





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,
)


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

_lowerCamelCase      							=   '\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  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_=8   ):
       _lowerCamelCase				:  List[str]						=		height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _lowerCamelCase				:  Tuple						=		width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__   ,				):



             """simple docstring"""





             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 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             if latents is None:
                    _lowerCamelCase				:  Optional[int]						=		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}""")
                    _lowerCamelCase				:  Union[str, Any]						=		latents.to(a__)

             _lowerCamelCase				:  Optional[int]						=		latents * scheduler.init_noise_sigma
             return latents


      def 					__snake_case      (		self   ,				a__=0):



             """simple docstring"""





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

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

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


      def 					__snake_case      (		self   ,				a__=0):



             """simple docstring"""





             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				:  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)

             _lowerCamelCase				:  Optional[int]						=		None
             for cpu_offloaded_model in [self.unet, self.movq]:
                    _lowerCamelCase,	_lowerCamelCase				:  List[Any]						=		cpu_offload_with_hook(a__   ,				a__   ,				prev_module_hook=a__)

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


      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 					__snake_case      (		self):



             """simple docstring"""





             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   ,				):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self._execution_device

             _lowerCamelCase				:  Optional[int]						=		guidance_scale > 1.0

             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Optional[Any]						=		torch.cat(a__   ,				dim=0)
             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  str						=		torch.cat(a__   ,				dim=0)
             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Optional[Any]						=		torch.cat(a__   ,				dim=0)

             _lowerCamelCase				:  Optional[int]						=		image_embeds.shape[0] * num_images_per_prompt

             if do_classifier_free_guidance:
                    _lowerCamelCase				:  List[Any]						=		image_embeds.repeat_interleave(a__   ,				dim=0)
                    _lowerCamelCase				:  Optional[int]						=		negative_image_embeds.repeat_interleave(a__   ,				dim=0)
                    _lowerCamelCase				:  List[Any]						=		hint.repeat_interleave(a__   ,				dim=0)

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

             _lowerCamelCase				:  Optional[Any]						=		self.movq.config.latent_channels

             _lowerCamelCase,	_lowerCamelCase				:  Union[str, Any]						=		downscale_height_and_width(a__   ,				a__   ,				self.movq_scale_factor)

             # create initial latent
             _lowerCamelCase				:  Dict						=		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
                    _lowerCamelCase				:  Union[str, Any]						=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                    _lowerCamelCase				:  int						=		{'''image_embeds''': image_embeds, '''hint''': hint}
                    _lowerCamelCase				:  Union[str, 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,	_lowerCamelCase				:  Any						=		noise_pred.split(latents.shape[1]   ,				dim=1)
                           _lowerCamelCase,	_lowerCamelCase				:  Optional[Any]						=		noise_pred.chunk(2)
                           _lowerCamelCase,	_lowerCamelCase				:  Optional[Any]						=		variance_pred.chunk(2)
                           _lowerCamelCase				:  Union[str, Any]						=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                           _lowerCamelCase				:  Union[str, 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,	_lowerCamelCase				:  List[str]						=		noise_pred.split(latents.shape[1]   ,				dim=1)

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

             if output_type == "pil":
                    _lowerCamelCase				:  int						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)








from __future__ import annotations

from scipy.special import comb  # type: ignore







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  str						=		list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             _lowerCamelCase				:  List[Any]						=		len(a__) - 1


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."
             _lowerCamelCase				:  list[float]						=		[]
             for i in range(len(self.list_of_points)):
                    # basis function for each i
                    output_values.append(
                        comb(self.degree   ,				a__) * ((1 - t) ** (self.degree - i)) * (t**i))
             # the basis must sum up to 1 for it to produce a valid Bezier curve.
             assert round(sum(a__)   ,				5) == 1
             return output_values


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             assert 0 <= t <= 1, "Time t must be between 0 and 1."

             _lowerCamelCase				:  Any						=		self.basis_function(a__)
             _lowerCamelCase				:  List[Any]						=		0.0
             _lowerCamelCase				:  List[Any]						=		0.0
             for i in range(len(self.list_of_points)):
                    # For all points, sum up the product of i-th basis function and i-th point.
                    x += basis_function[i] * self.list_of_points[i][0]
                    y += basis_function[i] * self.list_of_points[i][1]
             return (x, y)



      def 					__snake_case      (		self   ,				a__ = 0.01):



             """simple docstring"""





             from matplotlib import pyplot as plt  # type: ignore

             _lowerCamelCase				:  list[float]						=		[]  # x coordinates of points to plot
             _lowerCamelCase				:  list[float]						=		[]  # y coordinates of points to plot

             _lowerCamelCase				:  List[Any]						=		0.0
             while t <= 1:
                    _lowerCamelCase				:  Tuple						=		self.bezier_curve_function(a__)
                    to_plot_x.append(value[0])
                    to_plot_y.append(value[1])
                    t += step_size

             _lowerCamelCase				:  List[Any]						=		[i[0] for i in self.list_of_points]
             _lowerCamelCase				:  Union[str, Any]						=		[i[1] for i in self.list_of_points]

             plt.plot(
                 a__   ,				a__   ,				color='''blue'''   ,				label='''Curve of Degree ''' + str(self.degree)   ,				)
             plt.scatter(a__   ,				a__   ,				color='''red'''   ,				label='''Control Points''')
             plt.legend()
             plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
       BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
       BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

