import random




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		list[list[float]]							,      lowerCamelCase							:		list[int]							,      lowerCamelCase							:		float = 0.0_1							,      lowerCamelCase							:		int = 1_0_0_0							,      lowerCamelCase							:		float = -1							,      )			->				None:
        _UpperCamelCase  :							str	    =	sample
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        _UpperCamelCase  :							Any	    =	target
        if len(self.target	) == 0:
               raise ValueError('Target data can not be empty'	)
        if len(self.sample	) != len(self.target	):
               raise ValueError('Sample data and Target data do not have matching lengths'	)
        _UpperCamelCase  :							Dict	    =	learning_rate
        _UpperCamelCase  :							Dict	    =	epoch_number
        _UpperCamelCase  :							Optional[Any]	    =	bias
        _UpperCamelCase  :							Any	    =	len(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	len(sample[0]	)  # number of columns in dataset
        _UpperCamelCase  :							list	    =	[]

 def        __snake_case       (      self							:		List[str]	)			->				None:
        for sample in self.sample:
               sample.insert(0							,      self.bias	)

        for _ in range(self.col_sample	):
               self.weight.append(random.random()	)

        self.weight.insert(0							,      self.bias	)

        _UpperCamelCase  :							List[str]	    =	0

        while True:
               _UpperCamelCase  :							int	    =	False
               for i in range(self.number_sample	):
                      _UpperCamelCase  :							Union[str, Any]	    =	0
                      for j in range(self.col_sample + 1	):
                             _UpperCamelCase  :							List[Any]	    =	u + self.weight[j] * self.sample[i][j]
                      _UpperCamelCase  :							str	    =	self.sign(lowerCamelCase	)
                      if y != self.target[i]:
                             for j in range(self.col_sample + 1	):
                                    _UpperCamelCase  :							Optional[int]	    =	(
                                        self.weight[j]
                                        + self.learning_rate
                                        * (self.target[i] - y)
                                        * self.sample[i][j]
                                    )
                             _UpperCamelCase  :							Dict	    =	True
            # print('Epoch: \n',epoch_count)
               _UpperCamelCase  :							Optional[int]	    =	epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                      print(('\nEpoch:\n', epoch_count)	)
                      print('------------------------\n'	)
                      # if epoch_count > self.epoch_number or not error:
                      break

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		list[float]	)			->				None:
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        sample.insert(0							,      self.bias	)
        _UpperCamelCase  :							List[Any]	    =	0
        for i in range(self.col_sample + 1	):
               _UpperCamelCase  :							List[Any]	    =	u + self.weight[i] * sample[i]

        _UpperCamelCase  :							Union[str, Any]	    =	self.sign(lowerCamelCase	)

        if y == -1:
               print(('Sample: ', sample)	)
               print('classification: P1'	)
        else:
               print(('Sample: ', sample)	)
               print('classification: P2'	)

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		float	)			->				int:
        return 1 if u >= 0 else -1







UpperCAmelCase      		=					[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

UpperCAmelCase      		=					[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCAmelCase      		=					Perceptron(
     sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
 )
 network.training()
 print('''Finished training perceptron''')
 print('''Enter values to predict or q to exit''')
 while True:
  UpperCAmelCase      		=					[]
  for i in range(len(samples[0])):
   UpperCAmelCase      		=					input('''value: ''').strip()
   if user_input == "q":
    break
   UpperCAmelCase      		=					float(user_input)
   sample.insert(i, observation)
  network.sort(sample)






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase							={
    "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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for attribute in key.split('.'     ):
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     ).shape
     else:
          SCREAMING_SNAKE_CASE 		=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":
          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
     else:
          SCREAMING_SNAKE_CASE 		=value

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     SCREAMING_SNAKE_CASE 		=fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE 		=hf_model.feature_extractor

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

                    if key in name:
                         SCREAMING_SNAKE_CASE 		=True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE 		=name.split(lowerCAmelCase_     )[0].split('.'     )[-2]
                              SCREAMING_SNAKE_CASE 		=mapped_key.replace('*',       lowerCAmelCase_     )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE 		='weight_g'
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE 		='weight_v'
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE 		='weight'
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE 		='bias'
                         else:
                              SCREAMING_SNAKE_CASE 		=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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """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:
               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.'
               )
               SCREAMING_SNAKE_CASE 		=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.'
               )
               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:
               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."
               )
               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:
               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.'
               )
               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(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=HubertConfig()
     SCREAMING_SNAKE_CASE 		=model.config

     SCREAMING_SNAKE_CASE 		=fs_config.activation_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=fs_config.attention_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=eval(fs_config.extractor_conv_feature_layers     )
     SCREAMING_SNAKE_CASE 		=[x[0] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[1] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[2] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		='gelu'
     SCREAMING_SNAKE_CASE 		='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.activation_fn
     SCREAMING_SNAKE_CASE 		=fs_config.dropout
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_embed_dim
     SCREAMING_SNAKE_CASE 		=0.02
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_ffn_embed_dim
     SCREAMING_SNAKE_CASE 		=1e-5
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_attention_heads
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos_groups
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos
     SCREAMING_SNAKE_CASE 		=len(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_layers

     return config







@torch.no_grad()
def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=distilhubert().model.model

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

     SCREAMING_SNAKE_CASE 		=WavaVecaFeatureExtractor(
         feature_size=1,       sampling_rate=16000,       padding_value=0,       do_normalize=lowerCAmelCase_,       return_attention_mask=lowerCAmelCase_,       )
     SCREAMING_SNAKE_CASE 		=HubertModel(lowerCAmelCase_     )

     recursively_load_weights(lowerCAmelCase_,       lowerCAmelCase_     )

     feature_extractor.save_pretrained(lowerCAmelCase_     )
     hf_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
 _lowerCamelCase							=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")
 _lowerCamelCase							=parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

