




'''simple docstring'''

from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


a    :							Union[str, Any]										=    logging.get_logger(__name__)  # pylint: disable=invalid-name

a    :							Tuple										=    '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=8      )						->   str:

					'''simple docstring'''

					snake_case_		     =							h // scale_factor**2
					if h % scale_factor**2 != 0:
										new_h += 1
					snake_case_		     =							w // scale_factor**2
					if w % scale_factor**2 != 0:
										new_w += 1
					return new_h * scale_factor, new_w * scale_factor






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   MultilingualCLIP			,							lowercase_       :   XLMRobertaTokenizer			,							lowercase_       :   UNetaDConditionModel			,							lowercase_       :   Union[DDIMScheduler, DDPMScheduler]			,							lowercase_       :   VQModel			,							):
									super().__init__()

									self.register_modules(
									    text_encoder=lowercase_			,							tokenizer=lowercase_			,							unet=lowercase_			,							scheduler=lowercase_			,							movq=lowercase_			,							)
									snake_case_		     =							2 ** (len(self.movq.config.block_out_channels   ) - 1)


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   List[str]			,							lowercase_       :   Dict			,							lowercase_       :   str			,							lowercase_       :   Any   ):
									if latents is None:
														snake_case_		     =							randn_tensor(lowercase_			,							generator=lowercase_			,							device=lowercase_			,							dtype=lowercase_   )
									else:
														if latents.shape != shape:
																			raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
														snake_case_		     =							latents.to(lowercase_   )

									snake_case_		     =							latents * scheduler.init_noise_sigma
									return latents


				def     A_					(						self       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Dict=None			,							):
									snake_case_		     =							len(lowercase_   ) if isinstance(lowercase_			,							lowercase_   ) else 1
									# get prompt text embeddings
									snake_case_		     =							self.tokenizer(
									    lowercase_			,							padding='''max_length'''			,							truncation=lowercase_			,							max_length=77			,							return_attention_mask=lowercase_			,							add_special_tokens=lowercase_			,							return_tensors='''pt'''			,							)

									snake_case_		     =							text_inputs.input_ids
									snake_case_		     =							self.tokenizer(lowercase_			,							padding='''longest'''			,							return_tensors='''pt'''   ).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase_			,							lowercase_   ):
														snake_case_		     =							self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
														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_		     =							text_input_ids.to(lowercase_   )
									snake_case_		     =							text_inputs.attention_mask.to(lowercase_   )

									snake_case_     ,snake_case_		     =							self.text_encoder(
									    input_ids=lowercase_			,							attention_mask=lowercase_   )

									snake_case_		     =							prompt_embeds.repeat_interleave(lowercase_			,							dim=0   )
									snake_case_		     =							text_encoder_hidden_states.repeat_interleave(lowercase_			,							dim=0   )
									snake_case_		     =							text_mask.repeat_interleave(lowercase_			,							dim=0   )

									if do_classifier_free_guidance:
														snake_case_		     =							42
														if negative_prompt is None:
																			snake_case_		     =							[''''''] * batch_size
														elif type(lowercase_   ) is not type(lowercase_   ):
																			raise TypeError(
																			    F"`negative_prompt` should be the same type to `prompt`, but got {type(lowercase_   )} !="
																			    F" {type(lowercase_   )}."   )
														elif isinstance(lowercase_			,							lowercase_   ):
																			snake_case_		     =							[negative_prompt]
														elif batch_size != len(lowercase_   ):
																			raise ValueError(
																			    F"`negative_prompt`: {negative_prompt} has batch size {len(lowercase_   )}, 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_		     =							negative_prompt

														snake_case_		     =							self.tokenizer(
														    lowercase_			,							padding='''max_length'''			,							max_length=77			,							truncation=lowercase_			,							return_attention_mask=lowercase_			,							add_special_tokens=lowercase_			,							return_tensors='''pt'''			,							)
														snake_case_		     =							uncond_input.input_ids.to(lowercase_   )
														snake_case_		     =							uncond_input.attention_mask.to(lowercase_   )

														snake_case_     ,snake_case_		     =							self.text_encoder(
														    input_ids=lowercase_			,							attention_mask=lowercase_   )

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

														snake_case_		     =							negative_prompt_embeds.shape[1]
														snake_case_		     =							negative_prompt_embeds.repeat(1			,							lowercase_   )
														snake_case_		     =							negative_prompt_embeds.view(batch_size * num_images_per_prompt			,							lowercase_   )

														snake_case_		     =							uncond_text_encoder_hidden_states.shape[1]
														snake_case_		     =							uncond_text_encoder_hidden_states.repeat(1			,							lowercase_			,							1   )
														snake_case_		     =							uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt			,							lowercase_			,							-1   )
														snake_case_		     =							uncond_text_mask.repeat_interleave(lowercase_			,							dim=0   )

														# done duplicates

														# 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_		     =							torch.cat([negative_prompt_embeds, prompt_embeds]   )
														snake_case_		     =							torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

														snake_case_		     =							torch.cat([uncond_text_mask, text_mask]   )

									return prompt_embeds, text_encoder_hidden_states, text_mask


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Any=0   ):
									if is_accelerate_available():
														from accelerate import cpu_offload
									else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

									snake_case_		     =							torch.device(F"cuda:{gpu_id}"   )

									snake_case_		     =							[
									    self.unet,
									    self.text_encoder,
									    self.movq,
									]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   str=0   ):
									if is_accelerate_available() and is_accelerate_version('''>='''			,							'''0.17.0.dev0'''   ):
														from accelerate import cpu_offload_with_hook
									else:
														raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

									snake_case_		     =							torch.device(F"cuda:{gpu_id}"   )

									if self.device.type != "cpu":
														self.to('''cpu'''			,							silence_dtype_warnings=lowercase_   )
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									snake_case_		     =							None
									for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
														snake_case_     ,snake_case_		     =							cpu_offload_with_hook(lowercase_			,							lowercase_			,							prev_module_hook=lowercase_   )

									if self.safety_checker is not None:
														snake_case_     ,snake_case_		     =							cpu_offload_with_hook(self.safety_checker			,							lowercase_			,							prev_module_hook=lowercase_   )

