



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,
)


a_       :List[Any]				  =				logging.get_logger(__name__)  # pylint: disable=invalid-name

a_       :Any				  =				"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 	lowercase_							(A			:			int		, A			:			List[Any]		, A			:			Tuple=8				):
       snake_case__							:     int            =     height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       snake_case__							:     Tuple            =     width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor
def 	lowercase_							(A			:			List[str]		, A			:			Any=5_1_2		, A			:			Optional[Any]=5_1_2				):
       snake_case__							:     Dict            =     pil_image.resize((w, h)		, resample=Image.BICUBIC		, reducing_gap=1				)
       snake_case__							:     List[str]            =     np.array(pil_image.convert('RGB'				)				)
       snake_case__							:     Optional[Any]            =     arr.astype(np.floataa				) / 127.5 - 1
       snake_case__							:     Optional[int]            =     np.transpose(A		, [2, 0, 1]				)
       snake_case__							:     Dict            =     torch.from_numpy(A				).unsqueeze(0				)
       return image





class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def __init__(	self  :  Tuple,			_snake_case  :  UNetaDConditionModel,			_snake_case  :  DDPMScheduler,			_snake_case  :  VQModel,			)       ->Optional[int]:
            super().__init__()

            self.register_modules(
                unet=_snake_case,			scheduler=_snake_case,			movq=_snake_case,			)
            snake_case__							:     List[str]            =     2 ** (len(self.movq.config.block_out_channels ) - 1)






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Optional[Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Union[str, Any] )       ->List[Any]:
            # get the original timestep using init_timestep
            snake_case__							:     List[Any]            =     min(int(num_inference_steps * strength ),			_snake_case )

            snake_case__							:     Any            =     max(num_inference_steps - init_timestep,			0 )
            snake_case__							:     int            =     self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start






     def        lowercase_       (	self  :  Union[str, Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Union[str, Any],			_snake_case  :  Tuple,			_snake_case  :  Union[str, Any],			_snake_case  :  Dict,			_snake_case  :  Union[str, Any]=None )       ->Tuple:
            if not isinstance(_snake_case,			(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(_snake_case )}''' )

            snake_case__							:     Any            =     image.to(device=_snake_case,			dtype=_snake_case )

            snake_case__							:     Dict            =     batch_size * num_images_per_prompt

            if image.shape[1] == 4:
                   snake_case__							:     int            =     image

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

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

                   snake_case__							:     List[Any]            =     self.movq.config.scaling_factor * init_latents

            snake_case__							:     List[str]            =     torch.cat([init_latents],			dim=0 )

            snake_case__							:     int            =     init_latents.shape
            snake_case__							:     List[str]            =     randn_tensor(_snake_case,			generator=_snake_case,			device=_snake_case,			dtype=_snake_case )

            # get latents
            snake_case__							:     Dict            =     self.scheduler.add_noise(_snake_case,			_snake_case,			_snake_case )

            snake_case__							:     Union[str, Any]            =     init_latents

            return latents






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

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

            snake_case__							:     Union[str, Any]            =     [
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                          cpu_offload(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Optional[int]=0 )       ->List[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.' )

            snake_case__							:     List[Any]            =     torch.device(F'''cuda:{gpu_id}''' )

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

            snake_case__							:     Optional[Any]            =     None
            for cpu_offloaded_model in [self.unet, self.movq]:
                   snake_case__     ,    snake_case__							:     Union[str, Any]            =     cpu_offload_with_hook(_snake_case,			_snake_case,			prev_module_hook=_snake_case )

