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


class 			lowercase_		(      A       ):


       def __init__(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       )						->    Tuple:
              super().__init__()
              self.register_modules(
                  vae=__A						,       text_encoder=__A						,       tokenizer=__A						,       unet=__A						,       scheduler=__A						,       safety_checker=__A						,       feature_extractor=__A						,       )


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


       def 	_snake_case				(    self				)						->    str:
              self.enable_attention_slicing(__A				)





       @torch.no_grad()
       def __call__(    self						,       __A						,       __A = 512						,       __A = 512						,       __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						,       )						->    Tuple:

              if isinstance(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=1
              elif isinstance(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=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
              SCREAMING_SNAKE_CASE_							:				Dict		=self.tokenizer(
                  __A						,       padding='''max_length'''						,       max_length=self.tokenizer.model_max_length						,       return_tensors='''pt'''						,       )
              SCREAMING_SNAKE_CASE_							:				List[Any]		=text_inputs.input_ids

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

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

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

                     SCREAMING_SNAKE_CASE_							:				Tuple		=text_input_ids.shape[-1]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.tokenizer(
                         __A						,       padding='''max_length'''						,       max_length=__A						,       truncation=__A						,       return_tensors='''pt'''						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

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

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

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

                     # predict the noise residual
                     SCREAMING_SNAKE_CASE_							:				int		=self.unet(__A						,       __A						,       encoder_hidden_states=__A				).sample

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

                     # compute the previous noisy sample x_t -> x_t-1
                     SCREAMING_SNAKE_CASE_							:				str		=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				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=1 / 0.18_215 * latents
              SCREAMING_SNAKE_CASE_							:				int		=self.vae.decode(__A				).sample

              SCREAMING_SNAKE_CASE_							:				Tuple		=(image / 2 + 0.5).clamp(0						,       1				)

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

              if self.safety_checker is not None:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.feature_extractor(self.numpy_to_pil(__A				)						,       return_tensors='''pt'''				).to(
                         self.device				)
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				int		=self.safety_checker(
                         images=__A						,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype				)				)
              else:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=None

              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image, has_nsfw_concept)

              return StableDiffusionPipelineOutput(images=__A						,       nsfw_content_detected=__A				)


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
