
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)





@flax.struct.dataclass
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42





class 							UpperCAmelCase				(  nn.Module  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					(16, 32, 96, 256)
	snake_case_    =					jnp.floataa






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			nn.Conv(
					    self.block_out_channels[0]							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					__A             =			[]
					for i in range(len(self.block_out_channels			) - 1			):
									__A             =			self.block_out_channels[i]
									__A             =			self.block_out_channels[i + 1]
									__A             =			nn.Conv(
									    A							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)
									blocks.append(A			)
									__A             =			nn.Conv(
									    A							,kernel_size=(3, 3)							,strides=(2, 2)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)
									blocks.append(A			)
					__A             =			blocks

					__A             =			nn.Conv(
					    self.conditioning_embedding_channels							,kernel_size=(3, 3)							,padding=((1, 1), (1, 1))							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)







	def __call__(	self :			Tuple							,A :			Dict			):
					__A             =			self.conv_in(A			)
					__A             =			nn.silu(A			)

					for block in self.blocks:
									__A             =			block(A			)
									__A             =			nn.silu(A			)

					__A             =			self.conv_out(A			)

					return embedding





@flax_register_to_config
class 							UpperCAmelCase				(  nn.Module      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					32
	snake_case_    =					4
	snake_case_    =					(
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	snake_case_    =					False
	snake_case_    =					(320, 640, 1280, 1280)
	snake_case_    =					2
	snake_case_    =					8
	snake_case_    =					None
	snake_case_    =					1280
	snake_case_    =					0.0
	snake_case_    =					False
	snake_case_    =					jnp.floataa
	snake_case_    =					True
	snake_case_    =					0
	snake_case_    =					"rgb"
	snake_case_    =					(16, 32, 96, 256)






	def     UpperCamelCase_  (	self :			Any							,A :			jax.random.KeyArray			):
					# init input tensors
					__A             =			(1, self.in_channels, self.sample_size, self.sample_size)
					__A             =			jnp.zeros(A							,dtype=jnp.floataa			)
					__A             =			jnp.ones((1,)							,dtype=jnp.intaa			)
					__A             =			jnp.zeros((1, 1, self.cross_attention_dim)							,dtype=jnp.floataa			)
					__A             =			(1, 3, self.sample_size * 8, self.sample_size * 8)
					__A             =			jnp.zeros(A							,dtype=jnp.floataa			)

					__A      ,							__A             =			jax.random.split(A			)
					__A             =			{"params": params_rng, "dropout": dropout_rng}

					return self.init(A							,A							,A							,A							,A			)["params"]






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.block_out_channels
					__A             =			block_out_channels[0] * 4

					# If `num_attention_heads` is not defined (which is the case for most models)
					# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
					# The reason for this behavior is to correct for incorrectly named variables that were introduced
					# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
					# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
					# which is why we correct for the naming here.
					__A             =			self.num_attention_heads or self.attention_head_dim

					# input
					__A             =			nn.Conv(
					    block_out_channels[0]							,kernel_size=(3, 3)							,strides=(1, 1)							,padding=((1, 1), (1, 1))							,dtype=self.dtype							,)

					# time
					__A             =			FlaxTimesteps(
					    block_out_channels[0]							,flip_sin_to_cos=self.flip_sin_to_cos							,freq_shift=self.config.freq_shift			)
					__A             =			FlaxTimestepEmbedding(A							,dtype=self.dtype			)

					__A             =			FlaxControlNetConditioningEmbedding(
					    conditioning_embedding_channels=block_out_channels[0]							,block_out_channels=self.conditioning_embedding_out_channels							,)

					__A             =			self.only_cross_attention
					if isinstance(A							,A			):
									__A             =			(only_cross_attention,) * len(self.down_block_types			)

					if isinstance(A							,A			):
									__A             =			(num_attention_heads,) * len(self.down_block_types			)

