



"""simple docstring"""





from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar






def 	__UpperCAmelCase					(       snake_case_  :  float   ,	snake_case_  :  float   ,	snake_case_  :  int   )							->	list:






   """simple docstring"""


   seed(1   )
   return [gauss(snake_case_   ,	snake_case_   ) for _ in range(snake_case_   )]






def 	__UpperCAmelCase					(       snake_case_  :  int   ,	snake_case_  :  list   )							->	list:






   """simple docstring"""


   return [k for k in range(snake_case_   ) for _ in range(instance_count[k]   )]






def 	__UpperCAmelCase					(       snake_case_  :  int   ,	snake_case_  :  list   )							->	float:






   """simple docstring"""


   return sum(snake_case_   ) / instance_count






def 	__UpperCAmelCase					(       snake_case_  :  int   ,	snake_case_  :  int   )							->	float:






   """simple docstring"""


   return instance_count / total_count






def 	__UpperCAmelCase					(       snake_case_  :  list   ,	snake_case_  :  list   ,	snake_case_  :  int   )							->	float:






   """simple docstring"""


   _lowerCAmelCase					     =		[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(snake_case_   )   ):
      # for loop iterates over number of elements in inner layer of items
      for j in range(len(items[i]   )   ):
         # appending squared differences to 'squared_diff' list
         squared_diff.append((items[i][j] - means[i]) ** 2   )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
   _lowerCAmelCase					     =		len(snake_case_   )  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  list   ,	snake_case_  :  list   ,	snake_case_  :  float   ,	snake_case_  :  list   )							->	list:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   # for loop iterates over number of elements in list
   for i in range(len(snake_case_   )   ):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]   )   ):
         _lowerCAmelCase					     =		[]  # to store all discriminant values of each item as a list
         # for loop iterates over number of classes we have in our dataset
         for k in range(len(snake_case_   )   ):
            # appending values of discriminants for each class to 'temp' list
            temp.append(
                x_items[i][j] * (means[k] / variance)
                - (means[k] ** 2 / (2 * variance))
                + log(probabilities[k]   )   )
         # appending discriminant values of each item to 'results' list
         results.append(snake_case_   )

   return [result.index(max(snake_case_   )   ) for result in results]






def 	__UpperCAmelCase					(       snake_case_  :  list   ,	snake_case_  :  list   )							->	float:






   """simple docstring"""


   _lowerCAmelCase					     =		sum(1 for i, j in zip(snake_case_   ,	snake_case_   ) if i == j   )
   # percentage of accuracy equals to number of correct predictions divided by number
   # of all data and multiplied by 100
   return (correct / len(snake_case_   )) * 100


SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	TypeVar('''num''')






def 	__UpperCAmelCase					(       snake_case_  :  Callable[[object], num]   ,	snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  Callable[[num], bool] = lambda snake_case_   : True   ,	snake_case_  :  str | None = None   ,	)							->	num:






   """simple docstring"""


   while True:
      try:
         _lowerCAmelCase					     =		input_type(input(snake_case_   ).strip() or default   )
         if condition(snake_case_   ):
            return user_input
         else:
            print(F"""{user_input}: {err_msg}"""   )
            continue
      except ValueError:
         print(
             F"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""   )






def 	__UpperCAmelCase					(       )							->	Optional[int]:






   """simple docstring"""


   while True:
      print(""" Linear Discriminant Analysis """.center(50   ,	"""*"""   )   )
      print("""*""" * 50   ,	"""\n"""   )
      print("""First of all we should specify the number of classes that"""   )
      print("""we want to generate as training dataset"""   )
      # Trying to get number of classes
      _lowerCAmelCase					     =		valid_input(
          input_type=snake_case_   ,	condition=lambda snake_case_   : x > 0   ,	input_msg="""Enter the number of classes (Data Groupings): """   ,	err_msg="""Number of classes should be positive!"""   ,	)

      print("""-""" * 100   )

