



'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

_UpperCamelCase							    =						'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=8	)				->				int:
  __lowerCamelCase	:	int   	=			height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __lowerCamelCase	:	Optional[Any]   	=			width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=512	,_lowerCAmelCase=512	)				->				Union[str, Any]:
  __lowerCamelCase	:	str   	=			pil_image.resize((w, h)	,resample=Image.BICUBIC	,reducing_gap=1	)
  __lowerCamelCase	:	List[Any]   	=			np.array(pil_image.convert('RGB'	)	)
  __lowerCamelCase	:	Dict   	=			arr.astype(np.floataa	) / 127.5 - 1
  __lowerCamelCase	:	Any   	=			np.transpose(SCREAMING_SNAKE_CASE_	,[2, 0, 1]	)
  __lowerCamelCase	:	Any   	=			torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	)
  return image






class  lowerCamelCase_   (    snake_case_					):
  """simple docstring"""




  def __init__(			self				:     Tuple      ,     _a				:     Tuple      ,     _a				:     Tuple      ,     _a				:     Dict      ,     ) -> Optional[int]:
    super().__init__()

    self.register_modules(
        unet=_a      ,     scheduler=_a      ,     movq=_a      ,     )
    __lowerCamelCase	:	Union[str, Any]   	=			2 ** (len(self.movq.config.block_out_channels       ) - 1)




  def 						_lowercase					(			self				:     Any      ,     _a				:     Optional[int]      ,     _a				:     str      ,     _a				:     str       ) -> Tuple:
    __lowerCamelCase	:	int   	=			min(int(num_inference_steps * strength       )      ,     _a       )

    __lowerCamelCase	:	Optional[Any]   	=			max(num_inference_steps - init_timestep      ,     0       )
    __lowerCamelCase	:	Dict   	=			self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     Optional[Any]      ,     _a				:     List[str]      ,     _a				:     List[str]      ,     _a				:     Union[str, Any]      ,     _a				:     Dict      ,     _a				:     Union[str, Any]      ,     _a				:     Any=None       ) -> Union[str, Any]:


    if not isinstance(_a      ,     (torch.Tensor, PIL.Image.Image, list)       ):
      raise ValueError(
          f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_a       )}'       )

    __lowerCamelCase	:	List[str]   	=			image.to(device=_a      ,     dtype=_a       )

    __lowerCamelCase	:	Optional[Any]   	=			batch_size * num_images_per_prompt

    if image.shape[1] == 4:
      __lowerCamelCase	:	Optional[Any]   	=			image

    else:
      if isinstance(_a      ,     _a       ) and len(_a       ) != batch_size:
        raise ValueError(
            f'You have passed a list of generators of length {len(_a       )}, but requested an effective batch'
            f' size of {batch_size}. Make sure the batch size matches the length of the generators.'       )

      elif isinstance(_a      ,     _a       ):
        __lowerCamelCase	:	Dict   	=			[
            self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_a       )
        ]
        __lowerCamelCase	:	Union[str, Any]   	=			torch.cat(_a      ,     dim=0       )
      else:
        __lowerCamelCase	:	Optional[int]   	=			self.movq.encode(_a       ).latent_dist.sample(_a       )

      __lowerCamelCase	:	List[str]   	=			self.movq.config.scaling_factor * init_latents

    __lowerCamelCase	:	Dict   	=			torch.cat([init_latents]      ,     dim=0       )

    __lowerCamelCase	:	Any   	=			init_latents.shape
    __lowerCamelCase	:	str   	=			randn_tensor(_a      ,     generator=_a      ,     device=_a      ,     dtype=_a       )

    # get latents
    __lowerCamelCase	:	Tuple   	=			self.scheduler.add_noise(_a      ,     _a      ,     _a       )

    __lowerCamelCase	:	Optional[Any]   	=			init_latents

    return latents




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     Optional[Any]=0       ) -> int:


    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'       )

    __lowerCamelCase	:	str   	=			torch.device(f'cuda:{gpu_id}'       )

    __lowerCamelCase	:	List[str]   	=			[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
        cpu_offload(_a      ,     _a       )




  def 						_lowercase					(			self				:     Dict      ,     _a				:     Tuple=0       ) -> Optional[int]:


    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.'       )

    __lowerCamelCase	:	int   	=			torch.device(f'cuda:{gpu_id}'       )

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

    __lowerCamelCase	:	Union[str, Any]   	=			None
    for cpu_offloaded_model in [self.unet, self.movq]:
      __lowerCamelCase	:	List[str]   	=			cpu_offload_with_hook(_a      ,     _a      ,     prev_module_hook=_a       )

    # We'll offload the last model manually.
    __lowerCamelCase	:	Tuple   	=			hook




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 						_lowercase					(			self				:     List[Any]       ) -> List[str]:


    if not hasattr(self.unet      ,     '_hf_hook'       ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a      ,     '_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(_a       )
  def __call__(			self				:     str      ,     _a				:     Union[str, Any]      ,     _a				:     Dict      ,     _a				:     List[str]      ,     _a				:     Any      ,     _a				:     str = 512      ,     _a				:     Optional[int] = 512      ,     _a				:     Any = 100      ,     _a				:     Any = 4.0      ,     _a				:     int = 0.3      ,     _a				:     Any = 1      ,     _a				:     Any = None      ,     _a				:     Union[str, Any] = "pil"      ,     _a				:     Optional[int] = True      ,     ) -> Union[str, Any]:
    __lowerCamelCase	:	List[str]   	=			self._execution_device

