






"""simple docstring"""



import numpy as np
from matplotlib import pyplot as plt



def 						lowercase   (    __snake_case    :			str     ):
		return 1 / (1 + np.exp(-1 * x     ))





class  _UpperCAmelCase	:




					def __init__(    self					:						Any   ,						A					:						str   ,						A					:						str=None   ,						A					:						int=None   ,						A					:						Union[str, Any]=False       )  ->  Union[str, Any]:
							lowercase_			:			Optional[int]								=       units
							lowercase_			:			Any								=       None
							lowercase_			:			Any								=       None
							lowercase_			:			Dict								=       activation
							if learning_rate is None:
									lowercase_			:			List[Any]								=       0.3
							lowercase_			:			List[Any]								=       learning_rate
							lowercase_			:			Any								=       is_input_layer




					def   A  (    self					:						Tuple   ,						A					:						Dict       )  ->  Optional[int]:
							lowercase_			:			List[Any]								=       np.asmatrix(np.random.normal(0   ,						0.5   ,						(self.units, back_units)       )       )
							lowercase_			:			Dict								=       np.asmatrix(np.random.normal(0   ,						0.5   ,						self.units       )       ).T
							if self.activation is None:
									lowercase_			:			List[str]								=       sigmoid




					def   A  (    self					:						int       )  ->  Tuple:
							# activation function may be sigmoid or linear
							if self.activation == sigmoid:
									lowercase_			:			Optional[int]								=       np.dot(self.output   ,						(1 - self.output).T       )
									lowercase_			:			Any								=       np.diag(np.diag(A       )       )
							else:
									lowercase_			:			int								=       1
							return gradient_activation




					def   A  (    self					:						List[str]   ,						A					:						List[str]       )  ->  List[Any]:
							lowercase_			:			Optional[int]								=       xdata
							if self.is_input_layer:
									# input layer
									lowercase_			:			str								=       xdata
									lowercase_			:			Optional[int]								=       xdata
									return xdata
							else:
									lowercase_			:			List[str]								=       np.dot(self.weight   ,						self.xdata       ) - self.bias
									lowercase_			:			Tuple								=       self.activation(self.wx_plus_b       )
									return self.output






					def   A  (    self					:						List[str]   ,						A					:						Union[str, Any]       )  ->  str:
							lowercase_			:			List[Any]								=       self.cal_gradient()  # i * i 维
							lowercase_			:			str								=       np.asmatrix(np.dot(gradient.T   ,						A       )       )

							lowercase_			:			Dict								=       np.asmatrix(self.xdata       )
							lowercase_			:			int								=       -1
							lowercase_			:			Dict								=       self.weight

							lowercase_			:			List[Any]								=       np.dot(gradient.T   ,						self._gradient_weight.T       )
							lowercase_			:			int								=       gradient * self._gradient_bias
							lowercase_			:			int								=       np.dot(A   ,						self._gradient_x       ).T
							# upgrade: the Negative gradient direction
							lowercase_			:			Tuple								=       self.weight - self.learn_rate * self.gradient_weight
							lowercase_			:			List[str]								=       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  _UpperCAmelCase	:




					def __init__(    self					:						Dict       )  ->  Optional[int]:
							lowercase_			:			Optional[Any]								=       []
							lowercase_			:			Dict								=       []
							lowercase_			:			int								=       plt.figure()
							lowercase_			:			List[str]								=       self.fig_loss.add_subplot(1   ,						1   ,						1       )




					def   A  (    self					:						Dict   ,						A					:						List[Any]       )  ->  Optional[int]:
							self.layers.append(A       )




					def   A  (    self					:						str       )  ->  List[Any]:
							for i, layer in enumerate(self.layers[:]       ):
									if i < 1:
											lowercase_			:			List[str]								=       True
									else:
											layer.initializer(self.layers[i - 1].units       )




					def   A  (    self					:						List[str]       )  ->  Any:
							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   A  (    self					:						List[str]   ,						A					:						Tuple   ,						A					:						Optional[int]   ,						A					:						str   ,						A					:						Any       )  ->  List[Any]:
							lowercase_			:			List[Any]								=       train_round
							lowercase_			:			Union[str, Any]								=       accuracy

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

							lowercase_			:			Optional[Any]								=       np.shape(A       )
							for _ in range(A       ):
									lowercase_			:			Union[str, Any]								=       0
									for row in range(x_shape[0]       ):
											lowercase_			:			str								=       np.asmatrix(xdata[row, :]       ).T
											lowercase_			:			Union[str, Any]								=       np.asmatrix(ydata[row, :]       ).T

											# forward propagation
											for layer in self.layers:
													lowercase_			:			Optional[int]								=       layer.forward_propagation(_xdata       )

											lowercase_			:			List[str]								=       self.cal_loss(_ydata   ,						_xdata       )
											lowercase_			:			List[Any]								=       all_loss + loss