      # Trying to get the value of standard deviation
      _lowerCAmelCase					     =		valid_input(
          input_type=snake_case_   ,	condition=lambda snake_case_   : x >= 0   ,	input_msg=(
              """Enter the value of standard deviation"""
              """(Default value is 1.0 for all classes): """
          )   ,	err_msg="""Standard deviation should not be negative!"""   ,	default="""1.0"""   ,	)

      print("""-""" * 100   )

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      _lowerCAmelCase					     =		[]  # An empty list to store instance counts of classes in dataset
      for i in range(snake_case_   ):
         _lowerCAmelCase					     =		valid_input(
             input_type=snake_case_   ,	condition=lambda snake_case_   : x > 0   ,	input_msg=(F"""Enter The number of instances for class_{i+1}: """)   ,	err_msg="""Number of instances should be positive!"""   ,	)
         counts.append(snake_case_   )
      print("""-""" * 100   )

      # An empty list to store values of user-entered means of classes
      _lowerCAmelCase					     =		[]
      for a in range(snake_case_   ):
         _lowerCAmelCase					     =		valid_input(
             input_type=snake_case_   ,	input_msg=(F"""Enter the value of mean for class_{a+1}: """)   ,	err_msg="""This is an invalid value."""   ,	)
         user_means.append(snake_case_   )
      print("""-""" * 100   )

      print("""Standard deviation: """   ,	snake_case_   )
      # print out the number of instances in classes in separated line
      for i, count in enumerate(snake_case_   ,	1   ):
         print(F"""Number of instances in class_{i} is: {count}"""   )
      print("""-""" * 100   )

      # print out mean values of classes separated line
      for i, user_mean in enumerate(snake_case_   ,	1   ):
         print(F"""Mean of class_{i} is: {user_mean}"""   )
      print("""-""" * 100   )

      # Generating training dataset drawn from gaussian distribution
      _lowerCAmelCase					     =		[
          gaussian_distribution(user_means[j]   ,	snake_case_   ,	counts[j]   )
          for j in range(snake_case_   )
      ]
      print("""Generated Normal Distribution: \n"""   ,	snake_case_   )
      print("""-""" * 100   )

      # Generating Ys to detecting corresponding classes
      _lowerCAmelCase					     =		y_generator(snake_case_   ,	snake_case_   )
      print("""Generated Corresponding Ys: \n"""   ,	snake_case_   )
      print("""-""" * 100   )

      # Calculating the value of actual mean for each class
      _lowerCAmelCase					     =		[calculate_mean(counts[k]   ,	x[k]   ) for k in range(snake_case_   )]
      # for loop iterates over number of elements in 'actual_means' list and print
      # out them in separated line
      for i, actual_mean in enumerate(snake_case_   ,	1   ):
         print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""   )
      print("""-""" * 100   )

      # Calculating the value of probabilities for each class
      _lowerCAmelCase					     =		[
          calculate_probabilities(counts[i]   ,	sum(snake_case_   )   ) for i in range(snake_case_   )
      ]

      # for loop iterates over number of elements in 'probabilities' list and print
      # out them in separated line
      for i, probability in enumerate(snake_case_   ,	1   ):
         print(F"""Probability of class_{i} is: {probability}"""   )
      print("""-""" * 100   )

      # Calculating the values of variance for each class
      _lowerCAmelCase					     =		calculate_variance(snake_case_   ,	snake_case_   ,	sum(snake_case_   )   )
      print("""Variance: """   ,	snake_case_   )
      print("""-""" * 100   )

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      _lowerCAmelCase					     =		predict_y_values(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
      print("""-""" * 100   )

      # Calculating Accuracy of the model
      print(F"""Accuracy: {accuracy(snake_case_   ,	snake_case_   )}"""   )
      print("""-""" * 100   )
      print(""" DONE """.center(100   ,	"""+"""   )   )

      if input("""Press any key to restart or 'q' for quit: """   ).strip().lower() == "q":
         print("""\n""" + """GoodBye!""".center(100   ,	"""-"""   ) + """\n"""   )
         break
      system("""cls""" if name == """nt""" else """clear"""   )  # noqa: S605


if __name__ == "__main__":
  main()




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       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 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

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






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)