
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,
)


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

__magic_name__            =  '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=8							):
    snake_case__						=				height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    snake_case__						=				width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          super().__init__()

          self.register_modules(
              unet=lowerCamelCase		,	scheduler=lowerCamelCase		,	movq=lowerCamelCase		,	)
          snake_case__						=				2 ** (len(self.movq.config.block_out_channels ) - 1)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          if latents is None:
              snake_case__						=				randn_tensor(lowerCamelCase		,	generator=lowerCamelCase		,	device=lowerCamelCase		,	dtype=lowerCamelCase )
          else:
              if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
              snake_case__						=				latents.to(lowerCamelCase )

          snake_case__						=				latents * scheduler.init_noise_sigma
          return latents
      def A_			(			self		,	lowerCamelCase=0 ):
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError("Please install accelerate via `pip install accelerate`" )

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

          snake_case__						=				[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self		,	lowerCamelCase=0 ):
          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__						=				torch.device(F"""cuda:{gpu_id}""" )

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

          snake_case__						=				None
          for cpu_offloaded_model in [self.unet, self.movq]:
              snake_case__						,					snake_case__						=				cpu_offload_with_hook(lowerCamelCase		,	lowerCamelCase		,	prev_module_hook=lowerCamelCase )

          # We'll offload the last model manually.
          snake_case__						=				hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def A_			(			self ):
          if not hasattr(self.unet		,	"_hf_hook" ):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(lowerCamelCase		,	"_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(lowerCamelCase )
      def __call__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = 5_12		,	lowerCamelCase = 5_12		,	lowerCamelCase = 1_00		,	lowerCamelCase = 4.0		,	lowerCamelCase = 1		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = "pil"		,	lowerCamelCase = True		,	):
          snake_case__						=				self._execution_device

          snake_case__						=				guidance_scale > 1.0

          if isinstance(lowerCamelCase		,	lowerCamelCase ):
              snake_case__						=				torch.cat(lowerCamelCase		,	dim=0 )
          snake_case__						=				image_embeds.shape[0] * num_images_per_prompt
          if isinstance(lowerCamelCase		,	lowerCamelCase ):
              snake_case__						=				torch.cat(lowerCamelCase		,	dim=0 )

          if do_classifier_free_guidance:
              snake_case__						=				image_embeds.repeat_interleave(lowerCamelCase		,	dim=0 )
              snake_case__						=				negative_image_embeds.repeat_interleave(lowerCamelCase		,	dim=0 )

          snake_case__						=				torch.cat([negative_image_embeds, image_embeds]		,	dim=0 ).to(dtype=self.unet.dtype		,	device=lowerCamelCase )

          self.scheduler.set_timesteps(lowerCamelCase		,	device=lowerCamelCase )
          snake_case__						=				self.scheduler.timesteps

          snake_case__						=				self.unet.config.in_channels

          snake_case__						,					snake_case__						=				downscale_height_and_width(lowerCamelCase		,	lowerCamelCase		,	self.movq_scale_factor )

          # create initial latent
          snake_case__						=				self.prepare_latents(
              (batch_size, num_channels_latents, height, width)		,	image_embeds.dtype		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	self.scheduler		,	)

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

              snake_case__						=				{"image_embeds": image_embeds}
              snake_case__						=				self.unet(
                  sample=lowerCamelCase		,	timestep=lowerCamelCase		,	encoder_hidden_states=lowerCamelCase		,	added_cond_kwargs=lowerCamelCase		,	return_dict=lowerCamelCase		,	)[0]

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

              # compute the previous noisy sample x_t -> x_t-1
              snake_case__						=				self.scheduler.step(
                  lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	generator=lowerCamelCase		,	)[0]
          # post-processing
          snake_case__						=				self.movq.decode(lowerCamelCase		,	force_not_quantize=lowerCamelCase )["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__						=				image * 0.5 + 0.5
              snake_case__						=				image.clamp(0		,	1 )
              snake_case__						=				image.cpu().permute(0		,	2		,	3		,	1 ).float().numpy()

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowerCamelCase )





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



