






"""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()
_SCREAMING_SNAKE_CASE :    Dict    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    Optional[int]    =						{
    '''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''',
}
_SCREAMING_SNAKE_CASE :    int    =						[
    '''lm_head''',
]







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Dict		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Optional[Any]			)				->    Any:
  for attribute in key.split('.'			):
    lowerCamelCase_   					=						getattr(_lowerCamelCase		,    _lowerCamelCase			)

  if weight_type is not None:
    lowerCamelCase_   					=						getattr(_lowerCamelCase		,    _lowerCamelCase			).shape
  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
  else:
    lowerCamelCase_   					=						value

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		List[str]			)				->    Dict:
  lowerCamelCase_   					=						[]
  lowerCamelCase_   					=						fairseq_model.state_dict()

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

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

  for name, value in fairseq_dict.items():
    lowerCamelCase_   					=						False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    )
      lowerCamelCase_   					=						True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    )
      lowerCamelCase_   					=						True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          lowerCamelCase_   					=						'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]:
          lowerCamelCase_   					=						True
          if "*" in mapped_key:
            lowerCamelCase_   					=						name.split(_lowerCamelCase			)[0].split('.'			)[-2]
            lowerCamelCase_   					=						mapped_key.replace('*'		,    _lowerCamelCase			)
          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
          set_recursively(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
        continue
    if not is_used:
      unused_weights.append(_lowerCamelCase			)

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Tuple			)				->    Optional[Any]:
  lowerCamelCase_   					=						path.split('.'			)
  return reduce(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		List[Any]			)				->    List[Any]:
  lowerCamelCase_   					=						access_by_string(_lowerCamelCase		,    _lowerCamelCase			)
  lowerCamelCase_   					=						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.'''			)
  lowerCamelCase_   					=						fsq_value
  logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		int		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Optional[Any]			)				->    int:
  lowerCamelCase_   					=						full_name.split('conv_layers.'			)[-1]
  lowerCamelCase_   					=						name.split('.'			)
  lowerCamelCase_   					=						int(items[0]			)
  lowerCamelCase_   					=						int(items[1]			)

  lowerCamelCase_   					=						name.split('.'			)[-1]
  if type_id == 0:
    lowerCamelCase_   					=						'conv'
  elif type_id == 2:
    lowerCamelCase_   					=						'layer_norm'
  else:
    unused_weights.append(_lowerCamelCase			)
    return

  set_weights(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict			)				->    Dict:
  lowerCamelCase_   					=						full_name.split('pos_conv.'			)[-1]
  lowerCamelCase_   					=						name.split('.'			)
  lowerCamelCase_   					=						int(items[0]			)
  lowerCamelCase_   					=						int(items[1]			)

  lowerCamelCase_   					=						name.split('.'			)[-1]
  if type_id != 0:
    unused_weights.append(_lowerCamelCase			)
    return
  else:
    lowerCamelCase_   					=						'conv'

  set_weights(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    F'''layers.{layer_id}.{layer_type}.{weight_type}'''			)







@torch.no_grad()
def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]=None		,    _lowerCamelCase	:		Tuple=None		,    _lowerCamelCase	:		Dict=True			)				->    List[Any]:
  if config_path is not None:
    lowerCamelCase_   					=						DataaVecAudioConfig.from_pretrained(_lowerCamelCase			)
  else:
    lowerCamelCase_   					=						DataaVecAudioConfig()

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

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

  def load_dataavec(_lowerCamelCase	:		str			):
    lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([path]			)
    return model[0].eval()

  lowerCamelCase_   					=						load_dataavec(_lowerCamelCase			)

  recursively_load_weights(_lowerCamelCase		,    _lowerCamelCase		,    not is_finetuned			)

  lowerCamelCase_   					=						WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'			)

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

  lowerCamelCase_   					=						processor(_lowerCamelCase		,    return_tensors='pt'		,    padding=_lowerCamelCase			)

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

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

    lowerCamelCase_   					=						torch.argmax(_lowerCamelCase		,    dim=-1			)
    lowerCamelCase_   					=						processor.batch_decode(_lowerCamelCase			)

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

  print(our_output.shape		,    their_output.shape			)
  lowerCamelCase_   					=						torch.max(torch.abs(our_output - their_output			)			).item()
  print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
  lowerCamelCase_   					=						torch.allclose(_lowerCamelCase		,    _lowerCamelCase		,    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(_lowerCamelCase			)

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


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						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'
  )
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						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"""





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


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    str    =						{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					str						=			"""regnet"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			["""x""", """y"""]






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Any=32	,						__SCREAMING_SNAKE_CASE					:							List[str]=[128, 192, 512, 1088]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=[2, 6, 12, 2]	,						__SCREAMING_SNAKE_CASE					:							str=64	,						__SCREAMING_SNAKE_CASE					:							List[Any]="y"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="relu"	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						)					->		Union[str, Any]:
      super().__init__(**__SCREAMING_SNAKE_CASE )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						embedding_size
      lowerCamelCase_   					=						hidden_sizes
      lowerCamelCase_   					=						depths
      lowerCamelCase_   					=						groups_width
      lowerCamelCase_   					=						layer_type
      lowerCamelCase_   					=						hidden_act
      # always downsample in the first stage
      lowerCamelCase_   					=						True




