


'''simple docstring'''






import numpy

# List of input, output pairs
__A :  str    							=				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__A :  str    							=				(((515, 22, 13), 555), ((61, 35, 49), 150))
__A :  Tuple    							=				[2, 4, 1, 5]
__A :  Optional[int]    							=				len(train_data)
__A :  Optional[Any]    							=				0.0_0_9







def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       List[str]="train" ):
  '''simple docstring'''





  return calculate_hypothesis_value(A__							,   A__ ) - output(
      A__							,   A__ )







def     UpperCamelCase_      (   A__ :       Any ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Optional[int]							,   A__ :       List[Any] ):
  '''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     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       Union[str, Any] ):
  '''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     UpperCamelCase_      (   A__ :       str							,   A__ :       Union[str, Any]=m ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       summation_of_cost_derivative(A__							,   A__ ) / m
  return cost_derivative_value







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  lowerCAmelCase_      :				List[Any]      =       0.000002
  lowerCAmelCase_      :				Dict      =       0
  lowerCAmelCase_      :				Union[str, Any]      =       0
  while True:
    j += 1
    lowerCAmelCase_      :				Optional[int]      =       [0, 0, 0, 0]
    for i in range(0							,   len(A__ ) ):
      lowerCAmelCase_      :				int      =       get_cost_derivative(i - 1 )
      lowerCAmelCase_      :				Tuple      =       (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        A__							,   A__							,   atol=A__							,   rtol=A__							,   ):
      break
    lowerCAmelCase_      :				Union[str, Any]      =       temp_parameter_vector
  print(("""Number of iterations:""", j) )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





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


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







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