					# down
					__A             =			[]
					__A             =			[]

					__A             =			block_out_channels[0]

					__A             =			nn.Conv(
					    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
					controlnet_down_blocks.append(A			)

					for i, down_block_type in enumerate(self.down_block_types			):
									__A             =			output_channel
									__A             =			block_out_channels[i]
									__A             =			i == len(A			) - 1

									if down_block_type == "CrossAttnDownBlock2D":
													__A             =			FlaxCrossAttnDownBlockaD(
													    in_channels=A							,out_channels=A							,dropout=self.dropout							,num_layers=self.layers_per_block							,num_attention_heads=num_attention_heads[i]							,add_downsample=not is_final_block							,use_linear_projection=self.use_linear_projection							,only_cross_attention=only_cross_attention[i]							,dtype=self.dtype							,)
									else:
													__A             =			FlaxDownBlockaD(
													    in_channels=A							,out_channels=A							,dropout=self.dropout							,num_layers=self.layers_per_block							,add_downsample=not is_final_block							,dtype=self.dtype							,)

									down_blocks.append(A			)

									for _ in range(self.layers_per_block			):
													__A             =			nn.Conv(
													    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
													controlnet_down_blocks.append(A			)

									if not is_final_block:
													__A             =			nn.Conv(
													    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)
													controlnet_down_blocks.append(A			)

					__A             =			down_blocks
					__A             =			controlnet_down_blocks

					# mid
					__A             =			block_out_channels[-1]
					__A             =			FlaxUNetMidBlockaDCrossAttn(
					    in_channels=A							,dropout=self.dropout							,num_attention_heads=num_attention_heads[-1]							,use_linear_projection=self.use_linear_projection							,dtype=self.dtype							,)

					__A             =			nn.Conv(
					    A							,kernel_size=(1, 1)							,padding="VALID"							,kernel_init=nn.initializers.zeros_init()							,bias_init=nn.initializers.zeros_init()							,dtype=self.dtype							,)







	def __call__(	self :			Optional[int]							,A :			Union[str, Any]							,A :			Dict							,A :			int							,A :			int							,A :			float = 1.0							,A :			bool = True							,A :			bool = False							,):
					__A             =			self.controlnet_conditioning_channel_order
					if channel_order == "bgr":
									__A             =			jnp.flip(A							,axis=1			)

					# 1. time
					if not isinstance(A							,jnp.ndarray			):
									__A             =			jnp.array([timesteps]							,dtype=jnp.intaa			)
					elif isinstance(A							,jnp.ndarray			) and len(timesteps.shape			) == 0:
									__A             =			timesteps.astype(dtype=jnp.floataa			)
									__A             =			jnp.expand_dims(A							,0			)

					__A             =			self.time_proj(A			)
					__A             =			self.time_embedding(A			)

					# 2. pre-process
					__A             =			jnp.transpose(A							,(0, 2, 3, 1)			)
					__A             =			self.conv_in(A			)

					__A             =			jnp.transpose(A							,(0, 2, 3, 1)			)
					__A             =			self.controlnet_cond_embedding(A			)
					sample += controlnet_cond

					# 3. down
					__A             =			(sample,)
					for down_block in self.down_blocks:
									if isinstance(A							,A			):
													__A      ,							__A             =			down_block(A							,A							,A							,deterministic=not train			)
									else:
													__A      ,							__A             =			down_block(A							,A							,deterministic=not train			)
									down_block_res_samples += res_samples

					# 4. mid
					__A             =			self.mid_block(A							,A							,A							,deterministic=not train			)

					# 5. contronet blocks
					__A             =			()
					for down_block_res_sample, controlnet_block in zip(A							,self.controlnet_down_blocks			):
									__A             =			controlnet_block(A			)
									controlnet_down_block_res_samples += (down_block_res_sample,)

					__A             =			controlnet_down_block_res_samples

					__A             =			self.controlnet_mid_block(A			)

					# 6. scaling
					__A             =			[sample * conditioning_scale for sample in down_block_res_samples]
					mid_block_res_sample *= conditioning_scale

					if not return_dict:
									return (down_block_res_samples, mid_block_res_sample)

					return FlaxControlNetOutput(
					    down_block_res_samples=A							,mid_block_res_sample=A			)


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			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.
					__A             =			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.
					__A             =			numpy.random.rand(
					    4							,3			)

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

					# Real output values provided.
					__A             =			output_array

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






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







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

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

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

					__A             =			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 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			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.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			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=1_0		,       give_loss=a_						)

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


if __name__ == "__main__":
					example()
