






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				: Optional[int]						    =	{
    '''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				: int						    =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def        lowercase_			(     __snake_case  :				int						,						__snake_case  :				List[str]						,						__snake_case  :				Any						,						__snake_case  :				List[Any]						,						__snake_case  :				Optional[int]   )  ->			int:





				'''simple docstring'''


				for attribute in key.split("."   ):
								snake_case__   :Dict       = getattr(_UpperCamelCase						,						_UpperCamelCase   )

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

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





def        lowercase_			(     __snake_case  :				int						,						__snake_case  :				Tuple   )  ->			str:





				'''simple docstring'''


				snake_case__   :Optional[int]       = []
				snake_case__   :Union[str, Any]       = fairseq_model.state_dict()

				snake_case__   :str       = hf_model.feature_extractor
				snake_case__   :List[Any]       = hf_model.adapter

				for name, value in fairseq_dict.items():
								snake_case__   :Union[str, Any]       = False
								if "conv_layers" in name:
												load_conv_layer(
												    _UpperCamelCase						,						_UpperCamelCase						,						_UpperCamelCase						,						_UpperCamelCase						,						hf_model.config.feat_extract_norm == "group"						,						)
												snake_case__   :Any       = True
								elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]   ):
												load_adapter(_UpperCamelCase						,						_UpperCamelCase						,						_UpperCamelCase						,						_UpperCamelCase   )
												snake_case__   :List[str]       = True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
																				snake_case__   :int       = True
																				if "*" in mapped_key:
																								snake_case__   :Any       = name.split(_UpperCamelCase   )[0].split("."   )[-2]
																								snake_case__   :int       = mapped_key.replace("*"						,						_UpperCamelCase   )
																				if "weight_g" in name:
																								snake_case__   :List[Any]       = "weight_g"
																				elif "weight_v" in name:
																								snake_case__   :List[str]       = "weight_v"
																				elif "bias" in name:
																								snake_case__   :str       = "bias"
																				elif "weight" in name:
																								snake_case__   :Optional[Any]       = "weight"
																				else:
																								snake_case__   :str       = 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        lowercase_			(     __snake_case  :				Dict						,						__snake_case  :				List[Any]						,						__snake_case  :				Optional[int]						,						__snake_case  :				Optional[Any]						,						__snake_case  :				Optional[Any]   )  ->			Dict:





				'''simple docstring'''


				snake_case__   :int       = full_name.split("conv_layers."   )[-1]
				snake_case__   :List[str]       = name.split("."   )
				snake_case__   :Any       = int(items[0]   )
				snake_case__   :int       = 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.'
												)
												snake_case__   :Tuple       = 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.'
												)
												snake_case__   :Union[str, Any]       = 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."
												)
												snake_case__   :Union[str, Any]       = 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.'
												)
												snake_case__   :List[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        lowercase_			(     __snake_case  :				Dict						,						__snake_case  :				str						,						__snake_case  :				Optional[int]						,						__snake_case  :				Tuple   )  ->			Any:





				'''simple docstring'''


				snake_case__   :List[Any]       = full_name.split("adaptor."   )[-1]
				snake_case__   :Optional[Any]       = name.split("."   )

				if items[1].isdigit():
								snake_case__   :int       = int(items[1]   )
				else:
								snake_case__   :Dict       = 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.'
																snake_case__   :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.'
																snake_case__   :List[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.'
																snake_case__   :List[Any]       = 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.'
																snake_case__   :int       = 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.'
												snake_case__   :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.'
												snake_case__   :str       = value
												logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'   )
				else:
								unused_weights.append(_UpperCamelCase   )





def        lowercase_			(     __snake_case  :				Tuple   )  ->			Optional[Any]:





				'''simple docstring'''


				snake_case__     ,    snake_case__   :List[Any]       = emb.weight.shape
				snake_case__   :Optional[Any]       = nn.Linear(_UpperCamelCase						,						_UpperCamelCase						,						bias=_UpperCamelCase   )
				snake_case__   :Any       = emb.weight.data
				return lin_layer





@torch.no_grad()
def        lowercase_			(     __snake_case  :				List[str]						,						__snake_case  :				Dict						,						__snake_case  :				Dict						,						__snake_case  :				str						,						__snake_case  :				Union[str, Any]						,						__snake_case  :				Tuple						,						__snake_case  :				Any						,						__snake_case  :				Tuple						,						__snake_case  :				List[Any]						,						__snake_case  :				Union[str, Any]						,						__snake_case  :				Tuple						,						)  ->			List[str]:





				'''simple docstring'''


				snake_case__   :Any       = WavaVecaConfig.from_pretrained(
				    _UpperCamelCase						,						add_adapter=_UpperCamelCase						,						adapter_stride=_UpperCamelCase						,						adapter_kernel_size=_UpperCamelCase						,						use_auth_token=_UpperCamelCase						,						output_hidden_size=_UpperCamelCase						,						)
				snake_case__   :Optional[Any]       = MBartConfig.from_pretrained(_UpperCamelCase   )

				# load model
				snake_case__     ,    snake_case__     ,    snake_case__   :Optional[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,
				    }						,						)
				snake_case__   :Any       = model[0].eval()

				# load feature extractor
				snake_case__   :List[Any]       = WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase						,						use_auth_token=_UpperCamelCase   )

				# set weights for wav2vec2 encoder
				snake_case__   :Optional[int]       = WavaVecaModel(_UpperCamelCase   )

				recursively_load_weights_wavaveca(model.encoder						,						_UpperCamelCase   )

				# load decoder weights
				snake_case__   :Dict       = MBartForCausalLM(_UpperCamelCase   )
				snake_case__     ,    snake_case__   :int       = 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}'   )

				snake_case__   :Dict       = SpeechEncoderDecoderModel(encoder=_UpperCamelCase						,						decoder=_UpperCamelCase   )
				snake_case__   :Union[str, Any]       = False

				snake_case__   :Dict       = MBartaaTokenizer(_UpperCamelCase   )
				tokenizer.save_pretrained(_UpperCamelCase   )

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

				snake_case__   :List[Any]       = tokenizer.eos_token_id
				snake_case__   :Dict       = 25_00_04
				snake_case__   :Union[str, Any]       = tokenizer.eos_token_id

				snake_case__   :Optional[Any]       = SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase   )

				hf_wavavec.save_pretrained(_UpperCamelCase   )
				feature_extractor.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
	__UpperCAmelCase				: str						    =	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_0_2_4, type=int, help="encoder output dim")
	parser.add_argument("--start_token_id", default=2_5_0_0_0_4, type=int, help="`decoder_start_token_id` of model config")

	__UpperCAmelCase				: 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