									# We'll offload the last model manually.
									snake_case_		     =							hook


				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def     A_					(						self       :   Union[str, Any]   ):
									if not hasattr(self.unet			,							'''_hf_hook'''   ):
														return self.device
									for module in self.unet.modules():
														if (
														    hasattr(lowercase_			,							'''_hf_hook'''   )
														    and hasattr(module._hf_hook			,							'''execution_device'''   )
														    and module._hf_hook.execution_device is not None
														):
																			return torch.device(module._hf_hook.execution_device   )
									return self.device


				@torch.no_grad()
				@replace_example_docstring(lowercase_   )
				def __call__(						self       :   Optional[int]			,							lowercase_       :   Union[str, List[str]]			,							lowercase_       :   Union[torch.FloatTensor, List[torch.FloatTensor]]			,							lowercase_       :   Union[torch.FloatTensor, List[torch.FloatTensor]]			,							lowercase_       :   Optional[Union[str, List[str]]] = None			,							lowercase_       :   int = 512			,							lowercase_       :   int = 512			,							lowercase_       :   int = 100			,							lowercase_       :   float = 4.0			,							lowercase_       :   int = 1			,							lowercase_       :   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,							lowercase_       :   Optional[torch.FloatTensor] = None			,							lowercase_       :   Optional[str] = "pil"			,							lowercase_       :   bool = True			,							):

									if isinstance(lowercase_			,							lowercase_   ):
														snake_case_		     =							1
									elif isinstance(lowercase_			,							lowercase_   ):
														snake_case_		     =							len(lowercase_   )
									else:
														raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}"   )

									snake_case_		     =							self._execution_device

									snake_case_		     =							batch_size * num_images_per_prompt
									snake_case_		     =							guidance_scale > 1.0

									snake_case_     ,snake_case_     ,snake_case_		     =							self._encode_prompt(
									    lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_   )

									if isinstance(lowercase_			,							lowercase_   ):
														snake_case_		     =							torch.cat(lowercase_			,							dim=0   )
									if isinstance(lowercase_			,							lowercase_   ):
														snake_case_		     =							torch.cat(lowercase_			,							dim=0   )

									if do_classifier_free_guidance:
														snake_case_		     =							image_embeds.repeat_interleave(lowercase_			,							dim=0   )
														snake_case_		     =							negative_image_embeds.repeat_interleave(lowercase_			,							dim=0   )

									snake_case_		     =							torch.cat([negative_image_embeds, image_embeds]			,							dim=0   ).to(
									    dtype=prompt_embeds.dtype			,							device=lowercase_   )

									self.scheduler.set_timesteps(lowercase_			,							device=lowercase_   )
									snake_case_		     =							self.scheduler.timesteps

									snake_case_		     =							self.unet.config.in_channels

									snake_case_     ,snake_case_		     =							get_new_h_w(lowercase_			,							lowercase_			,							self.movq_scale_factor   )

									# create initial latent
									snake_case_		     =							self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)			,							text_encoder_hidden_states.dtype			,							lowercase_			,							lowercase_			,							lowercase_			,							self.scheduler			,							)

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

														snake_case_		     =							{'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
														snake_case_		     =							self.unet(
														    sample=lowercase_			,							timestep=lowercase_			,							encoder_hidden_states=lowercase_			,							added_cond_kwargs=lowercase_			,							return_dict=lowercase_			,							)[0]

														if do_classifier_free_guidance:
																			snake_case_     ,snake_case_		     =							noise_pred.split(latents.shape[1]			,							dim=1   )
																			snake_case_     ,snake_case_		     =							noise_pred.chunk(2   )
																			snake_case_     ,snake_case_		     =							variance_pred.chunk(2   )
																			snake_case_		     =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			snake_case_		     =							torch.cat([noise_pred, variance_pred_text]			,							dim=1   )

														if not (
														    hasattr(self.scheduler.config			,							'''variance_type'''   )
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																			snake_case_     ,snake_case_		     =							noise_pred.split(latents.shape[1]			,							dim=1   )

														# compute the previous noisy sample x_t -> x_t-1
														snake_case_		     =							self.scheduler.step(
														    lowercase_			,							lowercase_			,							lowercase_			,							generator=lowercase_			,							).prev_sample
									# post-processing
									snake_case_		     =							self.movq.decode(lowercase_			,							force_not_quantize=lowercase_   )['''sample''']

									if output_type not in ["pt", "np", "pil"]:
														raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"   )

									if output_type in ["np", "pil"]:
														snake_case_		     =							image * 0.5 + 0.5
														snake_case_		     =							image.clamp(0			,							1   )
														snake_case_		     =							image.cpu().permute(0			,							2			,							3			,							1   ).float().numpy()

									if output_type == "pil":
														snake_case_		     =							self.numpy_to_pil(lowercase_   )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=lowercase_   )







'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

					def brightness(__UpperCAmelCase      ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


if __name__ == "__main__":
							# Load image
							with Image.open('image_data/lena.jpg') as img:
														# Change brightness to 100
														a    :							int										=    change_brightness(img, 100)
														brigt_img.save('image_data/lena_brightness.png', format='png')

