

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()
_a			=       logging.get_logger(__name__)

_a			=       {
    '''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''',
}
_a			=       [
    '''lm_head''',
]







def _a			(	SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      Union[str, Any]		)   ->   int:

 """simple docstring"""






 for attribute in key.split('.'		):
  __lowerCAmelCase:       Optional[Any]            =				getattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)

 if weight_type is not None:
  __lowerCAmelCase:       List[Any]            =				getattr(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		).shape
 else:
  __lowerCAmelCase:       int            =				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:       Tuple            =				value
 elif weight_type == "weight_g":
  __lowerCAmelCase:       List[Any]            =				value
 elif weight_type == "weight_v":
  __lowerCAmelCase:       List[str]            =				value
 elif weight_type == "bias":
  __lowerCAmelCase:       List[str]            =				value
 else:
  __lowerCAmelCase:       Optional[int]            =				value

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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      Optional[Any]		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				[]
 __lowerCAmelCase:       Union[str, Any]            =				fairseq_model.state_dict()

 if not is_headless:
  __lowerCAmelCase:       Any            =				hf_model.dataavec_audio.feature_extractor
  __lowerCAmelCase:       Optional[Any]            =				hf_model.dataavec_audio.encoder.pos_conv_embed

 else:
  __lowerCAmelCase:       Union[str, Any]            =				hf_model.feature_extractor
  __lowerCAmelCase:       int            =				hf_model.encoder.pos_conv_embed

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:       List[Any]            =				False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , )
   __lowerCAmelCase:       Optional[int]            =				True
  elif "pos_conv" in name:
   load_pos_conv_layer(
       SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , )
   __lowerCAmelCase:       Tuple            =				True
  else:
   for key, mapped_key in MAPPING.items():
    if not is_headless:
     __lowerCAmelCase:       List[Any]            =				'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:       Any            =				True
     if "*" in mapped_key:
      __lowerCAmelCase:       Optional[Any]            =				name.split(SCREAMING_SNAKE_CASE		)[0].split('.'		)[-2]
      __lowerCAmelCase:       Optional[Any]            =				mapped_key.replace('*'   , SCREAMING_SNAKE_CASE		)
     if "weight_g" in name:
      __lowerCAmelCase:       Dict            =				'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:       List[str]            =				'weight_v'
     elif "bias" in name:
      __lowerCAmelCase:       Optional[Any]            =				'bias'
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowerCAmelCase:       Union[str, Any]            =				'weight'
     else:
      __lowerCAmelCase:       str            =				None
     set_recursively(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE		)

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







def _a			(	SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   List[Any]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[Any]            =				path.split('.'		)
 return reduce(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      str		)   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       List[Any]            =				access_by_string(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[Any]            =				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:       List[str]            =				fsq_value
 logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)







def _a			(	SCREAMING_SNAKE_CASE		:      Any   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Dict   , SCREAMING_SNAKE_CASE		:      Union[str, Any]		)   ->   Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				full_name.split('conv_layers.'		)[-1]
 __lowerCAmelCase:       List[str]            =				name.split('.'		)
 __lowerCAmelCase:       str            =				int(items[0]		)
 __lowerCAmelCase:       Optional[Any]            =				int(items[1]		)

 __lowerCAmelCase:       List[str]            =				name.split('.'		)[-1]
 if type_id == 0:
  __lowerCAmelCase:       List[str]            =				'conv'
 elif type_id == 2:
  __lowerCAmelCase:       Dict            =				'layer_norm'
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE		)
  return

 set_weights(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)







def _a			(	SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      List[Any]   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      List[Any]		)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       str            =				full_name.split('pos_conv.'		)[-1]
 __lowerCAmelCase:       List[Any]            =				name.split('.'		)
 __lowerCAmelCase:       int            =				int(items[0]		)
 __lowerCAmelCase:       Tuple            =				int(items[1]		)

 __lowerCAmelCase:       Optional[int]            =				name.split('.'		)[-1]
 if type_id != 0:
  unused_weights.append(SCREAMING_SNAKE_CASE		)
  return
 else:
  __lowerCAmelCase:       Any            =				'conv'

 set_weights(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , f'''layers.{layer_id}.{layer_type}.{weight_type}'''		)







