


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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for attribute in key.split('.'						):
  __lowerCamelCase		:							Union[str, Any]		 =						getattr(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

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

 logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						[]
 __lowerCamelCase		:							Union[str, Any]		 =						fairseq_model.state_dict()

 __lowerCamelCase		:							Union[str, Any]		 =						hf_model.feature_extractor
 __lowerCamelCase		:							Optional[Any]		 =						hf_model.adapter

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

 if items[1].isdigit():
  __lowerCamelCase		:							List[str]		 =						int(items[1]						)
 else:
  __lowerCamelCase		:							List[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.'
    __lowerCamelCase		:							Tuple		 =						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.'
    __lowerCamelCase		:							Dict		 =						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.'
    __lowerCamelCase		:							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.'
    __lowerCamelCase		:							Tuple		 =						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.'
   __lowerCamelCase		:							List[str]		 =						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.'
   __lowerCamelCase		:							Dict		 =						value
   logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						emb.weight.shape
 __lowerCamelCase		:							Optional[int]		 =						nn.Linear(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	bias=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]		 =						emb.weight.data
 return lin_layer
@torch.no_grad()
def 				UpperCamelCase__					(  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__      ,	):
 __lowerCamelCase		:							Optional[Any]		 =						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__      ,	)
 __lowerCamelCase		:							Dict		 =						MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)

 # load model
 __lowerCamelCase		:							List[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,
     }      ,	)
 __lowerCamelCase		:							int		 =						model[0].eval()

 # load feature extractor
 __lowerCamelCase		:							str		 =						WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__      ,	use_auth_token=SCREAMING_SNAKE_CASE__						)

 # set weights for wav2vec2 encoder
 __lowerCamelCase		:							str		 =						WavaVecaModel(SCREAMING_SNAKE_CASE__						)

 recursively_load_weights_wavaveca(model.encoder      ,	SCREAMING_SNAKE_CASE__						)

 # load decoder weights
 __lowerCamelCase		:							Union[str, Any]		 =						MBartForCausalLM(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str		 =						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}'						)

 __lowerCamelCase		:							Optional[int]		 =						SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__      ,	decoder=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Tuple		 =						False

 __lowerCamelCase		:							List[str]		 =						MBartaaTokenizer(SCREAMING_SNAKE_CASE__						)
 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__						)

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

 __lowerCamelCase		:							int		 =						tokenizer.eos_token_id
 __lowerCamelCase		:							Tuple		 =						250_004
 __lowerCamelCase		:							Union[str, Any]		 =						tokenizer.eos_token_id

 __lowerCamelCase		:							Union[str, 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=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')

       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,
       )





from math import pow
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	):
 if current_sum == needed_sum:
  # If the sum of the powers is equal to needed_sum, then we have a solution.
  solutions_count += 1
  return current_sum, solutions_count

 __lowerCamelCase		:							Optional[Any]		 =						int(pow(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
 if current_sum + i_to_n <= needed_sum:
  # If the sum of the powers is less than needed_sum, then continue adding powers.
  current_sum += i_to_n
  __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  current_sum -= i_to_n
 if i_to_n < needed_sum:
  # If the power of i is less than needed_sum, then try with the next power.
  __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return current_sum, solutions_count
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
  raise ValueError(
      'Invalid input\n'
      'needed_sum must be between 1 and 1000, power between 2 and 10.'						)

 return backtrack(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1      ,	0      ,	0						)[1]  # Return the solutions_count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