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






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def        lowercase_       (	self  :  Tuple )       ->List[str]:
            if not hasattr(self.unet,			'_hf_hook' ):
                   return self.device
            for module in self.unet.modules():
                   if (
                       hasattr(_snake_case,			'_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(_snake_case )
     def __call__(	self  :  List[str],			_snake_case  :  Union[torch.FloatTensor, List[torch.FloatTensor]],			_snake_case  :  Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],			_snake_case  :  Union[torch.FloatTensor, List[torch.FloatTensor]],			_snake_case  :  torch.FloatTensor,			_snake_case  :  int = 5_1_2,			_snake_case  :  int = 5_1_2,			_snake_case  :  int = 1_0_0,			_snake_case  :  float = 4.0,			_snake_case  :  float = 0.3,			_snake_case  :  int = 1,			_snake_case  :  Optional[Union[torch.Generator, List[torch.Generator]]] = None,			_snake_case  :  Optional[str] = "pil",			_snake_case  :  bool = True,			)       ->Optional[Any]:
            snake_case__							:     Dict            =     self._execution_device

            snake_case__							:     Dict            =     guidance_scale > 1.0

            if isinstance(_snake_case,			_snake_case ):
                   snake_case__							:     List[Any]            =     torch.cat(_snake_case,			dim=0 )
            if isinstance(_snake_case,			_snake_case ):
                   snake_case__							:     Dict            =     torch.cat(_snake_case,			dim=0 )
            if isinstance(_snake_case,			_snake_case ):
                   snake_case__							:     str            =     torch.cat(_snake_case,			dim=0 )

            snake_case__							:     List[Any]            =     image_embeds.shape[0]

            if do_classifier_free_guidance:
                   snake_case__							:     Dict            =     image_embeds.repeat_interleave(_snake_case,			dim=0 )
                   snake_case__							:     Union[str, Any]            =     negative_image_embeds.repeat_interleave(_snake_case,			dim=0 )
                   snake_case__							:     Optional[int]            =     hint.repeat_interleave(_snake_case,			dim=0 )

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

            if not isinstance(_snake_case,			_snake_case ):
                   snake_case__							:     int            =     [image]
            if not all(isinstance(_snake_case,			(PIL.Image.Image, torch.Tensor) ) for i in image ):
                   raise ValueError(
                       F'''Input is in incorrect format: {[type(_snake_case ) for i in image]}. Currently, we only support  PIL image and pytorch tensor''' )

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

            snake_case__							:     Dict            =     self.movq.encode(_snake_case )['latents']
            snake_case__							:     str            =     latents.repeat_interleave(_snake_case,			dim=0 )
            self.scheduler.set_timesteps(_snake_case,			device=_snake_case )
            snake_case__     ,    snake_case__							:     int            =     self.get_timesteps(_snake_case,			_snake_case,			_snake_case )
            snake_case__							:     Union[str, Any]            =     timesteps[:1].repeat(batch_size * num_images_per_prompt )
            snake_case__     ,    snake_case__							:     Optional[Any]            =     downscale_height_and_width(_snake_case,			_snake_case,			self.movq_scale_factor )
            snake_case__							:     str            =     self.prepare_latents(
                _snake_case,			_snake_case,			_snake_case,			_snake_case,			image_embeds.dtype,			_snake_case,			_snake_case )
            for i, t in enumerate(self.progress_bar(_snake_case ) ):
                   # expand the latents if we are doing classifier free guidance
                   snake_case__							:     Dict            =     torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

                   snake_case__							:     Optional[int]            =     {'image_embeds': image_embeds, 'hint': hint}
                   snake_case__							:     Optional[int]            =     self.unet(
                       sample=_snake_case,			timestep=_snake_case,			encoder_hidden_states=_snake_case,			added_cond_kwargs=_snake_case,			return_dict=_snake_case,			)[0]

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

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

                   snake_case__							:     int            =     self.scheduler.step(
                       _snake_case,			_snake_case,			_snake_case,			generator=_snake_case,			)[0]

