


"""simple docstring"""







from typing import List, Optional, Union

import torch

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


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

a__				:							List[str]						=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=8   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__SCREAMING_SNAKE_CASE						   =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				UNetaDConditionModel			,			UpperCAmelCase__			:				DDPMScheduler			,			UpperCAmelCase__			:				VQModel			,			)       ->	Tuple:
						super().__init__()

						self.register_modules(
						    unet=UpperCAmelCase__			,			scheduler=UpperCAmelCase__			,			movq=UpperCAmelCase__			,			)
						__SCREAMING_SNAKE_CASE						   =				2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Tuple   )       ->	Optional[Any]:
						if latents is None:
											__SCREAMING_SNAKE_CASE						   =				randn_tensor(UpperCAmelCase__			,			generator=UpperCAmelCase__			,			device=UpperCAmelCase__			,			dtype=UpperCAmelCase__   )
						else:
											if latents.shape != shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
											__SCREAMING_SNAKE_CASE						   =				latents.to(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				latents * scheduler.init_noise_sigma
						return latents




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				Optional[Any]=0   )       ->	Union[str, Any]:
						if is_accelerate_available():
											from accelerate import cpu_offload
						else:
											raise ImportError("Please install accelerate via `pip install accelerate`"   )

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

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




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				int=0   )       ->	Tuple:
						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."   )

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

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

						__SCREAMING_SNAKE_CASE						   =				None
						for cpu_offloaded_model in [self.unet, self.movq]:
											__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				cpu_offload_with_hook(UpperCAmelCase__			,			UpperCAmelCase__			,			prev_module_hook=UpperCAmelCase__   )

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




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	List[Any]:
						if not hasattr(self.unet			,			"_hf_hook"   ):
											return self.device
						for module in self.unet.modules():
											if (
											    hasattr(UpperCAmelCase__			,			"_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(UpperCAmelCase__   )
	def __call__( self			:				int			,			UpperCAmelCase__			:				Union[torch.FloatTensor, List[torch.FloatTensor]]			,			UpperCAmelCase__			:				Union[torch.FloatTensor, List[torch.FloatTensor]]			,			UpperCAmelCase__			:				torch.FloatTensor			,			UpperCAmelCase__			:				int = 5_1_2			,			UpperCAmelCase__			:				int = 5_1_2			,			UpperCAmelCase__			:				int = 1_0_0			,			UpperCAmelCase__			:				float = 4.0			,			UpperCAmelCase__			:				int = 1			,			UpperCAmelCase__			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			UpperCAmelCase__			:				Optional[torch.FloatTensor] = None			,			UpperCAmelCase__			:				Optional[str] = "pil"			,			UpperCAmelCase__			:				bool = True			,			)       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE						   =				self._execution_device

						__SCREAMING_SNAKE_CASE						   =				guidance_scale > 1.0

						if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				torch.cat(UpperCAmelCase__			,			dim=0   )
						if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				torch.cat(UpperCAmelCase__			,			dim=0   )
						if isinstance(UpperCAmelCase__			,			UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				torch.cat(UpperCAmelCase__			,			dim=0   )

						__SCREAMING_SNAKE_CASE						   =				image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
											__SCREAMING_SNAKE_CASE						   =				image_embeds.repeat_interleave(UpperCAmelCase__			,			dim=0   )
											__SCREAMING_SNAKE_CASE						   =				negative_image_embeds.repeat_interleave(UpperCAmelCase__			,			dim=0   )
											__SCREAMING_SNAKE_CASE						   =				hint.repeat_interleave(UpperCAmelCase__			,			dim=0   )

						__SCREAMING_SNAKE_CASE						   =				torch.cat([negative_image_embeds, image_embeds]			,			dim=0   ).to(dtype=self.unet.dtype			,			device=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				torch.cat([hint, hint]			,			dim=0   ).to(dtype=self.unet.dtype			,			device=UpperCAmelCase__   )
						self.scheduler.set_timesteps(UpperCAmelCase__			,			device=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				self.scheduler.timesteps

						__SCREAMING_SNAKE_CASE						   =				self.movq.config.latent_channels

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				downscale_height_and_width(UpperCAmelCase__			,			UpperCAmelCase__			,			self.movq_scale_factor   )

						# create initial latent
						__SCREAMING_SNAKE_CASE						   =				self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)			,			image_embeds.dtype			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			self.scheduler			,			)

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

											__SCREAMING_SNAKE_CASE						   =				{"image_embeds": image_embeds, "hint": hint}
											__SCREAMING_SNAKE_CASE						   =				self.unet(
											    sample=UpperCAmelCase__			,			timestep=UpperCAmelCase__			,			encoder_hidden_states=UpperCAmelCase__			,			added_cond_kwargs=UpperCAmelCase__			,			return_dict=UpperCAmelCase__			,			)[0]

											if do_classifier_free_guidance:
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				noise_pred.split(latents.shape[1]			,			dim=1   )
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				noise_pred.chunk(2   )
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				variance_pred.chunk(2   )
																__SCREAMING_SNAKE_CASE						   =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																__SCREAMING_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"]
											):
																__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				noise_pred.split(latents.shape[1]			,			dim=1   )

											# compute the previous noisy sample x_t -> x_t-1
											__SCREAMING_SNAKE_CASE						   =				self.scheduler.step(
											    UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			generator=UpperCAmelCase__			,			)[0]
						# post-processing
						__SCREAMING_SNAKE_CASE						   =				self.movq.decode(UpperCAmelCase__			,			force_not_quantize=UpperCAmelCase__   )["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"]:
											__SCREAMING_SNAKE_CASE						   =				image * 0.5 + 0.5
											__SCREAMING_SNAKE_CASE						   =				image.clamp(0			,			1   )
											__SCREAMING_SNAKE_CASE						   =				image.cpu().permute(0			,			2			,			3			,			1   ).float().numpy()

						if output_type == "pil":
											__SCREAMING_SNAKE_CASE						   =				self.numpy_to_pil(UpperCAmelCase__   )

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=UpperCAmelCase__   )







'''simple docstring'''




import string
from math import logaa
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   document.translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       ).replace("\n"			,     ""       )
     _UpperCAmelCase     							=   document_without_punctuation.split(" "       )  # word tokenization
     return len([word for word in tokenize_document if word.lower() == term.lower()]       )
def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      str       )		->       tuple[int, int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   corpus.lower().translate(
         str.maketrans(""			,     ""			,     string.punctuation       )       )  # strip all punctuation and replace it with ''
     _UpperCAmelCase     							=   corpus_without_punctuation.split("\n"       )
     _UpperCAmelCase     							=   term.lower()
     return (len([doc for doc in docs if term in doc]       ), len(__lowercase       ))
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int			,     __lowercase							:      Union[str, Any]=False       )		->       float:


     '''simple docstring'''



     if smoothing:
          if n == 0:
               raise ValueError("log10(0) is undefined."       )
          return round(1 + logaa(n / (1 + df)       )			,     3       )

     if df == 0:
          raise ZeroDivisionError("df must be > 0"       )
     elif n == 0:
          raise ValueError("log10(0) is undefined."       )
     return round(logaa(n / df       )			,     3       )






def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      int       )		->       float:


     '''simple docstring'''



     return round(tf * idf			,     3       )




