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()
__lowerCamelCase    :				List[str]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Dict							     =	{
    '''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''',
}
__lowerCamelCase    :				Union[str, Any]							     =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   {}
  with open(__UpperCamelCase				,  """r"""			) as file:
    for line_number, line in enumerate(__UpperCamelCase			):
      SCREAMING_SNAKE_CASE__    							=   line.strip()
      if line:
        SCREAMING_SNAKE_CASE__    							=   line.split()
        SCREAMING_SNAKE_CASE__    							=   line_number
        SCREAMING_SNAKE_CASE__    							=   words[0]
        SCREAMING_SNAKE_CASE__    							=   value
  return result



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any			)							->      Any:







  """simple docstring"""





  for attribute in key.split("""."""			):
    SCREAMING_SNAKE_CASE__    							=   getattr(__UpperCamelCase				,  __UpperCamelCase			)

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

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

    # let's reduce dimension
    SCREAMING_SNAKE_CASE__    							=   value[0]
  else:
    SCREAMING_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":
    SCREAMING_SNAKE_CASE__    							=   value
  elif weight_type == "weight_g":
    SCREAMING_SNAKE_CASE__    							=   value
  elif weight_type == "weight_v":
    SCREAMING_SNAKE_CASE__    							=   value
  elif weight_type == "bias":
    SCREAMING_SNAKE_CASE__    							=   value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""			):
      SCREAMING_SNAKE_CASE__    							=   getattr(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   value
  else:
    SCREAMING_SNAKE_CASE__    							=   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[Any]				,  __UpperCamelCase					: int				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Tuple			)							->      Tuple:







  """simple docstring"""





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

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

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


__lowerCamelCase    :				int							     =	{
    '''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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Dict=None				,  __UpperCamelCase					: Any=None			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   False
  for key, mapped_key in MAPPING.items():
    SCREAMING_SNAKE_CASE__    							=   """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]:
      SCREAMING_SNAKE_CASE__    							=   True
      if "*" in mapped_key:
        SCREAMING_SNAKE_CASE__    							=   name.split(__UpperCamelCase			)[0].split("""."""			)[-2]
        SCREAMING_SNAKE_CASE__    							=   mapped_key.replace("""*"""				,  __UpperCamelCase			)
      if "weight_g" in name:
        SCREAMING_SNAKE_CASE__    							=   """weight_g"""
      elif "weight_v" in name:
        SCREAMING_SNAKE_CASE__    							=   """weight_v"""
      elif "bias" in name:
        SCREAMING_SNAKE_CASE__    							=   """bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        SCREAMING_SNAKE_CASE__    							=   """weight"""
      else:
        SCREAMING_SNAKE_CASE__    							=   None
      if hf_dict is not None:
        rename_dict(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
      else:
        set_recursively(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
      return is_used
  return is_used



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: Any				,  __UpperCamelCase					: Optional[int]			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []
  SCREAMING_SNAKE_CASE__    							=   fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__    							=   hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__    							=   False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  hf_model.config.feat_extract_norm == """group"""				,  )
      SCREAMING_SNAKE_CASE__    							=   True
    else:
      SCREAMING_SNAKE_CASE__    							=   load_wavaveca_layer(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    if not is_used:
      unused_weights.append(__UpperCamelCase			)

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: str			)							->      Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   full_name.split("""conv_layers."""			)[-1]
  SCREAMING_SNAKE_CASE__    							=   name.split("""."""			)
  SCREAMING_SNAKE_CASE__    							=   int(items[0]			)
  SCREAMING_SNAKE_CASE__    							=   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."""			)
      SCREAMING_SNAKE_CASE__    							=   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."""			)
      SCREAMING_SNAKE_CASE__    							=   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."""			)
      SCREAMING_SNAKE_CASE__    							=   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."""			)
      SCREAMING_SNAKE_CASE__    							=   value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
  else:
    unused_weights.append(__UpperCamelCase			)



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: str=None				,  __UpperCamelCase					: List[str]=None				,  __UpperCamelCase					: List[Any]=True				,  __UpperCamelCase					: Dict=False			)							->      Union[str, Any]:







  """simple docstring"""





  if config_path is not None:
    SCREAMING_SNAKE_CASE__    							=   WavaVecaConfig.from_pretrained(__UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   WavaVecaConfig()

  if is_seq_class:
    SCREAMING_SNAKE_CASE__    							=   read_txt_into_dict(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   idalabel
    SCREAMING_SNAKE_CASE__    							=   WavaVecaForSequenceClassification(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   WavaVecaFeatureExtractor(
        feature_size=1				,  sampling_rate=1_60_00				,  padding_value=0				,  do_normalize=__UpperCamelCase				,  return_attention_mask=__UpperCamelCase				,  )
    feature_extractor.save_pretrained(__UpperCamelCase			)

  elif is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE__    							=   Dictionary.load(__UpperCamelCase			)

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

      # fairseq has the <pad> and <s> switched
      SCREAMING_SNAKE_CASE__    							=   0
      SCREAMING_SNAKE_CASE__    							=   1
      with open(__UpperCamelCase				,  """w"""				,  encoding="""utf-8"""			) as vocab_handle:
        json.dump(__UpperCamelCase				,  __UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   WavaVecaCTCTokenizer(
          __UpperCamelCase				,  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=__UpperCamelCase				,  )
      SCREAMING_SNAKE_CASE__    							=   True if config.feat_extract_norm == """layer""" else False
      SCREAMING_SNAKE_CASE__    							=   WavaVecaFeatureExtractor(
          feature_size=1				,  sampling_rate=1_60_00				,  padding_value=0				,  do_normalize=__UpperCamelCase				,  return_attention_mask=__UpperCamelCase				,  )
      SCREAMING_SNAKE_CASE__    							=   WavaVecaProcessor(feature_extractor=__UpperCamelCase				,  tokenizer=__UpperCamelCase			)
      processor.save_pretrained(__UpperCamelCase			)

    SCREAMING_SNAKE_CASE__    							=   WavaVecaForCTC(__UpperCamelCase			)
  else:
    SCREAMING_SNAKE_CASE__    							=   WavaVecaForPreTraining(__UpperCamelCase			)

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

    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,  task=__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   model[0].eval()

  recursively_load_weights(__UpperCamelCase				,  __UpperCamelCase				,  not is_finetuned			)

  hf_wavavec.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
   __lowerCamelCase    :				List[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'''
   )
   parser.add_argument(
       '''--is_seq_class''',
       action='''store_true''',
       help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
   )
   __lowerCamelCase    :				List[Any]							     =	parser.parse_args()

   __lowerCamelCase    :				str							     =	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,
   )

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
