

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       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     )							->	List[str]:



  """simple docstring"""






  for attribute in key.split('''.'''     ):
    _snake_case						=							getattr(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE     )

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

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



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     )							->	Optional[int]:



  """simple docstring"""






  _snake_case						=							[]
  _snake_case						=							fairseq_model.state_dict()

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

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

  for name, value in fairseq_dict.items():
    _snake_case						=							False
    if "conv_layers" in name:
      load_conv_layer(
          _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       )
      _snake_case						=							True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       )
      _snake_case						=							True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          _snake_case						=							"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]:
          _snake_case						=							True
          if "*" in mapped_key:
            _snake_case						=							name.split(_SCREAMING_SNAKE_CASE     )[0].split('''.'''     )[-2]
            _snake_case						=							mapped_key.replace('''*''' ,       _SCREAMING_SNAKE_CASE     )
          if "weight_g" in name:
            _snake_case						=							"weight_g"
          elif "weight_v" in name:
            _snake_case						=							"weight_v"
          elif "bias" in name:
            _snake_case						=							"bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _snake_case						=							"weight"
          else:
            _snake_case						=							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       snake_case_(_UpperCamelCase ,       _UpperCamelCase     )							->	List[str]:



  """simple docstring"""






  _snake_case						=							path.split('''.'''     )
  return reduce(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE     )



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     )							->	Tuple:



  """simple docstring"""






  _snake_case						=							access_by_string(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE     )
  _snake_case						=							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."""     )
  _snake_case						=							fsq_value
  logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""     )



def       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     )							->	int:



  """simple docstring"""






  _snake_case						=							full_name.split('''conv_layers.'''     )[-1]
  _snake_case						=							name.split('''.'''     )
  _snake_case						=							int(items[0]     )
  _snake_case						=							int(items[1]     )

  _snake_case						=							name.split('''.'''     )[-1]
  if type_id == 0:
    _snake_case						=							"conv"
  elif type_id == 2:
    _snake_case						=							"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       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase     )							->	List[Any]:



  """simple docstring"""






  _snake_case						=							full_name.split('''pos_conv.'''     )[-1]
  _snake_case						=							name.split('''.'''     )
  _snake_case						=							int(items[0]     )
  _snake_case						=							int(items[1]     )

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

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



@torch.no_grad()
def       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       _UpperCamelCase=True     )							->	List[str]:



  """simple docstring"""






  if config_path is not None:
    _snake_case						=							DataaVecAudioConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  else:
    _snake_case						=							DataaVecAudioConfig()

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

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

  def load_dataavec(_UpperCamelCase     ):
    _snake_case						=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]     )
    return model[0].eval()

  _snake_case						=							load_dataavec(_SCREAMING_SNAKE_CASE     )

  recursively_load_weights(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       not is_finetuned     )

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

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

  _snake_case						=							processor(_SCREAMING_SNAKE_CASE ,       return_tensors='''pt''' ,       padding=_SCREAMING_SNAKE_CASE     )

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

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

    _snake_case						=							torch.argmax(_SCREAMING_SNAKE_CASE ,       dim=-1     )
    _snake_case						=							processor.batch_decode(_SCREAMING_SNAKE_CASE     )

    print(F"""Expected Output: {ds[:4]['text']}, Pred: {output_string}"""     )
  else:
    _snake_case						=							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     )
    _snake_case						=							hf_wavavec(_SCREAMING_SNAKE_CASE ,       attention_mask=_SCREAMING_SNAKE_CASE     )["last_hidden_state"]

  print(our_output.shape ,       their_output.shape     )
  _snake_case						=							torch.max(torch.abs(our_output - their_output     )     ).item()
  print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
  _snake_case						=							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
 )




'''simple docstring'''
import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow and torch_device == "cuda":
		assert True
	else:
		assert False, "should have been skipped"
@require_torch
class  _snake_case						(unittest.TestCase):






			@slow
			@require_torch_gpu
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@require_torch_gpu
			@slow
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


@require_torch_gpu
@slow
def       a__    (  )			-> List[str]:


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