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

									lowercase_			:			Tuple								=       all_loss / x_shape[0]
									self.train_mse.append(A       )

									self.plot_loss()

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




					def   A  (    self					:						List[str]   ,						A					:						List[str]   ,						A					:						str       )  ->  Optional[int]:
							lowercase_			:			Tuple								=       np.sum(np.power((ydata - ydata_)   ,						2       )       )
							lowercase_			:			List[str]								=       2 * (ydata_ - ydata)
							# vector (shape is the same as _ydata.shape)
							return self.loss, self.loss_gradient






					def   A  (    self					:						List[str]       )  ->  Tuple:
							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 						lowercase   (    ):
		lowercase_			:			List[Any]								=       np.random.randn(1_0		, 1_0     )
		lowercase_			:			List[Any]								=       np.asarray(
		    [
		        [0.8, 0.4],
		        [0.4, 0.3],
		        [0.34, 0.45],
		        [0.67, 0.32],
		        [0.88, 0.67],
		        [0.78, 0.77],
		        [0.55, 0.66],
		        [0.55, 0.43],
		        [0.54, 0.1],
		        [0.1, 0.5],
		    ]     )
		lowercase_			:			List[Any]								=       BPNN()
		for i in (1_0, 2_0, 3_0, 2):
				model.add_layer(DenseLayer(__snake_case     )     )
		model.build()
		model.summary()
		model.train(xdata=__snake_case		, ydata=__snake_case		, train_round=1_0_0		, accuracy=0.01     )


if __name__ == "__main__":
						example()








"""simple docstring"""



from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__A		:	Union[str, Any]      =		logging.get_logger(__name__)  # pylint: disable=invalid-name


__A		:	str      =		'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''





class  _UpperCAmelCase	(			_A							):




					def __init__(    self					:						Union[str, Any]   ,						A					:						UNetaDModel   ,						A					:						CMStochasticIterativeScheduler       )  ->  None:
							super().__init__()

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

							lowercase_			:			Any								=       None




					def   A  (    self					:						Tuple   ,						A					:						Union[str, Any]=0       )  ->  Optional[Any]:
							if is_accelerate_available() and is_accelerate_version('''>='''   ,						'''0.14.0'''       ):
									from accelerate import cpu_offload
							else:
									raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''       )

							lowercase_			:			Any								=       torch.device(F'''cuda:{gpu_id}'''       )

							if self.device.type != "cpu":
									self.to('''cpu'''   ,						silence_dtype_warnings=A       )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							for cpu_offloaded_model in [self.unet]:
									cpu_offload(A   ,						A       )

							if self.safety_checker is not None:
									cpu_offload(self.safety_checker   ,						execution_device=A   ,						offload_buffers=A       )




					def   A  (    self					:						List[Any]   ,						A					:						Optional[Any]=0       )  ->  List[Any]:
							if is_accelerate_available() and is_accelerate_version('''>='''   ,						'''0.17.0.dev0'''       ):
									from accelerate import cpu_offload_with_hook
							else:
									raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''       )

							lowercase_			:			List[str]								=       torch.device(F'''cuda:{gpu_id}'''       )

							if self.device.type != "cpu":
									self.to('''cpu'''   ,						silence_dtype_warnings=A       )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowercase_			:			Union[str, Any]								=       None
							for cpu_offloaded_model in [self.unet]:
									lowercase_	, lowercase_			:			Optional[int]								=       cpu_offload_with_hook(A   ,						A   ,						prev_module_hook=A       )

							if self.safety_checker is not None:
									lowercase_	, lowercase_			:			Any								=       cpu_offload_with_hook(self.safety_checker   ,						A   ,						prev_module_hook=A       )

							# We'll offload the last model manually.
							lowercase_			:			str								=       hook




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   A  (    self					:						Any       )  ->  List[str]:
							if not hasattr(self.unet   ,						'''_hf_hook'''       ):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(A   ,						'''_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   A  (    self					:						List[str]   ,						A					:						int   ,						A					:						Union[str, Any]   ,						A					:						List[str]   ,						A					:						str   ,						A					:						int   ,						A					:						List[Any]   ,						A					:						Optional[Any]   ,						A					:						Optional[Any]=None       )  ->  Tuple:
							lowercase_			:			List[Any]								=       (batch_size, num_channels, height, width)
							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.'''       )

							if latents is None:
									lowercase_			:			Dict								=       randn_tensor(A   ,						generator=A   ,						device=A   ,						dtype=A       )
							else:
									lowercase_			:			Tuple								=       latents.to(device=A   ,						dtype=A       )

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase_			:			List[str]								=       latents * self.scheduler.init_noise_sigma
							return latents




					def   A  (    self					:						Tuple   ,						A					:						torch.FloatTensor   ,						A					:						str = "pil"       )  ->  Optional[int]:
							if output_type not in ["pt", "np", "pil"]:
									raise ValueError(
									    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''       )

