


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
snake_case__			 =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	Union[str, Any]:





   """simple docstring"""
   a__ :Optional[int]						  =    {}
   with open(__UpperCAmelCase	,  "r"				) as file:
      for line_number, line in enumerate(__UpperCAmelCase				):
         a__ :Tuple						  =    line.strip()
         if line:
            a__ :Dict						  =    line.split()
            a__ :Optional[int]						  =    line_number
            a__ :Any						  =    words[0]
            a__ :Optional[int]						  =    value
   return result


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple	,  a	:   int	,  a	:   Tuple	,  a	:   Optional[Any]				)    ->	Optional[Any]:





   """simple docstring"""







   for attribute in key.split("."				):
      a__ :List[Any]						  =    getattr(__UpperCAmelCase	,  __UpperCAmelCase				)

   a__ :int						  =    None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__UpperCAmelCase				):
         a__ :str						  =    PARAM_MAPPING[full_name.split("."				)[-1]]
         a__ :List[Any]						  =    "param"

   if weight_type is not None and weight_type != "param":
      a__ :Optional[int]						  =    getattr(__UpperCAmelCase	,  __UpperCAmelCase				).shape
   elif weight_type is not None and weight_type == "param":
      a__ :Union[str, Any]						  =    hf_pointer
      for attribute in hf_param_name.split("."				):
         a__ :Dict						  =    getattr(__UpperCAmelCase	,  __UpperCAmelCase				)
      a__ :Tuple						  =    shape_pointer.shape

      # let's reduce dimension
      a__ :List[str]						  =    value[0]
   else:
      a__ :Optional[Any]						  =    hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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__ :Dict						  =    value
   elif weight_type == "weight_g":
      a__ :str						  =    value
   elif weight_type == "weight_v":
      a__ :List[str]						  =    value
   elif weight_type == "bias":
      a__ :Dict						  =    value
   elif weight_type == "param":
      for attribute in hf_param_name.split("."				):
         a__ :str						  =    getattr(__UpperCAmelCase	,  __UpperCAmelCase				)
      a__ :Tuple						  =    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 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   Any	,  a	:   Optional[Any]	,  a	:   Union[str, Any]	,  a	:   int				)    ->	str:





   """simple docstring"""
   a__ :int						  =    None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__UpperCAmelCase				):
         a__ :List[Any]						  =    PARAM_MAPPING[full_name.split("."				)[-1]]
         a__ :int						  =    "param"

   if weight_type is not None and weight_type != "param":
      a__ :Tuple						  =    ".".join([key, weight_type]				)
   elif weight_type is not None and weight_type == "param":
      a__ :Union[str, Any]						  =    ".".join([key, hf_param_name]				)
   else:
      a__ :Optional[Any]						  =    key

   a__ :Optional[int]						  =    value if "lm_head" in full_key else value[0]


snake_case__			 =						{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}


def 				lowerCamelCase__	(      a	:   int	,  a	:   List[str]	,  a	:   Tuple=None	,  a	:   Union[str, Any]=None				)    ->	str:





   """simple docstring"""
   a__ :Optional[int]						  =    False
   for key, mapped_key in MAPPING.items():
      a__ :int						  =    "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
      if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
         a__ :int						  =    True
         if "*" in mapped_key:
            a__ :Any						  =    name.split(__UpperCAmelCase				)[0].split("."				)[-2]
            a__ :Tuple						  =    mapped_key.replace("*"	,  __UpperCAmelCase				)
         if "weight_g" in name:
            a__ :List[str]						  =    "weight_g"
         elif "weight_v" in name:
            a__ :Any						  =    "weight_v"
         elif "bias" in name:
            a__ :List[Any]						  =    "bias"
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a__ :List[Any]						  =    "weight"
         else:
            a__ :Dict						  =    None
         if hf_dict is not None:
            rename_dict(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase				)
         else:
            set_recursively(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase				)
         return is_used
   return is_used


def 				lowerCamelCase__	(      a	:   Dict	,  a	:   List[str]	,  a	:   Optional[Any]				)    ->	Optional[int]:





   """simple docstring"""
   a__ :Union[str, Any]						  =    []
   a__ :Any						  =    fairseq_model.state_dict()

   a__ :Dict						  =    hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      a__ :Union[str, Any]						  =    False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  hf_model.config.feat_extract_norm == "group"	,  )
         a__ :Optional[Any]						  =    True
      else:
         a__ :List[str]						  =    load_wavaveca_layer(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase				)
      if not is_used:
         unused_weights.append(__UpperCAmelCase				)

   logger.warning(F'''Unused weights: {unused_weights}'''				)


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   List[Any]	,  a	:   Any	,  a	:   Union[str, Any]	,  a	:   Optional[Any]				)    ->	Tuple:





   """simple docstring"""
   a__ :Optional[int]						  =    full_name.split("conv_layers."				)[-1]
   a__ :Any						  =    name.split("."				)
   a__ :Any						  =    int(items[0]				)
   a__ :Optional[int]						  =    int(items[1]				)

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
         a__ :Dict						  =    value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
         a__ :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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                F'''{full_name} has size {value.shape}, but'''
                F''' {feature_extractor.conv_layers[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(__UpperCAmelCase				)


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   List[Any]	,  a	:   Dict=None	,  a	:   Union[str, Any]=None	,  a	:   str=True	,  a	:   Optional[Any]=False				)    ->	List[Any]:





