


'''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()
a_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[int]						= {
    """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""",
}
a_  :				List[str]						= [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]





def __snake_case					(      UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  {}
 with open(SCREAMING_SNAKE_CASE_					,				"r"		) as file:
  for line_number, line in enumerate(SCREAMING_SNAKE_CASE_		):
   lowerCamelCase_      =  line.strip()
   if line:
    lowerCamelCase_      =  line.split()
    lowerCamelCase_      =  line_number
    lowerCamelCase_      =  words[0]
    lowerCamelCase_      =  value
 return result





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):

 for attribute in key.split("."		):
  lowerCamelCase_      =  getattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)

 lowerCamelCase_      =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE_		):
   lowerCamelCase_      =  PARAM_MAPPING[full_name.split("."		)[-1]]
   lowerCamelCase_      =  "param"

 if weight_type is not None and weight_type != "param":
  lowerCamelCase_      =  getattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		).shape
 elif weight_type is not None and weight_type == "param":
  lowerCamelCase_      =  hf_pointer
  for attribute in hf_param_name.split("."		):
   lowerCamelCase_      =  getattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_      =  shape_pointer.shape

  # let's reduce dimension
  lowerCamelCase_      =  value[0]
 else:
  lowerCamelCase_      =  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":
  lowerCamelCase_      =  value
 elif weight_type == "weight_g":
  lowerCamelCase_      =  value
 elif weight_type == "weight_v":
  lowerCamelCase_      =  value
 elif weight_type == "bias":
  lowerCamelCase_      =  value
 elif weight_type == "param":
  for attribute in hf_param_name.split("."		):
   lowerCamelCase_      =  getattr(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_      =  value
 else:
  lowerCamelCase_      =  value

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





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Dict		):
 lowerCamelCase_      =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE_		):
   lowerCamelCase_      =  PARAM_MAPPING[full_name.split("."		)[-1]]
   lowerCamelCase_      =  "param"

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

 lowerCamelCase_      =  value if "lm_head" in full_key else value[0]


a_  :				List[str]						= {
    """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 __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Tuple=None					,				UpperCAmelCase_						:						Optional[int]=None		):
 lowerCamelCase_      =  False
 for key, mapped_key in MAPPING.items():
  lowerCamelCase_      =  "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]:
   lowerCamelCase_      =  True
   if "*" in mapped_key:
    lowerCamelCase_      =  name.split(SCREAMING_SNAKE_CASE_		)[0].split("."		)[-2]
    lowerCamelCase_      =  mapped_key.replace("*"					,				SCREAMING_SNAKE_CASE_		)
   if "weight_g" in name:
    lowerCamelCase_      =  "weight_g"
   elif "weight_v" in name:
    lowerCamelCase_      =  "weight_v"
   elif "bias" in name:
    lowerCamelCase_      =  "bias"
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    lowerCamelCase_      =  "weight"
   else:
    lowerCamelCase_      =  None
   if hf_dict is not None:
    rename_dict(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
   else:
    set_recursively(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
   return is_used
 return is_used





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  fairseq_model.state_dict()

 lowerCamelCase_      =  hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase_      =  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_      =  True
  else:
   lowerCamelCase_      =  load_wavaveca_layer(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE_		)

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





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  full_name.split("conv_layers."		)[-1]
 lowerCamelCase_      =  name.split("."		)
 lowerCamelCase_      =  int(items[0]		)
 lowerCamelCase_      =  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.'''		)
   lowerCamelCase_      =  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.'''		)
   lowerCamelCase_      =  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.'''		)
   lowerCamelCase_      =  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.'''		)
   lowerCamelCase_      =  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_		)





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any=None					,				UpperCAmelCase_						:						int=None					,				UpperCAmelCase_						:						List[Any]=True					,				UpperCAmelCase_						:						List[Any]=False		):

 if config_path is not None:
  lowerCamelCase_      =  WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)
 else:
  lowerCamelCase_      =  WavaVecaConfig()

 if is_seq_class:
  lowerCamelCase_      =  read_txt_into_dict(SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_      =  WavaVecaFeatureExtractor(
      feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=SCREAMING_SNAKE_CASE_					,				return_attention_mask=SCREAMING_SNAKE_CASE_					,				)
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_		)

 elif is_finetuned:
  if dict_path:
   lowerCamelCase_      =  Dictionary.load(SCREAMING_SNAKE_CASE_		)

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

   # fairseq has the <pad> and <s> switched
   lowerCamelCase_      =  0
   lowerCamelCase_      =  1
   with open(SCREAMING_SNAKE_CASE_					,				"w"					,				encoding="utf-8"		) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_		)
   lowerCamelCase_      =  WavaVecaCTCTokenizer(
       SCREAMING_SNAKE_CASE_					,				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=SCREAMING_SNAKE_CASE_					,				)
   lowerCamelCase_      =  True if config.feat_extract_norm == "layer" else False
   lowerCamelCase_      =  WavaVecaFeatureExtractor(
       feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=SCREAMING_SNAKE_CASE_					,				return_attention_mask=SCREAMING_SNAKE_CASE_					,				)
   lowerCamelCase_      =  WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_					,				tokenizer=SCREAMING_SNAKE_CASE_		)
   processor.save_pretrained(SCREAMING_SNAKE_CASE_		)

  lowerCamelCase_      =  WavaVecaForCTC(SCREAMING_SNAKE_CASE_		)
 else:
  lowerCamelCase_      =  WavaVecaForPreTraining(SCREAMING_SNAKE_CASE_		)

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

  lowerCamelCase_      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,				task=SCREAMING_SNAKE_CASE_		)

 lowerCamelCase_      =  model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				not is_finetuned		)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
     a_  :				Optional[int]						= 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""",
     )
     a_  :				Optional[int]						= parser.parse_args()

     a_  :				Optional[int]						= 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,
     )







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



