



"""simple docstring"""
import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


__SCREAMING_SNAKE_CASE     :  int										=					logging.get_logger(__name__)  # pylint: disable=invalid-name



def 				lowerCAmelCase_(      self   :							str  ,      lowercase_   :							str  ,      lowercase_   :							Union[str, Any]  ,      lowercase_   :							int  ,      lowercase_   :							List[str]							)		->				Optional[int]:
				_lowerCamelCase					     = image.to(device=lowercase_							)
				_lowerCamelCase					     = image  # take image as image_embeddings
				_lowerCamelCase					     = image_embeddings.unsqueeze(1							)

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = image_embeddings.shape
				_lowerCamelCase					     = image_embeddings.repeat(1  ,      lowercase_  ,      1							)
				_lowerCamelCase					     = image_embeddings.view(bs_embed * num_images_per_prompt  ,      lowercase_  ,      -1							)

				if do_classifier_free_guidance:
								_lowerCamelCase					     = torch.zeros_like(lowercase_							)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								_lowerCamelCase					     = torch.cat([uncond_embeddings, image_embeddings]							)

				return image_embeddings




class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = None ,				):
									super().__init__()

									_lowerCamelCase					     = {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									_lowerCamelCase					     = decoder_pipe_kwargs.get('''torch_dtype''' ,				lowerCamelCase__      ) or prior.dtype

									_lowerCamelCase					     = StableDiffusionImageVariationPipeline.from_pretrained(
									    '''lambdalabs/sd-image-variations-diffusers''' ,				**lowerCamelCase__      )

									# replace `_encode_image` method
									_lowerCamelCase					     = types.MethodType(_encode_image ,				self.decoder_pipe      )

									self.register_modules(
									    prior=lowerCamelCase__ ,				tokenizer=lowerCamelCase__ ,				text_encoder=lowerCamelCase__ ,				prior_scheduler=lowerCamelCase__ ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				):
									if text_model_output is None:
													_lowerCamelCase					     = len(lowerCamelCase__      ) if isinstance(lowerCamelCase__ ,				lowerCamelCase__      ) else 1
													# get prompt text embeddings
													_lowerCamelCase					     = self.tokenizer(
													    lowerCamelCase__ ,				padding='''max_length''' ,				max_length=self.tokenizer.model_max_length ,				return_tensors='''pt''' ,				)
													_lowerCamelCase					     = text_inputs.input_ids
													_lowerCamelCase					     = text_inputs.attention_mask.bool().to(lowerCamelCase__      )

													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	_lowerCamelCase					     = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
																	logger.warning(
																	    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																	    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""      )
																	_lowerCamelCase					     = text_input_ids[:, : self.tokenizer.model_max_length]

													_lowerCamelCase					     = self.text_encoder(text_input_ids.to(lowerCamelCase__      )      )

													_lowerCamelCase					     = text_encoder_output.text_embeds
													_lowerCamelCase					     = text_encoder_output.last_hidden_state

									else:
													_lowerCamelCase					     = text_model_output[0].shape[0]
													_lowerCamelCase     ,   _lowerCamelCase					     = text_model_output[0], text_model_output[1]
													_lowerCamelCase					     = text_attention_mask

									_lowerCamelCase					     = text_embeddings.repeat_interleave(lowerCamelCase__ ,				dim=0      )
									_lowerCamelCase					     = text_encoder_hidden_states.repeat_interleave(lowerCamelCase__ ,				dim=0      )
									_lowerCamelCase					     = text_mask.repeat_interleave(lowerCamelCase__ ,				dim=0      )

									if do_classifier_free_guidance:
													_lowerCamelCase					     = [''''''] * batch_size

													_lowerCamelCase					     = self.tokenizer(
													    lowerCamelCase__ ,				padding='''max_length''' ,				max_length=self.tokenizer.model_max_length ,				truncation=lowerCamelCase__ ,				return_tensors='''pt''' ,				)
													_lowerCamelCase					     = uncond_input.attention_mask.bool().to(lowerCamelCase__      )
													_lowerCamelCase					     = self.text_encoder(uncond_input.input_ids.to(lowerCamelCase__      )      )

													_lowerCamelCase					     = uncond_embeddings_text_encoder_output.text_embeds
													_lowerCamelCase					     = uncond_embeddings_text_encoder_output.last_hidden_state

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

													_lowerCamelCase					     = uncond_embeddings.shape[1]
													_lowerCamelCase					     = uncond_embeddings.repeat(1 ,				lowerCamelCase__      )
													_lowerCamelCase					     = uncond_embeddings.view(batch_size * num_images_per_prompt ,				lowerCamelCase__      )

