



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


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







class 		snake_case						(	UpperCAmelCase__  ):



			'''simple docstring'''






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

				"""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      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Optional[Union[str, int]] = "auto")				->				Tuple:

				"""simple docstring"""







				if slice_size == "auto":
					# half the attention head size is usually a good trade-off between
					# speed and memory
					_snake_case    :  Union[str, Any]					=					self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowerCAmelCase)

			def      UpperCamelCase_  (					self							:			Optional[Any])				->				List[str]:

				"""simple docstring"""







				self.enable_attention_slicing(lowerCAmelCase)


			@torch.no_grad()
			def __call__(					self							:			Optional[int]				,		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							:			Tuple				,		)				->				Optional[Any]:

				"""simple docstring"""







				if isinstance(lowerCAmelCase				,		lowerCAmelCase):
					_snake_case    :  int					=					1
				elif isinstance(lowerCAmelCase				,		lowerCAmelCase):
					_snake_case    :  str					=					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
				_snake_case    :  int					=					self.tokenizer(
				    lowerCAmelCase				,		padding="""max_length"""				,		max_length=self.tokenizer.model_max_length				,		return_tensors="""pt"""				,		)
				_snake_case    :  List[str]					=					text_inputs.input_ids

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					_snake_case    :  Optional[int]					=					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}''')
					_snake_case    :  Optional[Any]					=					text_input_ids[:, : self.tokenizer.model_max_length]

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

				# duplicate text embeddings for each generation per prompt, using mps friendly method
				_snake_case    :  Dict					=					text_embeddings.shape
				_snake_case    :  Any					=					text_embeddings.repeat(1				,		lowerCAmelCase				,		1)
				_snake_case    :  Any					=					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.
				_snake_case    :  int					=					guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
					_snake_case    :  List[str]
					if negative_prompt is None:
						_snake_case    :  Union[str, Any]					=					[""""""]
					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):
						_snake_case    :  Any					=					[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:
						_snake_case    :  Optional[int]					=					negative_prompt

					_snake_case    :  List[Any]					=					text_input_ids.shape[-1]
					_snake_case    :  Tuple					=					self.tokenizer(
					    lowerCAmelCase				,		padding="""max_length"""				,		max_length=lowerCAmelCase				,		truncation=lowerCAmelCase				,		return_tensors="""pt"""				,		)
					_snake_case    :  Optional[int]					=					self.text_encoder(uncond_input.input_ids.to(self.device))[0]

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					_snake_case    :  Union[str, Any]					=					uncond_embeddings.shape[1]
					_snake_case    :  Tuple					=					uncond_embeddings.repeat(lowerCAmelCase				,		lowerCAmelCase				,		1)
					_snake_case    :  Tuple					=					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
					_snake_case    :  Tuple					=					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`.
				_snake_case    :  Tuple					=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				_snake_case    :  int					=					(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				_snake_case    :  str					=					text_embeddings.dtype
				if latents is None:
					if self.device.type == "mps":
						# randn does not exist on mps
						_snake_case    :  int					=					torch.randn(
						    lowerCAmelCase				,		generator=lowerCAmelCase				,		device="""cpu"""				,		dtype=lowerCAmelCase).to(self.device)
						_snake_case    :  Union[str, Any]					=					torch.randn(lowerCAmelCase				,		generator=lowerCAmelCase				,		device="""cpu"""				,		dtype=lowerCAmelCase).to(
						    self.device)
					else:
						_snake_case    :  List[str]					=					torch.randn(
						    lowerCAmelCase				,		generator=lowerCAmelCase				,		device=self.device				,		dtype=lowerCAmelCase)
						_snake_case    :  List[str]					=					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}''')
					_snake_case    :  List[str]					=					latents_reference.to(self.device)
					_snake_case    :  Optional[int]					=					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
				_snake_case    :  int					=					(latents_shape[3] - latents_shape_reference[3]) // 2
				_snake_case    :  Optional[int]					=					(latents_shape[2] - latents_shape_reference[2]) // 2
				_snake_case    :  List[Any]					=					latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				_snake_case    :  Optional[Any]					=					latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				_snake_case    :  Any					=					0 if dx < 0 else dx
				_snake_case    :  Tuple					=					0 if dy < 0 else dy
				_snake_case    :  Optional[Any]					=					max(-dx				,		0)
				_snake_case    :  int					=					max(-dy				,		0)
				# import pdb
				# pdb.set_trace()
				_snake_case    :  Optional[Any]					=					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
				_snake_case    :  List[Any]					=					self.scheduler.timesteps.to(self.device)

				# scale the initial noise by the standard deviation required by the scheduler
				_snake_case    :  Tuple					=					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]
				_snake_case    :  Dict					=					"""eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
				_snake_case    :  Any					=					{}
				if accepts_eta:
					_snake_case    :  Union[str, Any]					=					eta

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

					# predict the noise residual
					_snake_case    :  Any					=					self.unet(lowerCAmelCase				,		lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase).sample

					# perform guidance
					if do_classifier_free_guidance:
						_snake_case    :  List[Any]					=					noise_pred.chunk(2)
						_snake_case    :  int					=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

					# compute the previous noisy sample x_t -> x_t-1
					_snake_case    :  Dict					=					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)

				_snake_case    :  Union[str, Any]					=					1 / 0.18_215 * latents
				_snake_case    :  Dict					=					self.vae.decode(lowerCAmelCase).sample

				_snake_case    :  Optional[Any]					=					(image / 2 + 0.5).clamp(0				,		1)

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

				if self.safety_checker is not None:
					_snake_case    :  Optional[int]					=					self.feature_extractor(self.numpy_to_pil(lowerCAmelCase)				,		return_tensors="""pt""").to(
					    self.device)
					_snake_case    :  Tuple					=					self.safety_checker(
					    images=lowerCAmelCase				,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
				else:
					_snake_case    :  int					=					None

				if output_type == "pil":
					_snake_case    :  Tuple					=					self.numpy_to_pil(lowerCAmelCase)

				if not return_dict:
					return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowerCAmelCase				,		nsfw_content_detected=lowerCAmelCase)





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a__    =	1.0_54_57_18_17E-34  # unit of ℏ : J * s

a__    =	3E8  # unit of c : m * s^-1



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				,       SCREAMING_SNAKE_CASE__       :	float				)  ->     dict[str, float]:

 if (force, area, distance).count(0				) != 1:
  raise ValueError("""One and only one argument must be 0"""				)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""				)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""				)
 if area < 0:
  raise ValueError("""Area can not be negative"""				)
 if force == 0:
  _snake_case    :  Union[str, Any]					=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  _snake_case    :  Optional[Any]					=					(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  _snake_case    :  str					=					(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""				)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()
