






"""simple docstring"""




import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

lowerCamelCase__			=   {
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
lowerCamelCase__			=   [
    "lm_head",
]






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	int		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  for attribute in key.split("""."""    ):
    A__											=							getattr(UpperCamelCase		,				UpperCamelCase    )

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

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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Any    ):
  A__											=							[]
  A__											=							fairseq_model.state_dict()

  if not is_headless:
    A__											=							hf_model.dataavec_audio.feature_extractor
    A__											=							hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    A__											=							hf_model.feature_extractor
    A__											=							hf_model.encoder.pos_conv_embed

  for name, value in fairseq_dict.items():
    A__											=							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				)
      A__											=							True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				)
      A__											=							True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          A__											=							"""data2vec_audio.""" + 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__											=							True
          if "*" in mapped_key:
            A__											=							name.split(UpperCamelCase    )[0].split("""."""    )[-2]
            A__											=							mapped_key.replace("""*"""		,				UpperCamelCase    )
          if "weight_g" in name:
            A__											=							"""weight_g"""
          elif "weight_v" in name:
            A__											=							"""weight_v"""
          elif "bias" in name:
            A__											=							"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            A__											=							"""weight"""
          else:
            A__											=							None
          set_recursively(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase    )
        continue
    if not is_used:
      unused_weights.append(UpperCamelCase    )

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






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[Any]    ):
  A__											=							path.split("""."""    )
  return reduce(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]		,				UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	List[str]    ):
  A__											=							access_by_string(UpperCamelCase		,				UpperCamelCase    )
  A__											=							hf_weight.data

  if fsq_value.shape != hf_value.shape:
    raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""    )
  A__											=							fsq_value
  logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	str    ):
  A__											=							full_name.split("""conv_layers."""    )[-1]
  A__											=							name.split("""."""    )
  A__											=							int(items[0]    )
  A__											=							int(items[1]    )

  A__											=							name.split("""."""    )[-1]
  if type_id == 0:
    A__											=							"""conv"""
  elif type_id == 2:
    A__											=							"""layer_norm"""
  else:
    unused_weights.append(UpperCamelCase    )
    return

  set_weights(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	str		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Tuple    ):
  A__											=							full_name.split("""pos_conv."""    )[-1]
  A__											=							name.split("""."""    )
  A__											=							int(items[0]    )
  A__											=							int(items[1]    )

  A__											=							name.split("""."""    )[-1]
  if type_id != 0:
    unused_weights.append(UpperCamelCase    )
    return
  else:
    A__											=							"""conv"""

  set_weights(UpperCamelCase		,				UpperCamelCase		,				UpperCamelCase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""    )






@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	str		,				UpperCamelCase     :	int=None		,				UpperCamelCase     :	List[str]=None		,				UpperCamelCase     :	str=True    ):
  if config_path is not None:
    A__											=							DataaVecAudioConfig.from_pretrained(UpperCamelCase    )
  else:
    A__											=							DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    A__											=							DataaVecAudioModel(UpperCamelCase    )
    A__											=							os.path.dirname(UpperCamelCase    )

    A__											=							torch.load(UpperCamelCase    )
    A__											=							state_dict["""model"""].pop("""final_proj.0.weight"""    )
    A__											=							state_dict["""model"""].pop("""final_proj.0.bias"""    )
    A__											=							os.path.join(UpperCamelCase		,				"""converted.pt"""    )
    torch.save(UpperCamelCase		,				UpperCamelCase    )
  else:
    A__											=							DataaVecAudioForCTC(UpperCamelCase    )
    A__											=							checkpoint_path

  def load_dataavec(UpperCamelCase     :	List[str]    ):
    A__						,A__						,A__											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
    return model[0].eval()

  A__											=							load_dataavec(UpperCamelCase    )

  recursively_load_weights(UpperCamelCase		,				UpperCamelCase		,				not is_finetuned    )

  A__											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""    )

  A__											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""		,				"""clean"""		,				split="""validation"""    )
  A__											=							[x["""array"""] for x in ds[:4]["""audio"""]]

  A__											=							processor(UpperCamelCase		,				return_tensors="""pt"""		,				padding=UpperCamelCase    )

  A__											=							inputs.input_values
  A__											=							inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    A__											=							model(source=UpperCamelCase		,				padding_mask=(1 - attention_mask)		,				mask=UpperCamelCase		,				features_only=UpperCamelCase    )[
        """encoder_out"""
    ].transpose(0		,				1    )
    A__											=							hf_wavavec(UpperCamelCase		,				attention_mask=UpperCamelCase    )["""logits"""]

    A__											=							torch.argmax(UpperCamelCase		,				dim=-1    )
    A__											=							processor.batch_decode(UpperCamelCase    )

    print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""    )
  else:
    A__											=							model(source=UpperCamelCase		,				padding_mask=(1 - attention_mask)		,				mask=UpperCamelCase		,				features_only=UpperCamelCase    )[
        """layer_results"""
    ][-1][0].transpose(0		,				1    )
    A__											=							hf_wavavec(UpperCamelCase		,				attention_mask=UpperCamelCase    )["""last_hidden_state"""]

  print(our_output.shape		,				their_output.shape    )
  A__											=							torch.max(torch.abs(our_output - their_output    )    ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
  A__											=							torch.allclose(UpperCamelCase		,				UpperCamelCase		,				atol=1e-3    )
  print("""Do both models output the same tensors?"""		,				"""🔥""" if success else """💩"""    )
  if not success:
    raise Exception("""Something went wRoNg"""    )

  hf_wavavec.save_pretrained(UpperCamelCase    )

  if is_finetuned:
    processor.save_pretrained(UpperCamelCase    )
  else:
    processor.feature_extractor.save_pretrained(UpperCamelCase    )


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_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"
      )
      lowerCamelCase__			=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )












"""simple docstring"""




def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int = 1000    ):
  if (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (digit_position <= 0):
    raise ValueError("""Digit position must be a positive integer"""    )
  elif (not isinstance(UpperCamelCase		,				UpperCamelCase    )) or (precision < 0):
    raise ValueError("""Precision must be a nonnegative integer"""    )

  # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
  # accurate
  A__											=							(
      4 * _subsum(UpperCamelCase		,				1		,				UpperCamelCase    )
      - 2 * _subsum(UpperCamelCase		,				4		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				5		,				UpperCamelCase    )
      - _subsum(UpperCamelCase		,				6		,				UpperCamelCase    )
  )

  # return the first hex digit of the fractional part of the result
  return hex(int((sum_result % 1) * 16    )    )[2:]






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	int		,				UpperCamelCase     :	int		,				UpperCamelCase     :	int    ):
  A__											=							0.0
  for sum_index in range(digit_pos_to_extract + precision    ):
    A__											=							8 * sum_index + denominator_addend
    if sum_index < digit_pos_to_extract:
      # if the exponential term is an integer and we mod it by the denominator
      # before dividing, only the integer part of the sum will change;
      # the fractional part will not
      A__											=							pow(
          16		,				digit_pos_to_extract - 1 - sum_index		,				UpperCamelCase    )
    else:
      A__											=							pow(16		,				digit_pos_to_extract - 1 - sum_index    )
    total += exponential_term / denominator
  return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()