													_lowerCamelCase					     = uncond_text_encoder_hidden_states.shape[1]
													_lowerCamelCase					     = uncond_text_encoder_hidden_states.repeat(1 ,				lowerCamelCase__ ,				1      )
													_lowerCamelCase					     = uncond_text_encoder_hidden_states.view(
													    batch_size * num_images_per_prompt ,				lowerCamelCase__ ,				-1      )
													_lowerCamelCase					     = uncond_text_mask.repeat_interleave(lowerCamelCase__ ,				dim=0      )

													# done duplicates

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													_lowerCamelCase					     = torch.cat([uncond_embeddings, text_embeddings]      )
													_lowerCamelCase					     = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]      )

													_lowerCamelCase					     = torch.cat([uncond_text_mask, text_mask]      )

									return text_embeddings, text_encoder_hidden_states, text_mask

					@property
					def 	snake_case__					(			self      ):
									if self.device != torch.device('''meta'''      ) or not hasattr(self.prior ,				'''_hf_hook'''      ):
													return self.device
									for module in self.prior.modules():
													if (
													    hasattr(lowerCamelCase__ ,				'''_hf_hook'''      )
													    and hasattr(module._hf_hook ,				'''execution_device'''      )
													    and module._hf_hook.execution_device is not None
													):
																	return torch.device(module._hf_hook.execution_device      )
									return self.device

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									if latents is None:
													_lowerCamelCase					     = randn_tensor(lowerCamelCase__ ,				generator=lowerCamelCase__ ,				device=lowerCamelCase__ ,				dtype=lowerCamelCase__      )
									else:
													if latents.shape != shape:
																	raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
													_lowerCamelCase					     = latents.to(lowerCamelCase__      )

									_lowerCamelCase					     = latents * scheduler.init_noise_sigma
									return latents

					def 	snake_case__					(			self ,				lowerCamelCase__ = None      ):
									self.decoder_pipe.to(lowerCamelCase__      )
									super().to(lowerCamelCase__      )





					@torch.no_grad()
					def __call__(			self ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = 1 ,				lowerCamelCase__ = 2_5 ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = 4.0 ,				lowerCamelCase__ = 8.0 ,				lowerCamelCase__ = 5_0 ,				lowerCamelCase__ = 1 ,				lowerCamelCase__ = 0.0 ,				lowerCamelCase__ = "pil" ,				lowerCamelCase__ = True ,				):
									if prompt is not None:
													if isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	_lowerCamelCase					     = 1
													elif isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	_lowerCamelCase					     = len(lowerCamelCase__      )
													else:
																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__      )}"""      )
									else:
													_lowerCamelCase					     = text_model_output[0].shape[0]

									_lowerCamelCase					     = self._execution_device

									_lowerCamelCase					     = batch_size * num_images_per_prompt

									_lowerCamelCase					     = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = self._encode_prompt(
									    lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )

									# prior

									self.prior_scheduler.set_timesteps(lowerCamelCase__ ,				device=lowerCamelCase__      )
									_lowerCamelCase					     = self.prior_scheduler.timesteps

									_lowerCamelCase					     = self.prior.config.embedding_dim

									_lowerCamelCase					     = self.prepare_latents(
									    (batch_size, embedding_dim) ,				text_embeddings.dtype ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				self.prior_scheduler ,				)

									for i, t in enumerate(self.progress_bar(lowerCamelCase__      )      ):
													# expand the latents if we are doing classifier free guidance
													_lowerCamelCase					     = torch.cat([prior_latents] * 2      ) if do_classifier_free_guidance else prior_latents

													_lowerCamelCase					     = self.prior(
													    lowerCamelCase__ ,				timestep=lowerCamelCase__ ,				proj_embedding=lowerCamelCase__ ,				encoder_hidden_states=lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				).predicted_image_embedding

													if do_classifier_free_guidance:
																	_lowerCamelCase     ,   _lowerCamelCase					     = predicted_image_embedding.chunk(2      )
																	_lowerCamelCase					     = predicted_image_embedding_uncond + prior_guidance_scale * (
																	    predicted_image_embedding_text - predicted_image_embedding_uncond
																	)

													if i + 1 == prior_timesteps_tensor.shape[0]:
																	_lowerCamelCase					     = None
													else:
																	_lowerCamelCase					     = prior_timesteps_tensor[i + 1]

													_lowerCamelCase					     = self.prior_scheduler.step(
													    lowerCamelCase__ ,				timestep=lowerCamelCase__ ,				sample=lowerCamelCase__ ,				generator=lowerCamelCase__ ,				prev_timestep=lowerCamelCase__ ,				).prev_sample

