




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,
)


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

_snake_case            = "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_=8  ):
      _A							:  Dict    =		height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A							:  int    =		width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 		lowercase							(     UpperCamelCase__	):

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

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

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Dict:
           if latents is None:
                 _A							:  Any    =		randn_tensor(_a	,  generator=_a	,  device=_a	,  dtype=_a       )
           else:
                 if latents.shape != shape:
                       raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
                 _A							:  List[Any]    =		latents.to(_a       )

           _A							:  Any    =		latents * scheduler.init_noise_sigma
           return latents

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

           _A							:  Any    =		torch.device(F'''cuda:{gpu_id}'''       )

           _A							:  Optional[Any]    =		[
               self.unet,
               self.movq,
           ]
           for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                       cpu_offload(_a	,  _a       )

     def       a__			(						self	,  _a=0       )		->							Any:
           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."""       )

           _A							:  List[str]    =		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)

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

           # We'll offload the last model manually.
           _A							:  List[str]    =		hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def       a__			(						self       )		->							Optional[Any]:
           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	,  _a	,  _a	,  _a	,  _a = 512	,  _a = 512	,  _a = 100	,  _a = 4.0	,  _a = 1	,  _a = None	,  _a = None	,  _a = "pil"	,  _a = True	,  )		->							Optional[int]:
           _A							:  Optional[int]    =		self._execution_device

           _A							:  Any    =		guidance_scale > 1.0

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

           _A							:  List[str]    =		image_embeds.shape[0] * num_images_per_prompt

           if do_classifier_free_guidance:
                 _A							:  Any    =		image_embeds.repeat_interleave(_a	,  dim=0       )
                 _A							:  Dict    =		negative_image_embeds.repeat_interleave(_a	,  dim=0       )
                 _A							:  Union[str, Any]    =		hint.repeat_interleave(_a	,  dim=0       )

           _A							:  Dict    =		torch.cat([negative_image_embeds, image_embeds]	,  dim=0       ).to(dtype=self.unet.dtype	,  device=_a       )
           _A							:  Union[str, Any]    =		torch.cat([hint, hint]	,  dim=0       ).to(dtype=self.unet.dtype	,  device=_a       )
           self.scheduler.set_timesteps(_a	,  device=_a       )
           _A							:  int    =		self.scheduler.timesteps

           _A							:  str    =		self.movq.config.latent_channels

           _A							:  int    =		downscale_height_and_width(_a	,  _a	,  self.movq_scale_factor       )

           # create initial latent
           _A							:  Union[str, Any]    =		self.prepare_latents(
               (batch_size, num_channels_latents, height, width)	,  image_embeds.dtype	,  _a	,  _a	,  _a	,  self.scheduler	,  )

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

                 _A							:  List[str]    =		{"""image_embeds""": image_embeds, """hint""": hint}
                 _A							:  Dict    =		self.unet(
                     sample=_a	,  timestep=_a	,  encoder_hidden_states=_a	,  added_cond_kwargs=_a	,  return_dict=_a	,  )[0]

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

                 # compute the previous noisy sample x_t -> x_t-1
                 _A							:  Optional[int]    =		self.scheduler.step(
                     _a	,  _a	,  _a	,  generator=_a	,  )[0]
           # post-processing
           _A							:  str    =		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"]:
                 _A							:  Optional[Any]    =		image * 0.5 + 0.5
                 _A							:  Union[str, Any]    =		image.clamp(0	,  1       )
                 _A							:  List[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       ).float().numpy()

           if output_type == "pil":
                 _A							:  int    =		self.numpy_to_pil(_a       )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_a       )










from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> bool:
            _A							:  List[str]    =		sqrt((x**2) + (y**2)  )
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      _A							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		proportion * 4
      print(f'''The estimated value of pi is {pi_estimate}'''  )
      print(f'''The numpy value of pi is {pi}'''  )
      print(f'''The total error is {abs(pi - pi_estimate  )}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(max_value * max_value - min_value * min_value) / 2

      print("""******************"""  )
      print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {expected_value}'''  )
      print(f'''Total error is {abs(estimated_value - expected_value  )}'''  )
      print("""******************"""  )







def        lowerCAmelCase_			(  snake_case_  ):

      def function_to_integrate(snake_case_  ) -> float:
            return sqrt(4.0 - x * x  )

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,0.0,2.0  )

      print("""******************"""  )
      print("""Estimating pi using area_under_curve_estimator"""  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {pi}'''  )
      print(f'''Total error is {abs(estimated_value - pi  )}'''  )
      print("""******************"""  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

