




'''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()
lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    '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',
}
lowerCamelCase_			=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			List[Any]	,					__A    :			List[str]	,					__A    :			Dict	,					__A    :			Dict  )			->			Tuple:
   for attribute in key.split("."  ):
      _SCREAMING_SNAKE_CASE	=				getattr(__A	,					__A  )

   if weight_type is not None:
      _SCREAMING_SNAKE_CASE	=				getattr(__A	,					__A  ).shape
   else:
      _SCREAMING_SNAKE_CASE	=				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":
      _SCREAMING_SNAKE_CASE	=				value
   elif weight_type == "weight_g":
      _SCREAMING_SNAKE_CASE	=				value
   elif weight_type == "weight_v":
      _SCREAMING_SNAKE_CASE	=				value
   elif weight_type == "bias":
      _SCREAMING_SNAKE_CASE	=				value
   else:
      _SCREAMING_SNAKE_CASE	=				value

   logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			List[Any]  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				[]
   _SCREAMING_SNAKE_CASE	=				fairseq_model.state_dict()

   _SCREAMING_SNAKE_CASE	=				hf_model.feature_extractor
   _SCREAMING_SNAKE_CASE	=				hf_model.adapter

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

   logger.warning(f"""Unused weights: {unused_weights}"""  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			List[Any]	,					__A    :			str	,					__A    :			List[Any]	,					__A    :			List[Any]  )			->			Union[str, Any]:
   _SCREAMING_SNAKE_CASE	=				full_name.split("conv_layers."  )[-1]
   _SCREAMING_SNAKE_CASE	=				name.split("."  )
   _SCREAMING_SNAKE_CASE	=				int(items[0]  )
   _SCREAMING_SNAKE_CASE	=				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."""
         )
         _SCREAMING_SNAKE_CASE	=				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."""
         )
         _SCREAMING_SNAKE_CASE	=				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."
         )
         _SCREAMING_SNAKE_CASE	=				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."""
         )
         _SCREAMING_SNAKE_CASE	=				value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
   else:
      unused_weights.append(__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			Any	,					__A    :			Optional[Any]	,					__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				full_name.split("adaptor."  )[-1]
   _SCREAMING_SNAKE_CASE	=				name.split("."  )

   if items[1].isdigit():
      _SCREAMING_SNAKE_CASE	=				int(items[1]  )
   else:
      _SCREAMING_SNAKE_CASE	=				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."""
            _SCREAMING_SNAKE_CASE	=				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."""
            _SCREAMING_SNAKE_CASE	=				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."""
            _SCREAMING_SNAKE_CASE	=				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."""
            _SCREAMING_SNAKE_CASE	=				value
            logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
   elif isinstance(__A	,					__A  ):
      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."""
         _SCREAMING_SNAKE_CASE	=				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."""
         _SCREAMING_SNAKE_CASE	=				value
         logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
   else:
      unused_weights.append(__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str  )			->			int:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				emb.weight.shape
   _SCREAMING_SNAKE_CASE	=				nn.Linear(__A	,					__A	,					bias=__A  )
   _SCREAMING_SNAKE_CASE	=				emb.weight.data
   return lin_layer
@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			str	,					__A    :			Union[str, Any]	,					__A    :			Union[str, Any]	,					__A    :			int	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Optional[int]	,					__A    :			List[Any]	,					__A    :			int	,					__A    :			Dict	,					)			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				WavaVecaConfig.from_pretrained(
       __A	,					add_adapter=__A	,					adapter_stride=__A	,					adapter_kernel_size=__A	,					use_auth_token=__A	,					output_hidden_size=__A	,					)
   _SCREAMING_SNAKE_CASE	=				MBartConfig.from_pretrained(__A  )

   # load model
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				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,
       }	,					)
   _SCREAMING_SNAKE_CASE	=				model[0].eval()

   # load feature extractor
   _SCREAMING_SNAKE_CASE	=				WavaVecaFeatureExtractor.from_pretrained(__A	,					use_auth_token=__A  )

   # set weights for wav2vec2 encoder
   _SCREAMING_SNAKE_CASE	=				WavaVecaModel(__A  )

   recursively_load_weights_wavaveca(model.encoder	,					__A  )

   # load decoder weights
   _SCREAMING_SNAKE_CASE	=				MBartForCausalLM(__A  )
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,					strict=__A  )
   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}"""  )

   _SCREAMING_SNAKE_CASE	=				SpeechEncoderDecoderModel(encoder=__A	,					decoder=__A  )
   _SCREAMING_SNAKE_CASE	=				False

   _SCREAMING_SNAKE_CASE	=				MBartaaTokenizer(__A  )
   tokenizer.save_pretrained(__A  )

   _SCREAMING_SNAKE_CASE	=				hf_wavavec.config.to_dict()
   _SCREAMING_SNAKE_CASE	=				tokenizer.pad_token_id
   _SCREAMING_SNAKE_CASE	=				tokenizer.bos_token_id
   _SCREAMING_SNAKE_CASE	=				tokenizer.eos_token_id
   _SCREAMING_SNAKE_CASE	=				"mbart50"
   _SCREAMING_SNAKE_CASE	=				"wav2vec2"

   _SCREAMING_SNAKE_CASE	=				tokenizer.eos_token_id
   _SCREAMING_SNAKE_CASE	=				25_00_04
   _SCREAMING_SNAKE_CASE	=				tokenizer.eos_token_id

   _SCREAMING_SNAKE_CASE	=				SpeechEncoderDecoderConfig.from_dict(__A  )

   hf_wavavec.save_pretrained(__A  )
   feature_extractor.save_pretrained(__A  )


if __name__ == "__main__":
 lowerCamelCase_			=						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=10_24, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

 lowerCamelCase_			=						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'''



lowerCamelCase_			=						'Tobias Carryer'

from time import time







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict=int(time()   )   ):  # noqa: B008

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				multiplier
      _SCREAMING_SNAKE_CASE	=				increment
      _SCREAMING_SNAKE_CASE	=				modulo
      _SCREAMING_SNAKE_CASE	=				seed



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				(self.multiplier * self.seed + self.increment) % self.modulo
      return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 lowerCamelCase_			=						LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
 while True:
  print(lcg.next_number())

