

'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A_							    =				False





class 				_snake_case			(							unittest.TestCase    ):
   def      __UpperCamelCase    (			self      :   Tuple				):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     return 12
   @property
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     return 12
   @property
   def      __UpperCamelCase    (			self      :   int				):
     return 32
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE:Tuple       =       VQModel(
         block_out_channels=[32, 64]  ,in_channels=3  ,out_channels=3  ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]  ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]  ,latent_channels=3  ,num_vq_embeddings=self.num_embed  ,vq_embed_dim=3  ,)
     return model
   @property
   def      __UpperCamelCase    (			self      :   int				):
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"				)
     return tokenizer
   @property
   def      __UpperCamelCase    (			self      :   str				):
     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE:Any       =       CLIPTextConfig(
         bos_token_id=0  ,eos_token_id=2  ,hidden_size=self.text_embedder_hidden_size  ,intermediate_size=37  ,layer_norm_eps=1e-05  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_000  ,)
     return CLIPTextModel(__lowerCAmelCase				)
   @property
   def      __UpperCamelCase    (			self      :   Tuple				):
     torch.manual_seed(0				)

     SCREAMING_SNAKE_CASE:Dict       =       12
     SCREAMING_SNAKE_CASE:List[Any]       =       12

     SCREAMING_SNAKE_CASE:Optional[Any]       =       {
         "attention_bias": True,
         "cross_attention_dim": 32,
         "attention_head_dim": height * width,
         "num_attention_heads": 1,
         "num_vector_embeds": self.num_embed,
         "num_embeds_ada_norm": self.num_embeds_ada_norm,
         "norm_num_groups": 32,
         "sample_size": width,
         "activation_fn": "geglu-approximate",
     }

     SCREAMING_SNAKE_CASE:List[str]       =       TransformeraDModel(**__lowerCAmelCase				)
     return model
   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE:Any       =       "cpu"

     SCREAMING_SNAKE_CASE:Optional[int]       =       self.dummy_vqvae
     SCREAMING_SNAKE_CASE:List[str]       =       self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:List[str]       =       self.dummy_tokenizer
     SCREAMING_SNAKE_CASE:int       =       self.dummy_transformer
     SCREAMING_SNAKE_CASE:List[str]       =       VQDiffusionScheduler(self.num_embed				)
     SCREAMING_SNAKE_CASE:str       =       LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase				)

     SCREAMING_SNAKE_CASE:Any       =       VQDiffusionPipeline(
         vqvae=__lowerCAmelCase  ,text_encoder=__lowerCAmelCase  ,tokenizer=__lowerCAmelCase  ,transformer=__lowerCAmelCase  ,scheduler=__lowerCAmelCase  ,learned_classifier_free_sampling_embeddings=__lowerCAmelCase  ,)
     SCREAMING_SNAKE_CASE:Dict       =       pipe.to(__lowerCAmelCase				)
     pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

     SCREAMING_SNAKE_CASE:str       =       "teddy bear playing in the pool"

     SCREAMING_SNAKE_CASE:Tuple       =       torch.Generator(device=__lowerCAmelCase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       pipe([prompt]  ,generator=__lowerCAmelCase  ,num_inference_steps=2  ,output_type="np"				)
     SCREAMING_SNAKE_CASE:Tuple       =       output.images

     SCREAMING_SNAKE_CASE:Optional[int]       =       torch.Generator(device=__lowerCAmelCase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE:List[str]       =       pipe(
         [prompt]  ,generator=__lowerCAmelCase  ,output_type="np"  ,return_dict=__lowerCAmelCase  ,num_inference_steps=2				)[0]

     SCREAMING_SNAKE_CASE:int       =       image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Dict       =       image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 24, 24, 3)

     SCREAMING_SNAKE_CASE:Optional[int]       =       np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]				)

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







   def      __UpperCamelCase    (			self      :   Any				):
     SCREAMING_SNAKE_CASE:str       =       "cpu"

     SCREAMING_SNAKE_CASE:Optional[int]       =       self.dummy_vqvae
     SCREAMING_SNAKE_CASE:List[str]       =       self.dummy_text_encoder
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.dummy_tokenizer
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.dummy_transformer
     SCREAMING_SNAKE_CASE:Any       =       VQDiffusionScheduler(self.num_embed				)
     SCREAMING_SNAKE_CASE:Optional[int]       =       LearnedClassifierFreeSamplingEmbeddings(
         learnable=__lowerCAmelCase  ,hidden_size=self.text_embedder_hidden_size  ,length=tokenizer.model_max_length				)

     SCREAMING_SNAKE_CASE:str       =       VQDiffusionPipeline(
         vqvae=__lowerCAmelCase  ,text_encoder=__lowerCAmelCase  ,tokenizer=__lowerCAmelCase  ,transformer=__lowerCAmelCase  ,scheduler=__lowerCAmelCase  ,learned_classifier_free_sampling_embeddings=__lowerCAmelCase  ,)
     SCREAMING_SNAKE_CASE:List[str]       =       pipe.to(__lowerCAmelCase				)
     pipe.set_progress_bar_config(disable=__lowerCAmelCase				)

     SCREAMING_SNAKE_CASE:List[Any]       =       "teddy bear playing in the pool"

     SCREAMING_SNAKE_CASE:Tuple       =       torch.Generator(device=__lowerCAmelCase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE:List[str]       =       pipe([prompt]  ,generator=__lowerCAmelCase  ,num_inference_steps=2  ,output_type="np"				)
     SCREAMING_SNAKE_CASE:Any       =       output.images

     SCREAMING_SNAKE_CASE:Dict       =       torch.Generator(device=__lowerCAmelCase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE:Tuple       =       pipe(
         [prompt]  ,generator=__lowerCAmelCase  ,output_type="np"  ,return_dict=__lowerCAmelCase  ,num_inference_steps=2				)[0]

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

     assert image.shape == (1, 24, 24, 3)

     SCREAMING_SNAKE_CASE:Optional[Any]       =       np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]				)

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 2.0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2





@slow
@require_torch_gpu
class 				_snake_case			(							unittest.TestCase    ):
   def      __UpperCamelCase    (			self      :   Any				):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def      __UpperCamelCase    (			self      :   str				):
     SCREAMING_SNAKE_CASE:Optional[int]       =       load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"				)

     SCREAMING_SNAKE_CASE:Optional[Any]       =       VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"				)
     SCREAMING_SNAKE_CASE:Any       =       pipeline.to(__lowerCAmelCase				)
     pipeline.set_progress_bar_config(disable=__lowerCAmelCase				)

     # requires GPU generator for gumbel softmax
     # don't use GPU generator in tests though
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       torch.Generator(device=__lowerCAmelCase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE:List[str]       =       pipeline(
         "teddy bear playing in the pool"  ,num_images_per_prompt=1  ,generator=__lowerCAmelCase  ,output_type="np"  ,)

     SCREAMING_SNAKE_CASE:str       =       output.images[0]

     assert image.shape == (256, 256, 3)
     assert np.abs(expected_image - image				).max() < 2.0












'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   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.
							__snake_case						 =   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.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							__snake_case						 =   numpy.random.rand(3       ,		1      )

							# Real output values provided.
							__snake_case						 =   output_array

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




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




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

							__snake_case						 =   sigmoid(
							    numpy.dot(self.array       ,		self.input_layer_and_first_hidden_layer_weights      )      )

							__snake_case						 =   sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,		self.first_hidden_layer_and_second_hidden_layer_weights       ,		)      )

							__snake_case						 =   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 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   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.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

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

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


if __name__ == "__main__":
	example()

