






"""simple docstring"""




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__     (     UpperCamelCase__				):



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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for attribute in key.split("""."""				):
    _a  :       List[str]             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				)

  _a  :       Optional[Any]             =	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase__				):
      _a  :       Tuple             =	PARAM_MAPPING[full_name.split("""."""				)[-1]]
      _a  :       Optional[Any]             =	"""param"""

  if weight_type is not None and weight_type != "param":
    _a  :       List[str]             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				).shape
  elif weight_type is not None and weight_type == "param":
    _a  :       Any             =	hf_pointer
    for attribute in hf_param_name.split("""."""				):
      _a  :       str             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				)
    _a  :       Optional[Any]             =	shape_pointer.shape

    # let's reduce dimension
    _a  :       Dict             =	value[0]
  else:
    _a  :       Dict             =	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  :       Optional[int]             =	value
  elif weight_type == "weight_g":
    _a  :       Any             =	value
  elif weight_type == "weight_v":
    _a  :       Tuple             =	value
  elif weight_type == "bias":
    _a  :       List[Any]             =	value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""				):
      _a  :       Tuple             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				)
    _a  :       Optional[Any]             =	value
  else:
    _a  :       str             =	value

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase__				):
      _a  :       int             =	PARAM_MAPPING[full_name.split("""."""				)[-1]]
      _a  :       Union[str, Any]             =	"""param"""

  if weight_type is not None and weight_type != "param":
    _a  :       List[str]             =	""".""".join([key, weight_type]				)
  elif weight_type is not None and weight_type == "param":
    _a  :       str             =	""".""".join([key, hf_param_name]				)
  else:
    _a  :       Dict             =	key

  _a  :       Any             =	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__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	False
  for key, mapped_key in MAPPING.items():
    _a  :       Optional[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  :       Any             =	True
      if "*" in mapped_key:
        _a  :       int             =	name.split(UpperCamelCase__				)[0].split("""."""				)[-2]
        _a  :       List[Any]             =	mapped_key.replace("""*"""     ,   UpperCamelCase__				)
      if "weight_g" in name:
        _a  :       Optional[int]             =	"""weight_g"""
      elif "weight_v" in name:
        _a  :       Any             =	"""weight_v"""
      elif "bias" in name:
        _a  :       int             =	"""bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _a  :       Dict             =	"""weight"""
      else:
        _a  :       Any             =	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__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[str]             =	[]
  _a  :       int             =	fairseq_model.state_dict()

  _a  :       int             =	hf_model.wavaveca.feature_extractor

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

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	full_name.split("""conv_layers."""				)[-1]
  _a  :       Tuple             =	name.split("""."""				)
  _a  :       List[str]             =	int(items[0]				)
  _a  :       Any             =	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  :       int             =	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  :       List[str]             =	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  :       Dict             =	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__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=True     ,   UpperCamelCase__=False				):



  '''simple docstring'''
  if config_path is not None:
    _a  :       Union[str, Any]             =	WavaVecaConfig.from_pretrained(UpperCamelCase__				)
  else:
    _a  :       Union[str, Any]             =	WavaVecaConfig()

  if is_seq_class:
    _a  :       List[Any]             =	read_txt_into_dict(UpperCamelCase__				)
    _a  :       Optional[int]             =	idalabel
    _a  :       int             =	WavaVecaForSequenceClassification(UpperCamelCase__				)
    _a  :       int             =	WavaVecaFeatureExtractor(
        feature_size=1     ,   sampling_rate=1_6_0_0_0     ,   padding_value=0     ,   do_normalize=UpperCamelCase__     ,   return_attention_mask=UpperCamelCase__     ,   )
    feature_extractor.save_pretrained(UpperCamelCase__				)

  elif is_finetuned:
    if dict_path:
      _a  :       Dict             =	Dictionary.load(UpperCamelCase__				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _a  :       List[str]             =	target_dict.pad_index
      _a  :       Dict             =	target_dict.bos_index
      _a  :       Optional[Any]             =	target_dict.eos_index
      _a  :       int             =	len(target_dict.symbols				)
      _a  :       Any             =	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  :       Tuple             =	target_dict.indices

      # fairseq has the <pad> and <s> switched
      _a  :       List[Any]             =	0
      _a  :       Dict             =	1
      with open(UpperCamelCase__     ,   """w"""     ,   encoding="""utf-8"""				) as vocab_handle:
        json.dump(UpperCamelCase__     ,   UpperCamelCase__				)
      _a  :       Optional[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  :       Optional[int]             =	True if config.feat_extract_norm == """layer""" else False
      _a  :       Union[str, Any]             =	WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=1_6_0_0_0     ,   padding_value=0     ,   do_normalize=UpperCamelCase__     ,   return_attention_mask=UpperCamelCase__     ,   )
      _a  :       Any             =	WavaVecaProcessor(feature_extractor=UpperCamelCase__     ,   tokenizer=UpperCamelCase__				)
      processor.save_pretrained(UpperCamelCase__				)

    _a  :       List[str]             =	WavaVecaForCTC(UpperCamelCase__				)
  else:
    _a  :       Any             =	WavaVecaForPreTraining(UpperCamelCase__				)

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

    _a      ,  _a      ,  _a  :       Any             =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,   task=UpperCamelCase__				)

  _a  :       List[Any]             =	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,
 )









"""simple docstring"""




import warnings

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