							# Equivalent to diffusers.VaeImageProcessor.denormalize
							lowercase_			:			Dict								=       (sample / 2 + 0.5).clamp(0   ,						1       )
							if output_type == "pt":
									return sample

							# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
							lowercase_			:			List[str]								=       sample.cpu().permute(0   ,						2   ,						3   ,						1       ).numpy()
							if output_type == "np":
									return sample

							# Output_type must be 'pil'
							lowercase_			:			List[Any]								=       self.numpy_to_pil(A       )
							return sample




					def   A  (    self					:						Optional[int]   ,						A					:						Any   ,						A					:						Union[str, Any]   ,						A					:						Optional[int]=None       )  ->  List[Any]:
							if self.unet.config.num_class_embeds is not None:
									if isinstance(A   ,						A       ):
											lowercase_			:			Optional[int]								=       torch.tensor(A   ,						dtype=torch.int       )
									elif isinstance(A   ,						A       ):
											assert batch_size == 1, "Batch size must be 1 if classes is an int"
											lowercase_			:			int								=       torch.tensor([class_labels]   ,						dtype=torch.int       )
									elif class_labels is None:
											# Randomly generate batch_size class labels
											# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
											lowercase_			:			Any								=       torch.randint(0   ,						self.unet.config.num_class_embeds   ,						size=(batch_size,)       )
									lowercase_			:			List[str]								=       class_labels.to(A       )
							else:
									lowercase_			:			Union[str, Any]								=       None
							return class_labels




					def   A  (    self					:						Dict   ,						A					:						Union[str, Any]   ,						A					:						Any   ,						A					:						Optional[int]   ,						A					:						Union[str, Any]   ,						A					:						Any   ,						A					:						int       )  ->  Union[str, Any]:
							if num_inference_steps is None and timesteps is None:
									raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''       )

							if num_inference_steps is not None and timesteps is not None:
									logger.warning(
									    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
									    ''' `timesteps` will be used over `num_inference_steps`.'''       )

							if latents is not None:
									lowercase_			:			List[Any]								=       (batch_size, 3, img_size, img_size)
									if latents.shape != expected_shape:
											raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''       )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(A   ,						A       ) or callback_steps <= 0)
							):
									raise ValueError(
									    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
									    F''' {type(A       )}.'''       )






					@torch.no_grad()
					@replace_example_docstring(A       )
					def __call__(    self					:						Optional[Any]   ,						A					:						int = 1   ,						A					:						Optional[Union[torch.Tensor, List[int], int]] = None   ,						A					:						int = 1   ,						A					:						List[int] = None   ,						A					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						A					:						Optional[torch.FloatTensor] = None   ,						A					:						Optional[str] = "pil"   ,						A					:						bool = True   ,						A					:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,						A					:						int = 1   ,						)  ->  Tuple:
							lowercase_			:			Dict								=       self.unet.config.sample_size
							lowercase_			:			Union[str, Any]								=       self._execution_device

							# 1. Check inputs
							self.check_inputs(A   ,						A   ,						A   ,						A   ,						A   ,						A       )

							# 2. Prepare image latents
							# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
							lowercase_			:			Optional[int]								=       self.prepare_latents(
							    batch_size=A   ,						num_channels=self.unet.config.in_channels   ,						height=A   ,						width=A   ,						dtype=self.unet.dtype   ,						device=A   ,						generator=A   ,						latents=A   ,						)

							# 3. Handle class_labels for class-conditional models
							lowercase_			:			List[str]								=       self.prepare_class_labels(A   ,						A   ,						class_labels=A       )

							# 4. Prepare timesteps
							if timesteps is not None:
									self.scheduler.set_timesteps(timesteps=A   ,						device=A       )
									lowercase_			:			Optional[int]								=       self.scheduler.timesteps
									lowercase_			:			List[Any]								=       len(A       )
							else:
									self.scheduler.set_timesteps(A       )
									lowercase_			:			Any								=       self.scheduler.timesteps

							# 5. Denoising loop
							# Multistep sampling: implements Algorithm 1 in the paper
							with self.progress_bar(total=A       ) as progress_bar:
									for i, t in enumerate(A       ):
											lowercase_			:			List[str]								=       self.scheduler.scale_model_input(A   ,						A       )
											lowercase_			:			Optional[Any]								=       self.unet(A   ,						A   ,						class_labels=A   ,						return_dict=A       )[0]

											lowercase_			:			Tuple								=       self.scheduler.step(A   ,						A   ,						A   ,						generator=A       )[0]

											# call the callback, if provided
											progress_bar.update()
											if callback is not None and i % callback_steps == 0:
													callback(A   ,						A   ,						A       )

        # 6. Post-process image sample
							lowercase_			:			Union[str, Any]								=       self.postprocess_image(A   ,						output_type=A       )

							# Offload last model to CPU
							if hasattr(self   ,						'''final_offload_hook'''       ) and self.final_offload_hook is not None:
									self.final_offload_hook.offload()

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=A       )



