






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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Dict:
							for attribute in key.split('.'				):
														_lowercase							:   Union[str, Any] 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							if weight_type is not None:
														_lowercase							:   List[str] 							=   getattr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				).shape
							else:
														_lowercase							:   int 							=   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":
														_lowercase							:   List[Any] 							=   value
							elif weight_type == "weight_g":
														_lowercase							:   Optional[Any] 							=   value
							elif weight_type == "weight_v":
														_lowercase							:   Any 							=   value
							elif weight_type == "bias":
														_lowercase							:   Any 							=   value
							else:
														_lowercase							:   int 							=   value

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    List[str]:
							_lowercase							:   Any 							=   []
							_lowercase							:   int 							=   fairseq_model.state_dict()

							_lowercase							:   str 							=   hf_model.feature_extractor
							_lowercase							:   Tuple 							=   hf_model.adapter

							for name, value in fairseq_dict.items():
														_lowercase							:   Any 							=   False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     hf_model.config.feat_extract_norm == 'group'     ,     )
																					_lowercase							:   Tuple 							=   True
														elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']				):
																					load_adapter(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					_lowercase							:   Tuple 							=   True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
																																			_lowercase							:   int 							=   True
																																			if "*" in mapped_key:
																																										_lowercase							:   Tuple 							=   name.split(SCREAMING_SNAKE_CASE				)[0].split('.'				)[-2]
																																										_lowercase							:   Optional[int] 							=   mapped_key.replace('*'     ,     SCREAMING_SNAKE_CASE				)
																																			if "weight_g" in name:
																																										_lowercase							:   Tuple 							=   'weight_g'
																																			elif "weight_v" in name:
																																										_lowercase							:   List[str] 							=   'weight_v'
																																			elif "bias" in name:
																																										_lowercase							:   Optional[int] 							=   'bias'
																																			elif "weight" in name:
																																										_lowercase							:   Tuple 							=   'weight'
																																			else:
																																										_lowercase							:   int 							=   None
																																			set_recursively(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												continue
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE				)

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   int 							=   full_name.split('conv_layers.'				)[-1]
							_lowercase							:   Dict 							=   name.split('.'				)
							_lowercase							:   List[Any] 							=   int(items[0]				)
							_lowercase							:   List[Any] 							=   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."""
																					)
																					_lowercase							:   Any 							=   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."""
																					)
																					_lowercase							:   int 							=   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."
																					)
																					_lowercase							:   Tuple 							=   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."""
																					)
																					_lowercase							:   Union[str, Any] 							=   value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   Dict 							=   full_name.split('adaptor.'				)[-1]
							_lowercase							:   Optional[int] 							=   name.split('.'				)

							if items[1].isdigit():
														_lowercase							:   Any 							=   int(items[1]				)
							else:
														_lowercase							:   Any 							=   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."""
																												_lowercase							:   Any 							=   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."""
																												_lowercase							:   str 							=   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."""
																												_lowercase							:   int 							=   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."""
																												_lowercase							:   Optional[Any] 							=   value
																												logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""				)
							elif isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														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."""
																					_lowercase							:   Union[str, Any] 							=   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."""
																					_lowercase							:   Optional[int] 							=   value
																					logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""				)
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Tuple:
							_lowercase							,							_lowercase							:   Tuple 							=   emb.weight.shape
							_lowercase							:   Optional[int] 							=   nn.Linear(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     bias=SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   emb.weight.data
							return lin_layer






@torch.no_grad()
def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     )     ->    List[Any]:
							_lowercase							:   List[str] 							=   WavaVecaConfig.from_pretrained(
							    SCREAMING_SNAKE_CASE     ,     add_adapter=SCREAMING_SNAKE_CASE     ,     adapter_stride=SCREAMING_SNAKE_CASE     ,     adapter_kernel_size=SCREAMING_SNAKE_CASE     ,     use_auth_token=SCREAMING_SNAKE_CASE     ,     output_hidden_size=SCREAMING_SNAKE_CASE     ,     )
							_lowercase							:   Optional[Any] 							=   MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE				)

							# load model
							_lowercase							,							_lowercase							,							_lowercase							:   Any 							=   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,
							    }     ,     )
							_lowercase							:   int 							=   model[0].eval()

							# load feature extractor
							_lowercase							:   int 							=   WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     ,     use_auth_token=SCREAMING_SNAKE_CASE				)

							# set weights for wav2vec2 encoder
							_lowercase							:   str 							=   WavaVecaModel(SCREAMING_SNAKE_CASE				)

							recursively_load_weights_wavaveca(model.encoder     ,     SCREAMING_SNAKE_CASE				)

							# load decoder weights
							_lowercase							:   Any 							=   MBartForCausalLM(SCREAMING_SNAKE_CASE				)
							_lowercase							,							_lowercase							:   Dict 							=   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,     strict=SCREAMING_SNAKE_CASE				)
							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}"""				)

							_lowercase							:   List[Any] 							=   SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE     ,     decoder=SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   False

							_lowercase							:   Tuple 							=   MBartaaTokenizer(SCREAMING_SNAKE_CASE				)
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE				)

							_lowercase							:   List[str] 							=   hf_wavavec.config.to_dict()
							_lowercase							:   List[str] 							=   tokenizer.pad_token_id
							_lowercase							:   List[Any] 							=   tokenizer.bos_token_id
							_lowercase							:   List[str] 							=   tokenizer.eos_token_id
							_lowercase							:   int 							=   'mbart50'
							_lowercase							:   Tuple 							=   'wav2vec2'

							_lowercase							:   Optional[Any] 							=   tokenizer.eos_token_id
							_lowercase							:   Optional[Any] 							=   250_004
							_lowercase							:   Dict 							=   tokenizer.eos_token_id

							_lowercase							:   int 							=   SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE				)

							hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE				)
							feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE				)


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_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								=		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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



