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







class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Optional[Any]				,   lowerCamelCase			:    AutoencoderKL				,   lowerCamelCase			:    CLIPTextModel				,   lowerCamelCase			:    CLIPTokenizer				,   lowerCamelCase			:    UNetaDConditionModel				,   lowerCamelCase			:    Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,   lowerCamelCase			:    StableDiffusionSafetyChecker				,   lowerCamelCase			:    CLIPImageProcessor				,   ):



   """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 		lowercase_(   self			:    Tuple				,   lowerCamelCase			:    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		:     str												=		self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(lowerCamelCase       )
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   self.enable_attention_slicing(lowerCamelCase       )






 @torch.no_grad()
 def __call__(   self			:    List[Any]				,   lowerCamelCase			:    Union[str, List[str]]				,   lowerCamelCase			:    int = 5_12				,   lowerCamelCase			:    int = 5_12				,   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			:    Optional[Any]				,   ):



   """simple docstring"""





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

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

   if text_embeddings is None:
     __A		:     int												=		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		:     Dict												=		text_embeddings.repeat(1				,   lowerCamelCase				,   1       )
   __A		:     Dict												=		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.
   __A		:     Tuple												=		guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     __A		:     List[str]
     if negative_prompt is None:
       __A		:     List[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       ):
       __A		:     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:
       __A		:     Optional[Any]												=		negative_prompt

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

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

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

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

     # predict the noise residual
     __A		:     Union[str, Any]												=		self.unet(lowerCamelCase				,   lowerCamelCase				,   encoder_hidden_states=lowerCamelCase       ).sample

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

     # compute the previous noisy sample x_t -> x_t-1
     __A		:     Optional[int]												=		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       )

   __A		:     Union[str, Any]												=		1 / 0.18_215 * latents
   __A		:     List[str]												=		self.vae.decode(lowerCamelCase       ).sample

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

   if self.safety_checker is not None:
     __A		:     Union[str, Any]												=		self.feature_extractor(self.numpy_to_pil(lowerCamelCase       )				,   return_tensors="""pt"""       ).to(
         self.device       )
     __A							,					__A		:     Any												=		self.safety_checker(
         images=lowerCamelCase				,   clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
   else:
     __A		:     List[str]												=		None

   if output_type == "pil":
     __A		:     List[Any]												=		self.numpy_to_pil(lowerCamelCase       )

   if not return_dict:
     return (image, has_nsfw_concept)

   return StableDiffusionPipelineOutput(images=lowerCamelCase				,   nsfw_content_detected=lowerCamelCase       )





'''simple docstring'''
from collections.abc import Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



