






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


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




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


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			Optional[Union[str, int]] = "auto"				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         self.enable_attention_slicing(UpperCamelCase_				)





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


         """simple docstring"""



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

         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(UpperCamelCase_ ,	UpperCamelCase_				) or callback_steps <= 0)
         ):
             raise ValueError(
                 F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 F" {type(UpperCamelCase_				)}."				)

         # get prompt text embeddings
         __A         =	self.tokenizer(
             UpperCamelCase_ ,	padding="""max_length""" ,	max_length=self.tokenizer.model_max_length ,	return_tensors="""pt""" ,	)
         __A         =	text_inputs.input_ids

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

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

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         __A    ,	__A    ,	__A         =	text_embeddings.shape
         __A         =	text_embeddings.repeat(1 ,	UpperCamelCase_ ,	1				)
         __A         =	text_embeddings.view(bs_embed * num_images_per_prompt ,	UpperCamelCase_ ,	-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.
         __A         =	guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
             __A         =	42
             if negative_prompt is None:
                 __A         =	[""""""]
             elif type(UpperCamelCase_				) is not type(UpperCamelCase_				):
                 raise TypeError(
                     F"`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_				)} !="
                     F" {type(UpperCamelCase_				)}."				)
             elif isinstance(UpperCamelCase_ ,	UpperCamelCase_				):
                 __A         =	[negative_prompt]
             elif batch_size != len(UpperCamelCase_				):
                 raise ValueError(
                     F"`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_				)}, but `prompt`:"
                     F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                     """ the batch size of `prompt`."""				)
             else:
                 __A         =	negative_prompt

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

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             __A         =	uncond_embeddings.shape[1]
             __A         =	uncond_embeddings.repeat(UpperCamelCase_ ,	UpperCamelCase_ ,	1				)
             __A         =	uncond_embeddings.view(batch_size * num_images_per_prompt ,	UpperCamelCase_ ,	-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
             __A         =	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`.
         __A         =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         __A         =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         __A         =	text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":
                 # randn does not exist on mps
                 __A         =	torch.randn(
                     UpperCamelCase_ ,	generator=UpperCamelCase_ ,	device="""cpu""" ,	dtype=UpperCamelCase_				).to(self.device				)
                 __A         =	torch.randn(UpperCamelCase_ ,	generator=UpperCamelCase_ ,	device="""cpu""" ,	dtype=UpperCamelCase_				).to(
                     self.device				)
             else:
                 __A         =	torch.randn(
                     UpperCamelCase_ ,	generator=UpperCamelCase_ ,	device=self.device ,	dtype=UpperCamelCase_				)
                 __A         =	torch.randn(UpperCamelCase_ ,	generator=UpperCamelCase_ ,	device=self.device ,	dtype=UpperCamelCase_				)
         else:
             if latents_reference.shape != latents_shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"				)
             __A         =	latents_reference.to(self.device				)
             __A         =	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
         __A         =	(latents_shape[3] - latents_shape_reference[3]) // 2
         __A         =	(latents_shape[2] - latents_shape_reference[2]) // 2
         __A         =	latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         __A         =	latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         __A         =	0 if dx < 0 else dx
         __A         =	0 if dy < 0 else dy
         __A         =	max(-dx ,	0				)
         __A         =	max(-dy ,	0				)
         # import pdb
         # pdb.set_trace()
         __A         =	latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(UpperCamelCase_				)

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

         # scale the initial noise by the standard deviation required by the scheduler
         __A         =	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]
         __A         =	"""eta""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
         __A         =	{}
         if accepts_eta:
             __A         =	eta

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

             # predict the noise residual
             __A         =	self.unet(UpperCamelCase_ ,	UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_				).sample

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

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

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

         __A         =	1 / 0.18215 * latents
         __A         =	self.vae.decode(UpperCamelCase_				).sample

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

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

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

         if output_type == "pil":
             __A         =	self.numpy_to_pil(UpperCamelCase_				)

         if not return_dict:
             return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=UpperCamelCase_ ,	nsfw_content_detected=UpperCamelCase_				)














__a      :       List[Any]				  =   8.3_1_4_4_5_9_8



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			float			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    if temperature < 0:
        raise Exception("""Temperature cannot be less than 0 K"""    )
    if molar_mass <= 0:
        raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""    )
    else:
        return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # example
  __a      :       int				  =   300
  __a      :       int				  =   28
  __a      :       int				  =   rms_speed_of_molecule(temperature, molar_mass)
  print(f"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")




