


'''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


a__  :					Optional[int]        =       logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					lowercase_     (    SCREAMING_SNAKE_CASE__   ):


    def __init__(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a  ,					):
          super().__init__()
          self.register_modules(
              vae=A__  ,					text_encoder=A__  ,					tokenizer=A__  ,					unet=A__  ,					scheduler=A__  ,					safety_checker=A__  ,					feature_extractor=A__  ,					)


    def    __a							(     self  ,					a = "auto"    ):
          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(A__    )


    def    __a							(     self    ):
          self.enable_attention_slicing(A__    )


    @torch.no_grad()
    def __call__(     self  ,					a  ,					a = 5_12  ,					a = 5_12  ,					a = 50  ,					a = 7.5  ,					a = None  ,					a = 1  ,					a = 0.0  ,					a = None  ,					a = None  ,					a = "pil"  ,					a = True  ,					a = None  ,					a = 1  ,					a = None  ,					**a  ,					):

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

          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(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__    )}.'''    )

          # get prompt text embeddings
          UpperCamelCase__										=				self.tokenizer(
              A__  ,					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  ,					A__  ,					1    )
          UpperCamelCase__										=				text_embeddings.view(bs_embed * num_images_per_prompt  ,					A__  ,					-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(A__    ) is not type(A__    ):
                      raise TypeError(
                          f'''`negative_prompt` should be the same type to `prompt`, but got {type(A__    )} !='''
                          f''' {type(A__    )}.'''    )
                elif isinstance(A__  ,					A__    ):
                      UpperCamelCase__										=				[negative_prompt]
                elif batch_size != len(A__    ):
                      raise ValueError(
                          f'''`negative_prompt`: {negative_prompt} has batch size {len(A__    )}, 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(
                    A__  ,					padding="max_length"  ,					max_length=A__  ,					truncation=A__  ,					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(A__  ,					A__  ,					1    )
                UpperCamelCase__										=				uncond_embeddings.view(batch_size * num_images_per_prompt  ,					A__  ,					-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(
                          A__  ,					generator=A__  ,					device="cpu"  ,					dtype=A__    ).to(self.device    )
                      UpperCamelCase__										=				torch.randn(A__  ,					generator=A__  ,					device="cpu"  ,					dtype=A__    ).to(
                          self.device    )
                else:
                      UpperCamelCase__										=				torch.randn(
                          A__  ,					generator=A__  ,					device=self.device  ,					dtype=A__    )
                      UpperCamelCase__										=				torch.randn(A__  ,					generator=A__  ,					device=self.device  ,					dtype=A__    )
          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(A__    )

          # 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(A__    )    ):
                # 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(A__  ,					A__    )

                # predict the noise residual
                UpperCamelCase__										=				self.unet(A__  ,					A__  ,					encoder_hidden_states=A__    ).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(A__  ,					A__  ,					A__  ,					**A__    ).prev_sample

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

          UpperCamelCase__										=				1 / 0.1_8215 * latents
          UpperCamelCase__										=				self.vae.decode(A__    ).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(A__    )  ,					return_tensors="pt"    ).to(
                    self.device    )
                UpperCamelCase__					,	UpperCamelCase__										=				self.safety_checker(
                    images=A__  ,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
          else:
                UpperCamelCase__										=				None

          if output_type == "pil":
                UpperCamelCase__										=				self.numpy_to_pil(A__    )

          if not return_dict:
                return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A__  ,					nsfw_content_detected=A__    )







from __future__ import annotations

lowercase__		:Any   =    1.60_21E-19  # units = C



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			):
					'''simple docstring'''
					if (conductivity, electron_conc, mobility).count(0		) != 1:
										raise ValueError('''You cannot supply more or less than 2 values'''		)
					elif conductivity < 0:
										raise ValueError('''Conductivity cannot be negative'''		)
					elif electron_conc < 0:
										raise ValueError('''Electron concentration cannot be negative'''		)
					elif mobility < 0:
										raise ValueError('''mobility cannot be negative'''		)
					elif conductivity == 0:
										return (
										    "conductivity",
										    mobility * electron_conc * ELECTRON_CHARGE,
										)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    conductivity / (mobility * ELECTRON_CHARGE),
										)
					else:
										return (
										    "mobility",
										    conductivity / (electron_conc * ELECTRON_CHARGE),
										)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




