





'''simple docstring'''

import numpy

# List of input, output pairs
UpperCAmelCase					:		List[Any]		=							(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
UpperCAmelCase					:		Optional[int]		=							(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
UpperCAmelCase					:		str		=							[2, 4, 1, 5]
UpperCAmelCase					:		int		=							len(train_data)
UpperCAmelCase					:		List[str]		=							0.0_09




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_="train"	):





      """simple docstring"""



      return calculate_hypothesis_value(lowerCAmelCase_			,      lowerCAmelCase_	) - output(
          lowerCAmelCase_			,      lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      for i in range(len(lowerCAmelCase_	) - 1	):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if data_set == "train":
            return _hypothesis_value(train_data[example_no][0]	)
      elif data_set == "test":
            return _hypothesis_value(test_data[example_no][0]	)
      return None




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=m	):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     0
      for i in range(lowerCAmelCase_	):
            if index == -1:
                  summation_value += _error(lowerCAmelCase_	)
            else:
                  summation_value += _error(lowerCAmelCase_	) * train_data[i][0][index]
      return summation_value




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     summation_of_cost_derivative(lowerCAmelCase_			,      lowerCAmelCase_	) / m
      return cost_derivative_value




def    _a    (							):





      """simple docstring"""



      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      _snake_case		:	str					     =     0.000_002
      _snake_case		:	Optional[int]					     =     0
      _snake_case		:	Optional[int]					     =     0
      while True:
            j += 1
            _snake_case		:	List[str]					     =     [0, 0, 0, 0]
            for i in range(0			,      len(lowerCAmelCase_	)	):
                  _snake_case		:	Optional[Any]					     =     get_cost_derivative(i - 1	)
                  _snake_case		:	Tuple					     =     (
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                lowerCAmelCase_			,      lowerCAmelCase_			,      atol=lowerCAmelCase_			,      rtol=lowerCAmelCase_			,      ):
                  break
            _snake_case		:	Any					     =     temp_parameter_vector
      print(('''Number of iterations:''', j)	)




def    _a    (							):





      """simple docstring"""



      for i in range(len(lowerCAmelCase_	)	):
            print(('''Actual output value:''', output(lowerCAmelCase_			,      '''test'''	))	)
            print(('''Hypothesis output:''', calculate_hypothesis_value(lowerCAmelCase_			,      '''test'''	))	)


if __name__ == "__main__":
   run_gradient_descent()
   print('\nTesting gradient descent for a linear hypothesis function.\n')
   test_gradient_descent()









'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase					:		Dict		=							{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class lowerCamelCase						(a__					):
  _lowercase  :	List[str]       					=				"""sew-d"""







  def __init__(		self     ,     lowercase__=32     ,     lowercase__=768     ,     lowercase__=12     ,     lowercase__=12     ,     lowercase__=3_072     ,     lowercase__=2     ,     lowercase__=512     ,     lowercase__=256     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__=("p2c", "c2p")     ,     lowercase__="layer_norm"     ,     lowercase__="gelu_python"     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.1     ,     lowercase__=0.02     ,     lowercase__=1E-7     ,     lowercase__=1E-5     ,     lowercase__="group"     ,     lowercase__="gelu"     ,     lowercase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,     lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,     lowercase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,     lowercase__=False     ,     lowercase__=128     ,     lowercase__=16     ,     lowercase__=True     ,     lowercase__=0.05     ,     lowercase__=10     ,     lowercase__=2     ,     lowercase__=0.0     ,     lowercase__=10     ,     lowercase__=0     ,     lowercase__="mean"     ,     lowercase__=False     ,     lowercase__=False     ,     lowercase__=256     ,     lowercase__=0     ,     lowercase__=1     ,     lowercase__=2     ,     **lowercase__     ,     )		->						Dict:






        """simple docstring"""





        super().__init__(**lowercase__     ,     pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__		)
        _snake_case		:	List[str]					     =     hidden_size
        _snake_case		:	Optional[Any]					     =     feat_extract_norm
        _snake_case		:	Tuple					     =     feat_extract_activation
        _snake_case		:	Tuple					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     list(lowercase__		)
        _snake_case		:	Any					     =     conv_bias
        _snake_case		:	List[Any]					     =     num_conv_pos_embeddings
        _snake_case		:	Any					     =     num_conv_pos_embedding_groups
        _snake_case		:	Union[str, Any]					     =     len(self.conv_dim		)
        _snake_case		:	Optional[Any]					     =     num_hidden_layers
        _snake_case		:	Optional[int]					     =     intermediate_size
        _snake_case		:	Any					     =     squeeze_factor
        _snake_case		:	Optional[Any]					     =     max_position_embeddings
        _snake_case		:	Tuple					     =     position_buckets
        _snake_case		:	Tuple					     =     share_att_key
        _snake_case		:	Any					     =     relative_attention
        _snake_case		:	Optional[int]					     =     norm_rel_ebd
        _snake_case		:	Optional[Any]					     =     list(lowercase__		)
        _snake_case		:	List[Any]					     =     hidden_act
        _snake_case		:	List[Any]					     =     num_attention_heads
        _snake_case		:	Dict					     =     hidden_dropout
        _snake_case		:	Tuple					     =     attention_dropout
        _snake_case		:	Union[str, Any]					     =     activation_dropout
        _snake_case		:	List[Any]					     =     feat_proj_dropout
        _snake_case		:	Optional[int]					     =     final_dropout
        _snake_case		:	Optional[Any]					     =     layer_norm_eps
        _snake_case		:	Dict					     =     feature_layer_norm_eps
        _snake_case		:	List[Any]					     =     initializer_range
        _snake_case		:	Dict					     =     vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect.'''
                  '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                  F'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
                  F'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        _snake_case		:	Union[str, Any]					     =     apply_spec_augment
        _snake_case		:	Any					     =     mask_time_prob
        _snake_case		:	List[str]					     =     mask_time_length
        _snake_case		:	Dict					     =     mask_time_min_masks
        _snake_case		:	Union[str, Any]					     =     mask_feature_prob
        _snake_case		:	Tuple					     =     mask_feature_length
        _snake_case		:	Union[str, Any]					     =     mask_feature_min_masks

        # ctc loss
        _snake_case		:	Optional[Any]					     =     ctc_loss_reduction
        _snake_case		:	Optional[Any]					     =     ctc_zero_infinity

        # sequence classification
        _snake_case		:	List[Any]					     =     use_weighted_layer_sum
        _snake_case		:	Any					     =     classifier_proj_size







  @property
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        return functools.reduce(operator.mul     ,     self.conv_stride     ,     1		)


