
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,
)


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

_lowercase											=    """
    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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     List[Any]=8				)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				Dict		=height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       SCREAMING_SNAKE_CASE_							:				List[Any]		=width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor


class 			lowercase_		(      A       ):


       def __init__(    self						,       __A						,       __A						,       __A						,       )						->    Union[str, Any]:
              super().__init__()

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


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    Dict:
              if latents is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, 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}'				)
                     SCREAMING_SNAKE_CASE_							:				Dict		=latents.to(__A				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=latents * scheduler.init_noise_sigma
              return latents


       def 	_snake_case				(    self						,       __A=0				)						->    Any:
              if is_accelerate_available():
                     from accelerate import cpu_offload
              else:
                     raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

              SCREAMING_SNAKE_CASE_							:				Dict		=torch.device(F'cuda:{gpu_id}'				)

              SCREAMING_SNAKE_CASE_							:				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				)						->    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.'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=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)

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

              # We'll offload the last model manually.
              SCREAMING_SNAKE_CASE_							:				int		=hook


       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 	_snake_case				(    self				)						->    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						,       __A						,       __A						,       __A						,       __A = 512						,       __A = 512						,       __A = 100						,       __A = 4.0						,       __A = 1						,       __A = None						,       __A = None						,       __A = "pil"						,       __A = True						,       )						->    int:
              SCREAMING_SNAKE_CASE_							:				Dict		=self._execution_device

              SCREAMING_SNAKE_CASE_							:				Tuple		=guidance_scale > 1.0

              if isinstance(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				int		=torch.cat(__A						,       dim=0				)
              if isinstance(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.cat(__A						,       dim=0				)
              if isinstance(__A						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				str		=torch.cat(__A						,       dim=0				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=image_embeds.shape[0] * num_images_per_prompt

              if do_classifier_free_guidance:
                     SCREAMING_SNAKE_CASE_							:				Any		=image_embeds.repeat_interleave(__A						,       dim=0				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=negative_image_embeds.repeat_interleave(__A						,       dim=0				)
                     SCREAMING_SNAKE_CASE_							:				Any		=hint.repeat_interleave(__A						,       dim=0				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.cat([negative_image_embeds, image_embeds]						,       dim=0				).to(dtype=self.unet.dtype						,       device=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.cat([hint, hint]						,       dim=0				).to(dtype=self.unet.dtype						,       device=__A				)
              self.scheduler.set_timesteps(__A						,       device=__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.timesteps

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.movq.config.latent_channels

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=downscale_height_and_width(__A						,       __A						,       self.movq_scale_factor				)

              # create initial latent
              SCREAMING_SNAKE_CASE_							:				Optional[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
                     SCREAMING_SNAKE_CASE_							:				str		=torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''image_embeds''': image_embeds, '''hint''': hint}
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.unet(
                         sample=__A						,       timestep=__A						,       encoder_hidden_states=__A						,       added_cond_kwargs=__A						,       return_dict=__A						,       )[0]

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

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

              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Any		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)


from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
