






"""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()
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      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str]      )		->str:
						for attribute in key.split("""."""      ):
												A__						:  Any		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      )

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

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Any      )		->Union[str, Any]:
						A__						:  Dict		  =  []
						A__						:  int		  =  fairseq_model.state_dict()

						A__						:  Dict		  =  hf_model.feature_extractor
						A__						:  Any		  =  hf_model.adapter

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict      )		->str:
						A__						:  Tuple		  =  full_name.split("""adaptor."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )

						if items[1].isdigit():
												A__						:  Optional[Any]		  =  int(items[1]      )
						else:
												A__						:  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.'
																								A__						:  Union[str, 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.'
																								A__						:  Tuple		  =  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__						:  str		  =  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__						:  Union[str, Any]		  =  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__						:  Dict		  =  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__						:  Optional[int]		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->List[Any]:
						A__  ,  A__						:  Union[str, Any]		  =  emb.weight.shape
						A__						:  List[str]		  =  nn.Linear(UpperCAmelCase__,						UpperCAmelCase__,						bias=UpperCAmelCase__      )
						A__						:  List[Any]		  =  emb.weight.data
						return lin_layer




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						)		->str:
						A__						:  Tuple		  =  WavaVecaConfig.from_pretrained(
						    UpperCAmelCase__,						add_adapter=UpperCAmelCase__,						adapter_stride=UpperCAmelCase__,						adapter_kernel_size=UpperCAmelCase__,						use_auth_token=UpperCAmelCase__,						output_hidden_size=UpperCAmelCase__,						)
						A__						:  List[Any]		  =  MBartConfig.from_pretrained(UpperCAmelCase__      )

						# load model
						A__  ,  A__  ,  A__						:  str		  =  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__						:  List[Any]		  =  model[0].eval()

						# load feature extractor
						A__						:  Any		  =  WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__,						use_auth_token=UpperCAmelCase__      )

						# set weights for wav2vec2 encoder
						A__						:  Dict		  =  WavaVecaModel(UpperCAmelCase__      )

						recursively_load_weights_wavaveca(model.encoder,						UpperCAmelCase__      )

						# load decoder weights
						A__						:  Any		  =  MBartForCausalLM(UpperCAmelCase__      )
						A__  ,  A__						:  Union[str, Any]		  =  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__						:  Dict		  =  SpeechEncoderDecoderModel(encoder=UpperCAmelCase__,						decoder=UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  False

						A__						:  Optional[Any]		  =  MBartaaTokenizer(UpperCAmelCase__      )
						tokenizer.save_pretrained(UpperCAmelCase__      )

						A__						:  Dict		  =  hf_wavavec.config.to_dict()
						A__						:  List[Any]		  =  tokenizer.pad_token_id
						A__						:  Optional[Any]		  =  tokenizer.bos_token_id
						A__						:  List[Any]		  =  tokenizer.eos_token_id
						A__						:  Tuple		  =  """mbart50"""
						A__						:  List[str]		  =  """wav2vec2"""

						A__						:  Optional[int]		  =  tokenizer.eos_token_id
						A__						:  int		  =  2_5_0_0_0_4
						A__						:  Dict		  =  tokenizer.eos_token_id

						A__						:  str		  =  SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__      )

						hf_wavavec.save_pretrained(UpperCAmelCase__      )
						feature_extractor.save_pretrained(UpperCAmelCase__      )


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,
						)














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






