
'''simple docstring'''






import numpy

class 			UpperCAmelCase__    :



    def __init__(						self   :							Optional[Any],__A   :							numpy.ndarray,__A   :							numpy.ndarray  ):
          _lowerCamelCase					: Tuple		    = input_array

          # Random initial weights are assigned where first argument is the
          # number of nodes in previous layer and second argument is the
          # number of nodes in the next layer.

          # Random initial weights are assigned.
          # self.input_array.shape[1] is used to represent number of nodes in input layer.
          # First hidden layer consists of 4 nodes.
          _lowerCamelCase					: str		    = numpy.random.rand(
              self.input_array.shape[1],4  )

          # Random initial values for the first hidden layer.
          # First hidden layer has 4 nodes.
          # Second hidden layer has 3 nodes.
          _lowerCamelCase					: str		    = numpy.random.rand(
              4,3  )

          # Random initial values for the second hidden layer.
          # Second hidden layer has 3 nodes.
          # Output layer has 1 node.
          _lowerCamelCase					: List[Any]		    = numpy.random.rand(3,1  )

          # Real output values provided.
          _lowerCamelCase					: Optional[int]		    = output_array

          # Predicted output values by the neural network.
          # Predicted_output array initially consists of zeroes.
          _lowerCamelCase					: Tuple		    = numpy.zeros(output_array.shape  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: int		    = sigmoid(
              numpy.dot(self.input_array,self.input_layer_and_first_hidden_layer_weights  )  )

          # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
          # connecting the first hidden set of nodes with the second hidden set of nodes.
          _lowerCamelCase					: Dict		    = sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer,self.first_hidden_layer_and_second_hidden_layer_weights,)  )

          # layer_between_second_hidden_layer_and_output is the layer connecting
          # second hidden layer with the output node.
          _lowerCamelCase					: int		    = sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer,self.second_hidden_layer_and_output_layer_weights,)  )

          return self.layer_between_second_hidden_layer_and_output



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Optional[int]		    = numpy.dot(
              self.layer_between_first_hidden_layer_and_second_hidden_layer.T,2
              * (self.output_array - self.predicted_output)
              * sigmoid_derivative(self.predicted_output  ),)
          _lowerCamelCase					: Union[str, Any]		    = numpy.dot(
              self.layer_between_input_and_first_hidden_layer.T,numpy.dot(
                  2
                  * (self.output_array - self.predicted_output)
                  * sigmoid_derivative(self.predicted_output  ),self.second_hidden_layer_and_output_layer_weights.T,)
              * sigmoid_derivative(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer  ),)
          _lowerCamelCase					: Tuple		    = numpy.dot(
              self.input_array.T,numpy.dot(
                  numpy.dot(
                      2
                      * (self.output_array - self.predicted_output)
                      * sigmoid_derivative(self.predicted_output  ),self.second_hidden_layer_and_output_layer_weights.T,)
                  * sigmoid_derivative(
                      self.layer_between_first_hidden_layer_and_second_hidden_layer  ),self.first_hidden_layer_and_second_hidden_layer_weights.T,)
              * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer  ),)

          self.input_layer_and_first_hidden_layer_weights += (
              updated_input_layer_and_first_hidden_layer_weights
          )
          self.first_hidden_layer_and_second_hidden_layer_weights += (
              updated_first_hidden_layer_and_second_hidden_layer_weights
          )
          self.second_hidden_layer_and_output_layer_weights += (
              updated_second_hidden_layer_and_output_layer_weights
          )



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							numpy.ndarray,__A   :							int,__A   :							bool  ):
          for iteration in range(1,iterations + 1  ):
                _lowerCamelCase					: int		    = self.feedforward()
                self.back_propagation()
                if give_loss:
                      _lowerCamelCase					: List[str]		    = numpy.mean(numpy.square(output - self.feedforward()  )  )
                      print(f'Iteration {iteration} Loss: {loss}'  )



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							numpy.ndarray  ):
          _lowerCamelCase					: List[Any]		    = input_arr

          _lowerCamelCase					: Optional[Any]		    = sigmoid(
              numpy.dot(self.array,self.input_layer_and_first_hidden_layer_weights  )  )

          _lowerCamelCase					: Dict		    = sigmoid(
              numpy.dot(
                  self.layer_between_input_and_first_hidden_layer,self.first_hidden_layer_and_second_hidden_layer_weights,)  )

          _lowerCamelCase					: Optional[Any]		    = sigmoid(
              numpy.dot(
                  self.layer_between_first_hidden_layer_and_second_hidden_layer,self.second_hidden_layer_and_output_layer_weights,)  )

          return int(self.layer_between_second_hidden_layer_and_output > 0.6  )







def 		A_							(						_lowerCAmelCase :     numpy.ndarray				):



      """simple docstring"""




      return 1 / (1 + numpy.exp(-value				))







def 		A_							(						_lowerCAmelCase :     numpy.ndarray				):



      """simple docstring"""




      return (value) * (1 - (value))







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = numpy.array(
          (
              [0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1],
          )					,						dtype=numpy.floataa					,						)

      # True output values for the given input values.
      _lowerCamelCase					: List[str]		    = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])					,						dtype=numpy.floataa				)

      # Calling neural network class.
      _lowerCamelCase					: Tuple		    = TwoHiddenLayerNeuralNetwork(
          input_array=_lowerCAmelCase					,						output_array=_lowerCAmelCase				)

      # Calling training function.
      # Set give_loss to True if you want to see loss in every iteration.
      neural_network.train(output=_lowerCAmelCase					,						iterations=10					,						give_loss=_lowerCAmelCase				)

      return neural_network.predict(numpy.array(([1, 1, 1])					,						dtype=numpy.floataa				)				)


if __name__ == "__main__":
 example()






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase      							=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

             self.register_modules(unet=a__   ,				scheduler=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if strength < 0 or strength > 1:
                    raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

             _lowerCamelCase				:  Tuple						=		max(num_inference_steps - init_timestep   ,				0)
             _lowerCamelCase				:  int						=		self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(torch.Tensor, PIL.Image.Image, list)):
                    raise ValueError(
                        F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

             if isinstance(a__   ,				a__) and len(a__) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(a__)}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).sample

                    # 2. predict previous mean of image x_t-1 and add variance depending on eta
                    # eta corresponds to η in paper and should be between [0, 1]
                    # do x_t -> x_t-1
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image, latent_timestep.item())

             return ImagePipelineOutput(images=a__)