   """simple docstring"""







   if config_path is not None:
      a__ :int						  =    WavaVecaConfig.from_pretrained(__UpperCAmelCase				)
   else:
      a__ :Optional[int]						  =    WavaVecaConfig()

   if is_seq_class:
      a__ :Dict						  =    read_txt_into_dict(__UpperCAmelCase				)
      a__ :Optional[int]						  =    idalabel
      a__ :int						  =    WavaVecaForSequenceClassification(__UpperCAmelCase				)
      a__ :int						  =    WavaVecaFeatureExtractor(
          feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=__UpperCAmelCase	,  return_attention_mask=__UpperCAmelCase	,  )
      feature_extractor.save_pretrained(__UpperCAmelCase				)

   elif is_finetuned:
      if dict_path:
         a__ :Optional[int]						  =    Dictionary.load(__UpperCAmelCase				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         a__ :Dict						  =    target_dict.pad_index
         a__ :Tuple						  =    target_dict.bos_index
         a__ :List[Any]						  =    target_dict.eos_index
         a__ :List[str]						  =    len(target_dict.symbols				)
         a__ :Dict						  =    os.path.join(__UpperCAmelCase	,  "vocab.json"				)
         if not os.path.isdir(__UpperCAmelCase				):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__UpperCAmelCase				)				)
            return
         os.makedirs(__UpperCAmelCase	,  exist_ok=__UpperCAmelCase				)
         a__ :Dict						  =    target_dict.indices

         # fairseq has the <pad> and <s> switched
         a__ :int						  =    0
         a__ :int						  =    1
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"				) as vocab_handle:
            json.dump(__UpperCAmelCase	,  __UpperCAmelCase				)
         a__ :int						  =    WavaVecaCTCTokenizer(
             __UpperCAmelCase	,  unk_token=target_dict.unk_word	,  pad_token=target_dict.pad_word	,  bos_token=target_dict.bos_word	,  eos_token=target_dict.eos_word	,  word_delimiter_token="|"	,  do_lower_case=__UpperCAmelCase	,  )
         a__ :Union[str, Any]						  =    True if config.feat_extract_norm == "layer" else False
         a__ :List[Any]						  =    WavaVecaFeatureExtractor(
             feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=__UpperCAmelCase	,  return_attention_mask=__UpperCAmelCase	,  )
         a__ :Any						  =    WavaVecaProcessor(feature_extractor=__UpperCAmelCase	,  tokenizer=__UpperCAmelCase				)
         processor.save_pretrained(__UpperCAmelCase				)

      a__ :str						  =    WavaVecaForCTC(__UpperCAmelCase				)
   else:
      a__ :Optional[int]						  =    WavaVecaForPreTraining(__UpperCAmelCase				)

   if is_finetuned or is_seq_class:
      a__  ,  a__  ,  a__ :Any						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,  arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
   else:
      a__ :List[Any]						  =    argparse.Namespace(task="audio_pretraining"				)
      a__ :Union[str, Any]						  =    fairseq.tasks.setup_task(__UpperCAmelCase				)

      a__  ,  a__  ,  a__ :Tuple						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=__UpperCAmelCase				)

   a__ :int						  =    model[0].eval()

   recursively_load_weights(__UpperCAmelCase	,  __UpperCAmelCase	,  not is_finetuned				)

   hf_wavavec.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  snake_case__			 =						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_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  parser.add_argument(
      '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
  )
  parser.add_argument(
      '''--is_seq_class''',
      action='''store_true''',
      help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
  )
  snake_case__			 =						parser.parse_args()

  snake_case__			 =						not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'pegasus'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Any   ,     __A						:   Dict=50265   ,     __A						:   List[Any]=1024   ,     __A						:   int=12   ,     __A						:   Optional[Any]=4096   ,     __A						:   Optional[int]=16   ,     __A						:   Dict=12   ,     __A						:   List[Any]=4096   ,     __A						:   List[str]=16   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=True   ,     __A						:   str="gelu"   ,     __A						:   Tuple=1024   ,     __A						:   Any=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=0.0   ,     __A						:   Tuple=0.02   ,     __A						:   Union[str, Any]=0   ,     __A						:   Union[str, Any]=False   ,     __A						:   Optional[Any]=0   ,     __A						:   Tuple=1   ,     __A						:   str=1   ,     **__A						:   Any   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    vocab_size
									a__ :List[str]						  =    max_position_embeddings
									a__ :int						  =    d_model
									a__ :Union[str, Any]						  =    encoder_ffn_dim
									a__ :List[Any]						  =    encoder_layers
									a__ :Union[str, Any]						  =    encoder_attention_heads
									a__ :Tuple						  =    decoder_ffn_dim
									a__ :List[Any]						  =    decoder_layers
									a__ :Tuple						  =    decoder_attention_heads
									a__ :Optional[int]						  =    dropout
									a__ :str						  =    attention_dropout
									a__ :Optional[int]						  =    activation_dropout
									a__ :str						  =    activation_function
									a__ :Dict						  =    init_std
									a__ :Any						  =    encoder_layerdrop
									a__ :int						  =    decoder_layerdrop
									a__ :Union[str, Any]						  =    use_cache
									a__ :List[Any]						  =    encoder_layers
									a__ :Dict						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A   ,     eos_token_id=__A   ,     is_encoder_decoder=__A   ,     decoder_start_token_id=__A   ,     forced_eos_token_id=__A   ,     **__A   ,     )




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									return self.encoder_attention_heads






						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return self.d_model