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

            if output_type == "pil":
                   snake_case__							:     Tuple            =     self.numpy_to_pil(_snake_case )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_snake_case )











from manim import *




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[Any] )       ->Dict:
            snake_case__							:     str            =     Rectangle(height=0.5,			width=0.5 )
            snake_case__							:     str            =     Rectangle(height=0.4_6,			width=0.4_6 ).set_stroke(width=0 )
            snake_case__							:     List[Any]            =     Rectangle(height=0.2_5,			width=0.2_5 )

            snake_case__							:     Any            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Optional[Any]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[int]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Any            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[Any]            =     Text('CPU',			font_size=2_4 )
            snake_case__							:     str            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            cpu.move_to([-2.5, -0.5, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(4 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     str            =     Text('GPU',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            gpu.move_to([-1, -1, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Union[str, Any]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )

            snake_case__							:     int            =     Text('Model',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            model.move_to([3, -1.0, 0] )
            self.add(_snake_case )

            snake_case__							:     Any            =     []
            snake_case__							:     Optional[int]            =     []

            for i, rect in enumerate(_snake_case ):
                   rect.set_stroke(_snake_case )

                   snake_case__							:     Union[str, Any]            =     Rectangle(height=0.4_6 / 4,			width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case,			opacity=0.7 )

                   if i == 0:
                          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ),			buff=0.0_2,			direction=_snake_case )
                          cpu_target.set_x(cpu_target.get_x() + 0.1 )
                   elif i == 3:
                          cpu_target.next_to(model_cpu_arr[0],			direction=_snake_case,			buff=0.0 )
                   else:
                          cpu_target.next_to(model_cpu_arr[i - 1],			direction=_snake_case,			buff=0.0 )
                   self.add(_snake_case )
                   model_cpu_arr.append(_snake_case )

            self.add(*_snake_case,			*_snake_case )

            snake_case__							:     Optional[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     List[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     Dict            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Tuple            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     int            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Union[str, Any]            =     Text('Disk',			font_size=2_4 )
            snake_case__							:     int            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            disk.move_to([-4.0, -1.2_5, 0] )
            self.add(_snake_case,			_snake_case )

            snake_case__							:     str            =     []

            for i in range(6 ):
                   snake_case__							:     str            =     fill.copy().set_fill(_snake_case,			opacity=0.8 )
                   target.move_to(disk_left_col_base[i] ).scale(0.5 )
                   cpu_disk_arr.append(_snake_case )

            self.add(*_snake_case )

            snake_case__							:     int            =     Square(side_length=2.2 )
            key.move_to([-5, 2, 0] )

            snake_case__							:     int            =     MarkupText(
                F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',			font_size=1_8,			)

            key_text.move_to([-5, 2.4, 0] )

            self.add(_snake_case,			_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(
                F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',			font_size=1_8,			)

            blue_text.next_to(_snake_case,			DOWN * 2.4,			aligned_edge=key_text.get_left() )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(F'''The offloaded weights are all sent to the CPU.''',			font_size=2_4 )
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            for i in range(6 ):
                   snake_case__							:     int            =     cpu_disk_arr[i]
                   snake_case__							:     Optional[Any]            =     rect.copy().set_fill(_snake_case,			opacity=0.8 ).scale(2.0 )
                   cpa.generate_target()
                   cpa.target.move_to(model_base[i] )

                   if i == 0:
                          rect.set_fill(_snake_case,			opacity=0.8 )
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[0] ).scale(2.0 )

                          self.remove(
                              *_snake_case,			*_snake_case,			)

                   else:
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[i] ).scale(2.0 )
                   self.play(MoveToTarget(_snake_case ),			MoveToTarget(_snake_case ),			model_base[i].animate.set_stroke(_snake_case ) )
            self.play(FadeOut(_snake_case ) )

            snake_case__							:     Optional[Any]            =     MarkupText(
                F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.''',			font_size=2_4,			)
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            snake_case__							:     str            =     []
            snake_case__							:     Optional[Any]            =     []
            for i in range(6 ):
                   snake_case__							:     List[Any]            =     Arrow(start=_snake_case,			end=_snake_case,			color=_snake_case,			buff=0.5 )
                   a.next_to(model_base[i].get_left(),			_snake_case,			buff=0.2 )
                   arrows.append(_snake_case )
                   animations.append(Write(_snake_case ) )
            self.play(*_snake_case )
            self.wait()






