





from __future__ import annotations







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   list[str]:
  if partitions <= 0:
    raise ValueError("""partitions must be a positive number!""")
  if partitions > number_of_bytes:
    raise ValueError("""partitions can not > number_of_bytes!""")
  __snake_case:						Tuple			  =						number_of_bytes // partitions
  __snake_case:						int			  =						[]
  for i in range(SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						i * bytes_per_partition + 1
    __snake_case:						Dict			  =						(
        number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
    )
    allocation_list.append(F'''{start_bytes}-{end_bytes}''')
  return allocation_list


if __name__ == "__main__":
      import doctest

      doctest.testmod()













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







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







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




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




       def 							UpperCAmelCase__			(  self			:   str		):
         self.enable_attention_slicing(A		)






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

         if isinstance(A							,    A		):
           __snake_case:						int			  =						1
         elif isinstance(A							,    A		):
           __snake_case:						Optional[Any]			  =						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
         __snake_case:						Tuple			  =						self.tokenizer(
             A							,    padding="""max_length"""							,    max_length=self.tokenizer.model_max_length							,    return_tensors="""pt"""							,    )
         __snake_case:						Any			  =						text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           __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}'''		)
           __snake_case:						Dict			  =						text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
           __snake_case:						Union[str, Any]			  =						self.text_encoder(text_input_ids.to(self.device		)		)[0]

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

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

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

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

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

           # predict the noise residual
           __snake_case:						List[Any]			  =						self.unet(A							,    A							,    encoder_hidden_states=A		).sample

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

           # compute the previous noisy sample x_t -> x_t-1
           __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		)

         __snake_case:						Optional[int]			  =						1 / 0.1_8215 * latents
         __snake_case:						List[Any]			  =						self.vae.decode(A		).sample

         __snake_case:						str			  =						(image / 2 + 0.5).clamp(0							,    1		)

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

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

         if output_type == "pil":
           __snake_case:						Tuple			  =						self.numpy_to_pil(A		)

         if not return_dict:
           return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=A							,    nsfw_content_detected=A		)






