"""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()
lowercase__					       =	logging.get_logger(__name__)

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[Any]:
  for attribute in key.split('.'   ):
    a__:     Optional[Any]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  if weight_type is not None:
    a__:     List[str]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).shape
  else:
    a__:     Optional[Any]										=	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__:     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__:     List[str]										=	value

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[str]										=	[]
  a__:     Tuple										=	fairseq_model.state_dict()

  a__:     int										=	hf_model.feature_extractor
  a__:     str										=	hf_model.adapter

  for name, value in fairseq_dict.items():
    a__:     List[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'		,     )
      a__:     str										=	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   )
      a__:     Dict										=	True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
          a__:     Dict										=	True
          if "*" in mapped_key:
            a__:     str										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
            a__:     List[Any]										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
          if "weight_g" in name:
            a__:     str										=	'weight_g'
          elif "weight_v" in name:
            a__:     str										=	'weight_v'
          elif "bias" in name:
            a__:     Dict										=	'bias'
          elif "weight" in name:
            a__:     List[str]										=	'weight'
          else:
            a__:     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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__:     List[str]										=	full_name.split('conv_layers.'   )[-1]
  a__:     Tuple										=	name.split('.'   )
  a__:     Optional[int]										=	int(items[0]   )
  a__:     Optional[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.'
      )
      a__:     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.'
      )
      a__:     Optional[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."
      )
      a__:     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.'
      )
      a__:     List[str]										=	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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  a__:     Optional[Any]										=	full_name.split('adaptor.'   )[-1]
  a__:     List[str]										=	name.split('.'   )

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







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







@torch.no_grad()
def 						__a   (	_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		,     )	->int:
  a__:     Optional[int]										=	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		,     )
  a__:     List[Any]										=	MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )

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

  # load feature extractor
  a__:     Any										=	WavaVecaFeatureExtractor.from_pretrained(_SCREAMING_SNAKE_CASE		,     use_auth_token=_SCREAMING_SNAKE_CASE   )

  # set weights for wav2vec2 encoder
  a__:     Dict										=	WavaVecaModel(_SCREAMING_SNAKE_CASE   )

  recursively_load_weights_wavaveca(model.encoder		,     _SCREAMING_SNAKE_CASE   )

  # load decoder weights
  a__:     Optional[int]										=	MBartForCausalLM(_SCREAMING_SNAKE_CASE   )
  a__							,    a__:     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}'   )

  a__:     Dict										=	SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE		,     decoder=_SCREAMING_SNAKE_CASE   )
  a__:     List[Any]										=	False

  a__:     str										=	MBartaaTokenizer(_SCREAMING_SNAKE_CASE   )
  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE   )

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

  a__:     Dict										=	tokenizer.eos_token_id
  a__:     Tuple										=	250004
  a__:     Any										=	tokenizer.eos_token_id

  a__:     List[Any]										=	SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE   )

  hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE   )
  feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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')

 lowercase__					       =	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"""
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





        if index is not None and index.is_initialized() and len(lowercase) > 0:
          raise ValueError(
              'When using Ray for distributed fine-tuning, '
              'you\'ll need to provide the paths instead, '
              'as the dataset and the index are loaded '
              'separately. More info in examples/rag/use_own_knowledge_dataset.py ')
        super().__init__(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
        a__:     Any										=	retrieval_workers
        if len(self.retrieval_workers) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)
                  for worker in self.retrieval_workers
              ])



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        logger.info('initializing retrieval')

        if len(self.retrieval_workers) > 0:
          ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
        else:
          # Non-distributed training. Load index into this same process.
          self.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        if len(self.retrieval_workers) > 0:
          # Select a random retrieval actor.
          a__:     int										=	self.retrieval_workers[random.randint(0    ,			len(self.retrieval_workers) - 1)]
          a__							,    a__:     List[Any]										=	ray.get(random_worker.retrieve.remote(lowercase    ,			lowercase))
        else:
          a__							,    a__:     Dict										=	self._main_retrieve(lowercase    ,			lowercase)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        return super(lowercase    ,			cls).get_tokenizers(lowercase    ,			lowercase    ,			**lowercase)



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	kwargs.pop('config'    ,			lowercase) or RagConfig.from_pretrained(lowercase    ,			**lowercase)
        a__:     Union[str, Any]										=	RagTokenizer.from_pretrained(lowercase    ,			config=lowercase)
        a__:     int										=	rag_tokenizer.question_encoder
        a__:     Any										=	rag_tokenizer.generator
        if indexed_dataset is not None:
          a__:     List[Any]										=	'custom'
          a__:     Optional[Any]										=	CustomHFIndex(config.retrieval_vector_size    ,			lowercase)
        else:
          a__:     Dict										=	cls._build_index(lowercase)
        return cls(
            lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			retrieval_workers=lowercase    ,			index=lowercase    ,			)