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      int=None   , SCREAMING_SNAKE_CASE		:      List[Any]=None   , SCREAMING_SNAKE_CASE		:      int=True		)   ->   str:

 """simple docstring"""






 if config_path is not None:
  __lowerCAmelCase:       List[str]            =				DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 else:
  __lowerCAmelCase:       List[str]            =				DataaVecAudioConfig()

 if not is_finetuned:
  # Modify final_proj layer name
  __lowerCAmelCase:       Optional[int]            =				DataaVecAudioModel(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[Any]            =				os.path.dirname(SCREAMING_SNAKE_CASE		)

  __lowerCAmelCase:       List[str]            =				torch.load(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Optional[Any]            =				state_dict['model'].pop('final_proj.0.weight'		)
  __lowerCAmelCase:       str            =				state_dict['model'].pop('final_proj.0.bias'		)
  __lowerCAmelCase:       Union[str, Any]            =				os.path.join(SCREAMING_SNAKE_CASE   , 'converted.pt'		)
  torch.save(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 else:
  __lowerCAmelCase:       str            =				DataaVecAudioForCTC(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       Tuple            =				checkpoint_path

 def load_dataavec(SCREAMING_SNAKE_CASE		:      int		):
  __lowerCAmelCase:       Any            =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
  return model[0].eval()

 __lowerCAmelCase:       int            =				load_dataavec(SCREAMING_SNAKE_CASE		)

 recursively_load_weights(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , not is_finetuned		)

 __lowerCAmelCase:       List[Any]            =				WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'		)

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

 __lowerCAmelCase:       Optional[int]            =				processor(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , padding=SCREAMING_SNAKE_CASE		)

 __lowerCAmelCase:       Optional[Any]            =				inputs.input_values
 __lowerCAmelCase:       Dict            =				inputs.attention_mask
 #    input_values = inputs.input_values[:, :-1]
 #    attention_mask = inputs.attention_mask[:, :-1]

 hf_wavavec.eval()
 model.eval()
 if is_finetuned:
  __lowerCAmelCase:       List[Any]            =				model(source=SCREAMING_SNAKE_CASE   , padding_mask=(1 - attention_mask)   , mask=SCREAMING_SNAKE_CASE   , features_only=SCREAMING_SNAKE_CASE		)[
      'encoder_out'
  ].transpose(0   , 1		)
  __lowerCAmelCase:       Optional[int]            =				hf_wavavec(SCREAMING_SNAKE_CASE   , attention_mask=SCREAMING_SNAKE_CASE		)['logits']

  __lowerCAmelCase:       Optional[int]            =				torch.argmax(SCREAMING_SNAKE_CASE   , dim=-1		)
  __lowerCAmelCase:       Optional[int]            =				processor.batch_decode(SCREAMING_SNAKE_CASE		)

  print(f'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''		)
 else:
  __lowerCAmelCase:       Union[str, Any]            =				model(source=SCREAMING_SNAKE_CASE   , padding_mask=(1 - attention_mask)   , mask=SCREAMING_SNAKE_CASE   , features_only=SCREAMING_SNAKE_CASE		)[
      'layer_results'
  ][-1][0].transpose(0   , 1		)
  __lowerCAmelCase:       str            =				hf_wavavec(SCREAMING_SNAKE_CASE   , attention_mask=SCREAMING_SNAKE_CASE		)['last_hidden_state']

 print(our_output.shape   , their_output.shape		)
 __lowerCAmelCase:       List[str]            =				torch.max(torch.abs(our_output - their_output		)		).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
 __lowerCAmelCase:       str            =				torch.allclose(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 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(SCREAMING_SNAKE_CASE		)

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


if __name__ == "__main__":
  _a			=       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'''
  )
  _a			=       parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a			=       ['''UtteranceLevel''']







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_SNAKE_CASE   , map_location='cpu'		)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  _a			=       argparse.ArgumentParser()
  parser.add_argument(
      '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
  )
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
  parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
  _a			=       parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





