


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


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



class A__     ( _lowercase  ):



  """simple docstring"""



  def __init__( self			:  List[Any]				,				lowerCamelCase__			:  AutoencoderKL				,				lowerCamelCase__			:  CLIPTextModel				,				lowerCamelCase__			:  CLIPTokenizer				,				lowerCamelCase__			:  UNetaDConditionModel				,				lowerCamelCase__			:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,				lowerCamelCase__			:  StableDiffusionSafetyChecker				,				lowerCamelCase__			:  CLIPImageProcessor				,				):
        super().__init__()
        self.register_modules(
            vae=_snake_case				,				text_encoder=_snake_case				,				tokenizer=_snake_case				,				unet=_snake_case				,				scheduler=_snake_case				,				safety_checker=_snake_case				,				feature_extractor=_snake_case				,				)

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Optional[Union[str, int]] = "auto"       ):
        if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              a__	:  Dict     =   self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_snake_case       )

  def    _UpperCamelCase( self			:  Any       ):
        self.enable_attention_slicing(_snake_case       )

  @torch.no_grad()
  def __call__( self			:  Tuple				,				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__			:  List[str]				,				):

        if isinstance(_snake_case				,				_snake_case       ):
              a__	:  int     =   1
        elif isinstance(_snake_case				,				_snake_case       ):
              a__	:  Optional[Any]     =   len(_snake_case       )
        else:
              raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_snake_case       )}'''       )

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

        # get prompt text embeddings
        a__	:  int     =   self.tokenizer(
            _snake_case				,				padding="max_length"				,				max_length=self.tokenizer.model_max_length				,				return_tensors="pt"				,				)
        a__	:  str     =   text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              a__	:  Tuple     =   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__	:  Tuple     =   text_input_ids[:, : self.tokenizer.model_max_length]

        if text_embeddings is None:
              a__	:  Tuple     =   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__	:  str     =   text_embeddings.shape
        a__	:  List[str]     =   text_embeddings.repeat(1				,				_snake_case				,				1       )
        a__	:  List[str]     =   text_embeddings.view(bs_embed * num_images_per_prompt				,				_snake_case				,				-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__	:  List[str]     =   guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
              a__	:  Any     =   42
              if negative_prompt is None:
                    a__	:  Union[str, Any]     =   [""]
              elif type(_snake_case       ) is not type(_snake_case       ):
                    raise TypeError(
                        f'''`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case       )} !='''
                        f''' {type(_snake_case       )}.'''       )
              elif isinstance(_snake_case				,				_snake_case       ):
                    a__	:  Tuple     =   [negative_prompt]
              elif batch_size != len(_snake_case       ):
                    raise ValueError(
                        f'''`negative_prompt`: {negative_prompt} has batch size {len(_snake_case       )}, but `prompt`:'''
                        f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                        " the batch size of `prompt`."       )
              else:
                    a__	:  str     =   negative_prompt

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

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

        # set timesteps
        self.scheduler.set_timesteps(_snake_case       )

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

        # scale the initial noise by the standard deviation required by the scheduler
        a__	:  Optional[int]     =   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__	:  Dict     =   "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
        a__	:  int     =   {}
        if accepts_eta:
              a__	:  Optional[Any]     =   eta

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

              # predict the noise residual
              a__	:  Optional[Any]     =   self.unet(_snake_case				,				_snake_case				,				encoder_hidden_states=_snake_case       ).sample

              # perform guidance
              if do_classifier_free_guidance:
                    a__,	a__	:  List[str]     =   noise_pred.chunk(2       )
                    a__	:  Dict     =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              a__	:  Tuple     =   self.scheduler.step(_snake_case				,				_snake_case				,				_snake_case				,				**_snake_case       ).prev_sample

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

        a__	:  List[Any]     =   1 / 0.1_8215 * latents
        a__	:  Tuple     =   self.vae.decode(_snake_case       ).sample

        a__	:  Union[str, 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
        a__	:  List[str]     =   image.cpu().permute(0				,				2				,				3				,				1       ).float().numpy()

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

        if output_type == "pil":
              a__	:  Union[str, Any]     =   self.numpy_to_pil(_snake_case       )

        if not return_dict:
              return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=_snake_case				,				nsfw_content_detected=_snake_case       )



"""simple docstring"""





from __future__ import annotations
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										if resistor <= 0:
															A_								=    f'Resistor at index {index} has a negative or zero value!'
															raise ValueError(__a					)
										first_sum += 1 / float(__a					)
										index += 1
					return 1 / first_sum
def 				A_  (__a					):

					'''simple docstring'''





					A_								=    0.00
					A_								=    0
					for resistor in resistors:
										sum_r += resistor
										if resistor < 0:
															A_								=    f'Resistor at index {index} has a negative value!'
															raise ValueError(__a					)
										index += 1
					return sum_r


if __name__ == "__main__":
		import doctest

		doctest.testmod()




