import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class __snake_case       :




			@property
			def 			__a		(					self    :       Optional[int]   ):



					"""simple docstring"""






					return self.get_dummy_input()




			@property
			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					if self.block_type == "down":
							return (4, 32, 16, 16)
					elif self.block_type == "mid":
							return (4, 32, 32, 32)
					elif self.block_type == "up":
							return (4, 32, 64, 64)

					raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""   )




			def 			__a		(					self    :       Dict ,			_lowercase    :       Tuple=True ,			_lowercase    :       List[Any]=False ,			_lowercase    :       int=False ,			_lowercase    :       Dict=False ,			):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   4
					SCREAMING_SNAKE_CASE__    							=   32
					SCREAMING_SNAKE_CASE__    							=   (32, 32)

					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   torch.device(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   (batch_size, num_channels) + sizes
					SCREAMING_SNAKE_CASE__    							=   randn_tensor(_lowercase ,			generator=_lowercase ,			device=_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   {"""hidden_states""": hidden_states}

					if include_temb:
							SCREAMING_SNAKE_CASE__    							=   1_28
							SCREAMING_SNAKE_CASE__    							=   randn_tensor((batch_size, temb_channels) ,			generator=_lowercase ,			device=_lowercase   )

					if include_res_hidden_states_tuple:
							SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(1   )
							SCREAMING_SNAKE_CASE__    							=   (randn_tensor(_lowercase ,			generator=_lowercase ,			device=_lowercase   ),)

					if include_encoder_hidden_states:
							SCREAMING_SNAKE_CASE__    							=   floats_tensor((batch_size, 32, 32)   ).to(_lowercase   )

					if include_skip_sample:
							SCREAMING_SNAKE_CASE__    							=   randn_tensor(((batch_size, 3) + sizes) ,			generator=_lowercase ,			device=_lowercase   )

					return dummy_input




			def 			__a		(					self    :       Union[str, Any]   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   {
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 1_28,
					}
					if self.block_type == "up":
							SCREAMING_SNAKE_CASE__    							=   32

					if self.block_type == "mid":
							init_dict.pop("""out_channels"""   )

					SCREAMING_SNAKE_CASE__    							=   self.dummy_input
					return init_dict, inputs_dict




			def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Optional[Any]   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.prepare_init_args_and_inputs_for_common()
					SCREAMING_SNAKE_CASE__    							=   self.block_class(**_lowercase   )
					unet_block.to(_lowercase   )
					unet_block.eval()

					with torch.no_grad():
							SCREAMING_SNAKE_CASE__    							=   unet_block(**_lowercase   )

					if isinstance(_lowercase ,			_lowercase   ):
							SCREAMING_SNAKE_CASE__    							=   output[0]

					self.assertEqual(output.shape ,			self.output_shape   )

					SCREAMING_SNAKE_CASE__    							=   output[0, -1, -3:, -3:]
					SCREAMING_SNAKE_CASE__    							=   torch.tensor(_lowercase   ).to(_lowercase   )
					assert torch_all_close(output_slice.flatten() ,			_lowercase ,			atol=5E-3   )




			@unittest.skipIf(torch_device == """mps""" ,			"""Training is not supported in mps"""   )
			def 			__a		(					self    :       int   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.prepare_init_args_and_inputs_for_common()
					SCREAMING_SNAKE_CASE__    							=   self.block_class(**_lowercase   )
					model.to(_lowercase   )
					model.train()
					SCREAMING_SNAKE_CASE__    							=   model(**_lowercase   )

					if isinstance(_lowercase ,			_lowercase   ):
							SCREAMING_SNAKE_CASE__    							=   output[0]

					SCREAMING_SNAKE_CASE__    							=   torch.device(_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   randn_tensor(output.shape ,			device=_lowercase   )
					SCREAMING_SNAKE_CASE__    							=   torch.nn.functional.mse_loss(_lowercase ,			_lowercase   )
					loss.backward()

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
   __lowerCamelCase    :				List[Any]							     =	datasets.load_iris()
   __lowerCamelCase    :				List[Any]							     =	iris.data[:, :2]
   __lowerCamelCase    :				Dict							     =	(iris.target != 0) * 1

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # predicting the value of probability from the logistic regression algorithm

   plt.figure(figsize=(10, 6))
   plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
   plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