									_lowerCamelCase					     = self.prior.post_process_latents(lowerCamelCase__      )

									_lowerCamelCase					     = prior_latents

									_lowerCamelCase					     = self.decoder_pipe(
									    image=lowerCamelCase__ ,				height=lowerCamelCase__ ,				width=lowerCamelCase__ ,				num_inference_steps=lowerCamelCase__ ,				guidance_scale=lowerCamelCase__ ,				generator=lowerCamelCase__ ,				output_type=lowerCamelCase__ ,				return_dict=lowerCamelCase__ ,				num_images_per_prompt=lowerCamelCase__ ,				eta=lowerCamelCase__ ,				)
									return output





"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = units
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = activation
									if learning_rate is None:
													_lowerCamelCase					     = 0.3
									_lowerCamelCase					     = learning_rate
									_lowerCamelCase					     = is_input_layer

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				(self.units, back_units)      )      )
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				self.units      )      ).T
									if self.activation is None:
													_lowerCamelCase					     = sigmoid

					def 	snake_case__					(			self      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = xdata
									if self.is_input_layer:
													# input layer
													_lowerCamelCase					     = xdata
													_lowerCamelCase					     = xdata
													return xdata
									else:
													_lowerCamelCase					     = np.dot(self.weight ,				self.xdata      ) - self.bias
													_lowerCamelCase					     = self.activation(self.wx_plus_b      )
													return self.output





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = np.asmatrix(self.xdata      )
									_lowerCamelCase					     = -1
									_lowerCamelCase					     = self.weight

									_lowerCamelCase					     = np.dot(gradient.T ,				self._gradient_weight.T      )
									_lowerCamelCase					     = gradient * self._gradient_bias
									_lowerCamelCase					     = np.dot(lowerCamelCase__ ,				self._gradient_x      ).T
									# upgrade: the Negative gradient direction
									_lowerCamelCase					     = self.weight - self.learn_rate * self.gradient_weight
									_lowerCamelCase					     = self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = []
									_lowerCamelCase					     = []
									_lowerCamelCase					     = plt.figure()
									_lowerCamelCase					     = self.fig_loss.add_subplot(1 ,				1 ,				1      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													if i < 1:
																	_lowerCamelCase					     = True
													else:
																	layer.initializer(self.layers[i - 1].units      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													print(F"""------- layer {i} -------"""      )
													print('''weight.shape ''' ,				np.shape(layer.weight      )      )
													print('''bias.shape ''' ,				np.shape(layer.bias      )      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

									self.ax_loss.hlines(self.accuracy ,				0 ,				self.train_round * 1.1      )

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_lowerCamelCase					     = 0
													for row in range(x_shape[0]      ):
																	_lowerCamelCase					     = np.asmatrix(xdata[row, :]      ).T
																	_lowerCamelCase					     = np.asmatrix(ydata[row, :]      ).T

																	# forward propagation
																	for layer in self.layers:
																					_lowerCamelCase					     = layer.forward_propagation(_xdata      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = self.cal_loss(_ydata ,				_xdata      )
																	_lowerCamelCase					     = all_loss + loss

																	# back propagation: the input_layer does not upgrade
																	for layer in self.layers[:0:-1]:
																					_lowerCamelCase					     = layer.back_propagation(lowerCamelCase__      )

													_lowerCamelCase					     = all_loss / x_shape[0]
													self.train_mse.append(lowerCamelCase__      )

													self.plot_loss()

													if mse < self.accuracy:
																	print('''----达到精度----'''      )
																	return mse
									return None

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.sum(np.power((ydata - ydata_) ,				2      )      )
									_lowerCamelCase					     = 2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient





					def 	snake_case__					(			self      ):
									if self.ax_loss.lines:
													self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
									self.ax_loss.plot(self.train_mse ,				'''r-'''      )
									plt.ion()
									plt.xlabel('''step'''      )
									plt.ylabel('''loss'''      )
									plt.show()
									plt.pause(0.1      )



def 				lowerCAmelCase_(      )		->				Optional[Any]:
				_lowerCamelCase					     = np.random.randn(10  ,      10							)
				_lowerCamelCase					     = np.asarray(
				    [
				        [0.8, 0.4],
				        [0.4, 0.3],
				        [0.3_4, 0.4_5],
				        [0.6_7, 0.3_2],
				        [0.8_8, 0.6_7],
				        [0.7_8, 0.7_7],
				        [0.5_5, 0.6_6],
				        [0.5_5, 0.4_3],
				        [0.5_4, 0.1],
				        [0.1, 0.5],
				    ]							)
				_lowerCamelCase					     = BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
