






import math

from numpy import inf
from scipy.integrate import quad







def     a_   (__A      )				->						float:



					"""simple docstring"""

					if num <= 0:
										raise ValueError("math domain error"      )

					return quad(__A				,       0				,       __A				,       args=(__A)      )[0]







def     a_   (__A				,       __A      )				->						float:



					"""simple docstring"""
					return math.pow(__A				,       z - 1      ) * math.exp(-x      )


if __name__ == "__main__":
			from doctest import testmod

			testmod()












'''simple docstring'''

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:     List[str]			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						AutoencoderKL			,					lowerCAmelCase__  :						CLIPTextModel			,					lowerCAmelCase__  :						CLIPTokenizer			,					lowerCAmelCase__  :						UNetaDConditionModel			,					lowerCAmelCase__  :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,					lowerCAmelCase__  :						StableDiffusionSafetyChecker			,					lowerCAmelCase__  :						CLIPImageProcessor			,					)       ->	Optional[Any]:

            '''simple docstring'''




            super().__init__()
            self.register_modules(
                vae=lowerCAmelCase__			,					text_encoder=lowerCAmelCase__			,					tokenizer=lowerCAmelCase__			,					unet=lowerCAmelCase__			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Union[str, int]] = "auto"	)       ->	Union[str, Any]:

            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 _UpperCamelCase							       =			self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            self.enable_attention_slicing(lowerCAmelCase__	)







       @torch.no_grad()
       def __call__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, List[str]]			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 50			,					lowerCAmelCase__  :						float = 7.5			,					lowerCAmelCase__  :						Optional[Union[str, List[str]]] = None			,					lowerCAmelCase__  :						Optional[int] = 1			,					lowerCAmelCase__  :						float = 0.0			,					lowerCAmelCase__  :						Optional[torch.Generator] = None			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					lowerCAmelCase__  :						Optional[str] = "pil"			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,					lowerCAmelCase__  :						int = 1			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					**lowerCAmelCase__  :						Optional[int]			,					)       ->	Any:

            '''simple docstring'''




            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			1
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			len(lowerCAmelCase__	)
            else:
                 raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__	)}"""	)

            if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

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

            # get prompt text embeddings
            _UpperCamelCase							       =			self.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=self.tokenizer.model_max_length			,					return_tensors='''pt'''			,					)
            _UpperCamelCase							       =			text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _UpperCamelCase							       =			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}"""	)
                 _UpperCamelCase							       =			text_input_ids[:, : self.tokenizer.model_max_length]

            if text_embeddings is None:
                 _UpperCamelCase							       =			self.text_encoder(text_input_ids.to(self.device	)	)[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			text_embeddings.shape
            _UpperCamelCase							       =			text_embeddings.repeat(1			,					lowerCAmelCase__			,					1	)
            _UpperCamelCase							       =			text_embeddings.view(bs_embed * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            _UpperCamelCase							       =			guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 _UpperCamelCase							       =			42
                 if negative_prompt is None:
                      _UpperCamelCase							       =			['''''']
                 elif type(lowerCAmelCase__	) is not type(lowerCAmelCase__	):
                      raise TypeError(
                          f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__	)} !="""
                          f""" {type(lowerCAmelCase__	)}."""	)
                 elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                      _UpperCamelCase							       =			[negative_prompt]
                 elif batch_size != len(lowerCAmelCase__	):
                      raise ValueError(
                          f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__	)}, but `prompt`:"""
                          f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                          ''' the batch size of `prompt`.'''	)
                 else:
                      _UpperCamelCase							       =			negative_prompt

                 _UpperCamelCase							       =			text_input_ids.shape[-1]
                 _UpperCamelCase							       =			self.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					return_tensors='''pt'''			,					)
                 _UpperCamelCase							       =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _UpperCamelCase							       =			uncond_embeddings.shape[1]
                 _UpperCamelCase							       =			uncond_embeddings.repeat(lowerCAmelCase__			,					lowerCAmelCase__			,					1	)
                 _UpperCamelCase							       =			uncond_embeddings.view(batch_size * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

                 # 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
                 _UpperCamelCase							       =			torch.cat([uncond_embeddings, text_embeddings]	)

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
            _UpperCamelCase							       =			text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(self.device	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(
                          self.device	)
                 else:
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
            else:
                 if latents_reference.shape != latents_shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
                 _UpperCamelCase							       =			latents_reference.to(self.device	)
                 _UpperCamelCase							       =			latents.to(self.device	)

            # This is the key part of the pipeline where we
            # try to ensure that the generated images w/ the same seed
            # but different sizes actually result in similar images
            _UpperCamelCase							       =			(latents_shape[3] - latents_shape_reference[3]) // 2
            _UpperCamelCase							       =			(latents_shape[2] - latents_shape_reference[2]) // 2
            _UpperCamelCase							       =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
            _UpperCamelCase							       =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
            _UpperCamelCase							       =			0 if dx < 0 else dx
            _UpperCamelCase							       =			0 if dy < 0 else dy
            _UpperCamelCase							       =			max(-dx			,					0	)
            _UpperCamelCase							       =			max(-dy			,					0	)
            # import pdb
            # pdb.set_trace()
            _UpperCamelCase							       =			latents_reference[:, :, dy : dy + h, dx : dx + w]

            # set timesteps
            self.scheduler.set_timesteps(lowerCAmelCase__	)

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            _UpperCamelCase							       =			self.scheduler.timesteps.to(self.device	)

            # scale the initial noise by the standard deviation required by the scheduler
            _UpperCamelCase							       =			latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _UpperCamelCase							       =			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
            _UpperCamelCase							       =			{}
            if accepts_eta:
                 _UpperCamelCase							       =			eta

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

                 # predict the noise residual
                 _UpperCamelCase							       =			self.unet(lowerCAmelCase__			,					lowerCAmelCase__			,					encoder_hidden_states=lowerCAmelCase__	).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                      _UpperCamelCase       ,							_UpperCamelCase							       =			noise_pred.chunk(2	)
                      _UpperCamelCase							       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase							       =			self.scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					**lowerCAmelCase__	).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                      callback(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			1 / 0.18215 * latents
            _UpperCamelCase							       =			self.vae.decode(lowerCAmelCase__	).sample

            _UpperCamelCase							       =			(image / 2 + 0.5).clamp(0			,					1	)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            _UpperCamelCase							       =			image.cpu().permute(0			,					2			,					3			,					1	).float().numpy()

            if self.safety_checker is not None:
                 _UpperCamelCase							       =			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__	)			,					return_tensors='''pt'''	).to(
                     self.device	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			self.safety_checker(
                     images=lowerCAmelCase__			,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
            else:
                 _UpperCamelCase							       =			None

            if output_type == "pil":
                 _UpperCamelCase							       =			self.numpy_to_pil(lowerCAmelCase__	)

            if not return_dict:
                 return (image, has_nsfw_concept)

            return StableDiffusionPipelineOutput(images=lowerCAmelCase__			,					nsfw_content_detected=lowerCAmelCase__	)