    __lowerCamelCase	:	str   	=			guidance_scale > 1.0

    if isinstance(_a      ,     _a       ):
      __lowerCamelCase	:	List[Any]   	=			torch.cat(_a      ,     dim=0       )
    if isinstance(_a      ,     _a       ):
      __lowerCamelCase	:	Union[str, Any]   	=			torch.cat(_a      ,     dim=0       )
    if isinstance(_a      ,     _a       ):
      __lowerCamelCase	:	Union[str, Any]   	=			torch.cat(_a      ,     dim=0       )

    __lowerCamelCase	:	str   	=			image_embeds.shape[0]

    if do_classifier_free_guidance:
      __lowerCamelCase	:	Optional[int]   	=			image_embeds.repeat_interleave(_a      ,     dim=0       )
      __lowerCamelCase	:	Union[str, Any]   	=			negative_image_embeds.repeat_interleave(_a      ,     dim=0       )
      __lowerCamelCase	:	Tuple   	=			hint.repeat_interleave(_a      ,     dim=0       )

    __lowerCamelCase	:	int   	=			torch.cat([negative_image_embeds, image_embeds]      ,     dim=0       ).to(dtype=self.unet.dtype      ,     device=_a       )
    __lowerCamelCase	:	List[str]   	=			torch.cat([hint, hint]      ,     dim=0       ).to(dtype=self.unet.dtype      ,     device=_a       )

    if not isinstance(_a      ,     _a       ):
      __lowerCamelCase	:	Union[str, Any]   	=			[image]
    if not all(isinstance(_a      ,     (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
      raise ValueError(
          f'Input is in incorrect format: {[type(_a       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'       )

    __lowerCamelCase	:	Dict   	=			torch.cat([prepare_image(_a      ,     _a      ,     _a       ) for i in image]      ,     dim=0       )
    __lowerCamelCase	:	List[str]   	=			image.to(dtype=image_embeds.dtype      ,     device=_a       )

    __lowerCamelCase	:	Optional[int]   	=			self.movq.encode(_a       )["latents"]
    __lowerCamelCase	:	List[Any]   	=			latents.repeat_interleave(_a      ,     dim=0       )
    self.scheduler.set_timesteps(_a      ,     device=_a       )
    __lowerCamelCase	:	List[Any]   	=			self.get_timesteps(_a      ,     _a      ,     _a       )
    __lowerCamelCase	:	Union[str, Any]   	=			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
    __lowerCamelCase	:	List[str]   	=			downscale_height_and_width(_a      ,     _a      ,     self.movq_scale_factor       )
    __lowerCamelCase	:	Union[str, Any]   	=			self.prepare_latents(
        _a      ,     _a      ,     _a      ,     _a      ,     image_embeds.dtype      ,     _a      ,     _a       )
    for i, t in enumerate(self.progress_bar(_a       )       ):
      # expand the latents if we are doing classifier free guidance
      __lowerCamelCase	:	Union[str, Any]   	=			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

      __lowerCamelCase	:	Tuple   	=			{"image_embeds": image_embeds, "hint": hint}
      __lowerCamelCase	:	Any   	=			self.unet(
          sample=_a      ,     timestep=_a      ,     encoder_hidden_states=_a      ,     added_cond_kwargs=_a      ,     return_dict=_a      ,     )[0]

      if do_classifier_free_guidance:
        __lowerCamelCase	:	List[str]   	=			noise_pred.split(latents.shape[1]      ,     dim=1       )
        __lowerCamelCase	:	Any   	=			noise_pred.chunk(2       )
        __lowerCamelCase	:	List[str]   	=			variance_pred.chunk(2       )
        __lowerCamelCase	:	List[Any]   	=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        __lowerCamelCase	:	Optional[Any]   	=			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"]
      ):
        __lowerCamelCase	:	Tuple   	=			noise_pred.split(latents.shape[1]      ,     dim=1       )

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

      __lowerCamelCase	:	Optional[Any]   	=			self.scheduler.step(
          _a      ,     _a      ,     _a      ,     generator=_a      ,     )[0]

    # post-processing
    __lowerCamelCase	:	Tuple   	=			self.movq.decode(_a      ,     force_not_quantize=_a       )["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"]:
      __lowerCamelCase	:	Union[str, Any]   	=			image * 0.5 + 0.5
      __lowerCamelCase	:	List[Any]   	=			image.clamp(0      ,     1       )
      __lowerCamelCase	:	str   	=			image.cpu().permute(0      ,     2      ,     3      ,     1       ).float().numpy()

    if output_type == "pil":
      __lowerCamelCase	:	List[str]   	=			self.numpy_to_pil(_a       )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a       )










'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




  def 						_lowercase					(			self				:     Tuple       ) -> bool:
    return (self.x**2 + self.y**2) <= 1




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




def 	a_					(							_lowerCAmelCase	)				->				float:
  if number_of_simulations < 1:
    raise ValueError('At least one simulation is necessary to estimate PI.'	)

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			Point.random_unit_square()

    if random_point.is_in_unit_circle():
      number_in_unit_circle += 1

  return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
   # import doctest

   # doctest.testmod()
   from math import pi

   _UpperCamelCase							    =						'Please enter the desired number of Monte Carlo simulations: '
   _UpperCamelCase							    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






