

'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
						import jax
						import jax.numpy as jnp



@slow
@require_flax
class 					lowercase						(  unittest.TestCase ):


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								return F"""gaussian_noise_s={seed}_shape={"_".join([str(_snake_case) for s in shape])}.npy"""


							def 						_snake_case							(			self)							->				Any:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()


							def 						_snake_case							(			self    ,			_snake_case=0    ,			_snake_case=(4, 4, 64, 64)    ,			_snake_case=False)							->				Any:
								UpperCAmelCase_		:      str       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      Any       	=					jnp.array(load_hf_numpy(self.get_file_format(_snake_case    ,			_snake_case))    ,			dtype=_snake_case)
								return image


							def 						_snake_case							(			self    ,			_snake_case=False    ,			_snake_case="CompVis/stable-diffusion-v1-4")							->				Any:
								UpperCAmelCase_		:      Any       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      Union[str, Any]       	=					'bf16' if fpaa else None

								UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					FlaxUNetaDConditionModel.from_pretrained(
								    _snake_case    ,			subfolder='unet'    ,			dtype=_snake_case    ,			revision=_snake_case)
								return model, params


							def 						_snake_case							(			self    ,			_snake_case=0    ,			_snake_case=(4, 77, 768)    ,			_snake_case=False)							->				Any:
								UpperCAmelCase_		:      List[str]       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      int       	=					jnp.array(load_hf_numpy(self.get_file_format(_snake_case    ,			_snake_case))    ,			dtype=_snake_case)
								return hidden_states


							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
							        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
							        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
							        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
							        # fmt: on
							    ])
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Dict:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					self.get_latents(_snake_case    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.get_encoder_hidden_states(_snake_case    ,			fpaa=_snake_case)

								UpperCAmelCase_		:      int       	=					model.apply(
								    {'params': params}    ,			_snake_case    ,			jnp.array(_snake_case    ,			dtype=jnp.intaa)    ,			encoder_hidden_states=_snake_case    ,			).sample

								assert sample.shape == latents.shape

								UpperCAmelCase_		:      str       	=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))    ,			dtype=jnp.floataa)
								UpperCAmelCase_		:      Dict       	=					jnp.array(_snake_case    ,			dtype=jnp.floataa)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(_snake_case    ,			_snake_case    ,			atol=1e-2)





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
							        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
							        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
							        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
							        # fmt: on
							    ])
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.get_latents(_snake_case    ,			shape=(4, 4, 96, 96)    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Any       	=					self.get_encoder_hidden_states(_snake_case    ,			shape=(4, 77, 1024)    ,			fpaa=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					model.apply(
								    {'params': params}    ,			_snake_case    ,			jnp.array(_snake_case    ,			dtype=jnp.intaa)    ,			encoder_hidden_states=_snake_case    ,			).sample

								assert sample.shape == latents.shape

								UpperCAmelCase_		:      Dict       	=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))    ,			dtype=jnp.floataa)
								UpperCAmelCase_		:      Tuple       	=					jnp.array(_snake_case    ,			dtype=jnp.floataa)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(_snake_case    ,			_snake_case    ,			atol=1e-2)









'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[Any]       	=					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.
								UpperCAmelCase_		:      Tuple       	=					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.
								UpperCAmelCase_		:      List[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.
								UpperCAmelCase_		:      Dict       	=					numpy.random.rand(3    ,			1)

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase_		:      Union[str, Any]       	=					numpy.zeros(output_array.shape)


							def 						_snake_case							(			self)							->				numpy.ndarray:
								UpperCAmelCase_		:      Any       	=					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.
								UpperCAmelCase_		:      Tuple       	=					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.
								UpperCAmelCase_		:      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 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      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)    ,			)
								UpperCAmelCase_		:      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)    ,			)
								UpperCAmelCase_		:      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 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(self.array    ,			self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      int       	=					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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					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.
	UpperCAmelCase_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase   ,output_array=UpperCamelCase  )

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=UpperCamelCase   ,iterations=1_0   ,give_loss=UpperCamelCase  )

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


if __name__ == "__main__":
						example()






