


'''simple docstring'''
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      lowerCamelCase_  (      nn.Module ):






      def __init__(   self							:       Dict		,		_A							:       Tuple = 16		,		_A							:       Any = 88		,		_A							:       Union[str, Any] = None		,		_A							:       Union[str, Any] = 1		,		_A							:       List[Any] = 0.0		,		_A							:       Dict = 32		,		_A							:       List[str] = None		,		_A							:       Any = False		,		_A							:       Tuple = None		,		_A							:       Dict = None		,		_A							:       Tuple = "geglu"		,		_A							:       List[str] = None		,		):


          '''simple docstring'''


          super().__init__()
          UpperCAmelCase__     :						str						=							nn.ModuleList(
              [
                  TransformeraDModel(
                      num_attention_heads=_A		,		attention_head_dim=_A		,		in_channels=_A		,		num_layers=_A		,		dropout=_A		,		norm_num_groups=_A		,		cross_attention_dim=_A		,		attention_bias=_A		,		sample_size=_A		,		num_vector_embeds=_A		,		activation_fn=_A		,		num_embeds_ada_norm=_A		,		)
                  for _ in range(2					)
              ]					)

          # Variables that can be set by a pipeline:

          # The ratio of transformer1 to transformer2's output states to be combined during inference
          UpperCAmelCase__     :						Union[str, Any]						=							0.5

          # The shape of `encoder_hidden_states` is expected to be
          # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
          UpperCAmelCase__     :						str						=							[77, 257]

          # Which transformer to use to encode which condition.
          # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
          UpperCAmelCase__     :						str						=							[1, 0]






      def lowercase_				(   self							:       Any		,		_A							:       Dict		,		_A							:       List[str]		,		_A							:       Tuple=None		,		_A							:       Any=None		,		_A							:       Any=None		,		_A							:       List[Any] = True		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							hidden_states

          UpperCAmelCase__     :						int						=							[]
          UpperCAmelCase__     :						List[str]						=							0
          # attention_mask is not used yet
          for i in range(2					):
              # for each of the two transformers, pass the corresponding condition tokens
              UpperCAmelCase__     :						Union[str, Any]						=							encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
              UpperCAmelCase__     :						Dict						=							self.transformer_index_for_condition[i]
              UpperCAmelCase__     :						List[Any]						=							self.transformers[transformer_index](
                  _A		,		encoder_hidden_states=_A		,		timestep=_A		,		cross_attention_kwargs=_A		,		return_dict=_A		,		)[0]
              encoded_states.append(encoded_state - input_states					)
              tokens_start += self.condition_lengths[i]

          UpperCAmelCase__     :						List[str]						=							encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
          UpperCAmelCase__     :						Dict						=							output_states + input_states

          if not return_dict:
              return (output_states,)

          return TransformeraDModelOutput(sample=_A					)


'''simple docstring'''




import numpy

# List of input, output pairs
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







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




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''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 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''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 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



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


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




