250004, type=int, help='''`decoder_start_token_id` of model config''')

							__SCREAMING_SNAKE_CASE					:				Tuple									=		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,
							)









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		{
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[Any]   						="transfo-xl"
						_UpperCAmelCase		:       str   						=["mems"]
						_UpperCAmelCase		:       Optional[int]   						={
						    "n_token": "vocab_size",
						    "hidden_size": "d_model",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}






						def __init__(  self							:      str ,  lowerCAmelCase							:      Tuple=26_77_35 ,  lowerCAmelCase							:      List[Any]=[2_00_00, 4_00_00, 20_00_00] ,  lowerCAmelCase							:      Dict=10_24 ,  lowerCAmelCase							:      List[str]=10_24 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      Optional[Any]=64 ,  lowerCAmelCase							:      str=40_96 ,  lowerCAmelCase							:      Optional[int]=4 ,  lowerCAmelCase							:      Any=False ,  lowerCAmelCase							:      List[str]=18 ,  lowerCAmelCase							:      Any=16_00 ,  lowerCAmelCase							:      List[str]=10_00 ,  lowerCAmelCase							:      int=True ,  lowerCAmelCase							:      List[str]=True ,  lowerCAmelCase							:      List[str]=0 ,  lowerCAmelCase							:      Tuple=-1 ,  lowerCAmelCase							:      Optional[int]=True ,  lowerCAmelCase							:      List[str]=0.1 ,  lowerCAmelCase							:      int=0.0 ,  lowerCAmelCase							:      Tuple=True ,  lowerCAmelCase							:      List[str]="normal" ,  lowerCAmelCase							:      Tuple=0.0_1 ,  lowerCAmelCase							:      Optional[int]=0.0_1 ,  lowerCAmelCase							:      List[Any]=0.0_2 ,  lowerCAmelCase							:      Dict=1e-5 ,  lowerCAmelCase							:      Optional[int]=0 ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		vocab_size
													A_			    =		[]
													self.cutoffs.extend(lowerCAmelCase	)
													if proj_share_all_but_first:
																				A_			    =		[False] + [True] * len(self.cutoffs	)
													else:
																				A_			    =		[False] + [False] * len(self.cutoffs	)
													A_			    =		d_model
													A_			    =		d_embed
													A_			    =		d_head
													A_			    =		d_inner
													A_			    =		div_val
													A_			    =		pre_lnorm
													A_			    =		n_layer
													A_			    =		n_head
													A_			    =		mem_len
													A_			    =		same_length
													A_			    =		attn_type
													A_			    =		clamp_len
													A_			    =		sample_softmax
													A_			    =		adaptive
													A_			    =		dropout
													A_			    =		dropatt
													A_			    =		untie_r
													A_			    =		init
													A_			    =		init_range
													A_			    =		proj_init_std
													A_			    =		init_std
													A_			    =		layer_norm_epsilon
													super().__init__(eos_token_id=lowerCAmelCase ,  **lowerCAmelCase	)






						@property
						def 				_UpperCAmelCase       (  self							:      str	):
													# Message copied from Transformer-XL documentation
													logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."	)
													return -1






						@max_position_embeddings.setter
						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Dict	):
													# Message copied from Transformer-XL documentation
													raise NotImplementedError(
													    F"The model {self.model_type} is one of the few models that has no sequence length limit."	)





