"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__lowerCAmelCase     :				Any    				=    "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__=8  ):





		"""simple docstring"""




		lowerCAmelCase__   = h // scale_factor**2
		if h % scale_factor**2 != 0:
				new_h += 1
		lowerCAmelCase__   = w // scale_factor**2
		if w % scale_factor**2 != 0:
				new_w += 1
		return new_h * scale_factor, new_w * scale_factor






class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Any	,    snake_case__    :  MultilingualCLIP	,    snake_case__    :  XLMRobertaTokenizer	,    snake_case__    :  UNetaDConditionModel	,    snake_case__    :  Union[DDIMScheduler, DDPMScheduler]	,    snake_case__    :  VQModel	,    ):
							super().__init__()

							self.register_modules(
							    text_encoder=snake_case__	,    tokenizer=snake_case__	,    unet=snake_case__	,    scheduler=snake_case__	,    movq=snake_case__	,    )
							lowerCAmelCase__   = 2 ** (len(self.movq.config.block_out_channels  ) - 1)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any	,    snake_case__    :  Dict	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Dict	,    snake_case__    :  Any	,    snake_case__    :  Optional[int]  ):
							if latents is None:
									lowerCAmelCase__   = randn_tensor(snake_case__	,    generator=snake_case__	,    device=snake_case__	,    dtype=snake_case__  )
							else:
									if latents.shape != shape:
											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
									lowerCAmelCase__   = latents.to(snake_case__  )

							lowerCAmelCase__   = latents * scheduler.init_noise_sigma
							return latents




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Dict	,    snake_case__    :  str	,    snake_case__    :  List[str]	,    snake_case__    :  int	,    snake_case__    :  Any=None	,    ):
							lowerCAmelCase__   = len(snake_case__  ) if isinstance(snake_case__	,    snake_case__  ) else 1
							# get prompt text embeddings
							lowerCAmelCase__   = self.tokenizer(
							    snake_case__	,    padding="""max_length"""	,    truncation=snake_case__	,    max_length=77	,    return_attention_mask=snake_case__	,    add_special_tokens=snake_case__	,    return_tensors="""pt"""	,    )

							lowerCAmelCase__   = text_inputs.input_ids
							lowerCAmelCase__   = self.tokenizer(snake_case__	,    padding="""longest"""	,    return_tensors="""pt"""  ).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""  )

							lowerCAmelCase__   = text_input_ids.to(snake_case__  )
							lowerCAmelCase__   = text_inputs.attention_mask.to(snake_case__  )

							lowerCAmelCase__ ,       lowerCAmelCase__   = self.text_encoder(
							    input_ids=snake_case__	,    attention_mask=snake_case__  )

							lowerCAmelCase__   = prompt_embeds.repeat_interleave(snake_case__	,    dim=0  )
							lowerCAmelCase__   = text_encoder_hidden_states.repeat_interleave(snake_case__	,    dim=0  )
							lowerCAmelCase__   = text_mask.repeat_interleave(snake_case__	,    dim=0  )

							if do_classifier_free_guidance:
									lowerCAmelCase__   = 42
									if negative_prompt is None:
											lowerCAmelCase__   = [""""""] * batch_size
									elif type(snake_case__  ) is not type(snake_case__  ):
											raise TypeError(
											    F"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__  )} !="""
											    F""" {type(snake_case__  )}."""  )
									elif isinstance(snake_case__	,    snake_case__  ):
											lowerCAmelCase__   = [negative_prompt]
									elif batch_size != len(snake_case__  ):
											raise ValueError(
											    F"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__  )}, but `prompt`:"""
											    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
											    """ the batch size of `prompt`."""  )
									else:
											lowerCAmelCase__   = negative_prompt

									lowerCAmelCase__   = self.tokenizer(
									    snake_case__	,    padding="""max_length"""	,    max_length=77	,    truncation=snake_case__	,    return_attention_mask=snake_case__	,    add_special_tokens=snake_case__	,    return_tensors="""pt"""	,    )
									lowerCAmelCase__   = uncond_input.input_ids.to(snake_case__  )
									lowerCAmelCase__   = uncond_input.attention_mask.to(snake_case__  )

									lowerCAmelCase__ ,       lowerCAmelCase__   = self.text_encoder(
									    input_ids=snake_case__	,    attention_mask=snake_case__  )

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									lowerCAmelCase__   = negative_prompt_embeds.shape[1]
									lowerCAmelCase__   = negative_prompt_embeds.repeat(1	,    snake_case__  )
									lowerCAmelCase__   = negative_prompt_embeds.view(batch_size * num_images_per_prompt	,    snake_case__  )

									lowerCAmelCase__   = uncond_text_encoder_hidden_states.shape[1]
									lowerCAmelCase__   = uncond_text_encoder_hidden_states.repeat(1	,    snake_case__	,    1  )
									lowerCAmelCase__   = uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt	,    snake_case__	,    -1  )
									lowerCAmelCase__   = uncond_text_mask.repeat_interleave(snake_case__	,    dim=0  )

									# done duplicates

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCAmelCase__   = torch.cat([negative_prompt_embeds, prompt_embeds]  )
									lowerCAmelCase__   = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )

									lowerCAmelCase__   = torch.cat([uncond_text_mask, text_mask]  )

							return prompt_embeds, text_encoder_hidden_states, text_mask




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  List[Any]=0  ):
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

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

							lowerCAmelCase__   = [
							    self.unet,
							    self.text_encoder,
							    self.movq,
							]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(snake_case__	,    snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  str=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."""  )

							lowerCAmelCase__   = 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)

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

							if self.safety_checker is not None:
									lowerCAmelCase__ ,       lowerCAmelCase__   = cpu_offload_with_hook(self.safety_checker	,    snake_case__	,    prev_module_hook=snake_case__  )

							# We'll offload the last model manually.
							lowerCAmelCase__   = hook




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							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    :  Union[str, Any]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  Union[torch.FloatTensor, List[torch.FloatTensor]]	,    snake_case__    :  Union[torch.FloatTensor, List[torch.FloatTensor]]	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 100	,    snake_case__    :  float = 4.0	,    snake_case__    :  int = 1	,    snake_case__    :  Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    ):

							if isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = 1
							elif isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = len(snake_case__  )
							else:
									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(snake_case__  )}"""  )

							lowerCAmelCase__   = self._execution_device

							lowerCAmelCase__   = batch_size * num_images_per_prompt
							lowerCAmelCase__   = guidance_scale > 1.0

							lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = self._encode_prompt(
							    snake_case__	,    snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )

							if isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = torch.cat(snake_case__	,    dim=0  )
							if isinstance(snake_case__	,    snake_case__  ):
									lowerCAmelCase__   = torch.cat(snake_case__	,    dim=0  )

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

							lowerCAmelCase__   = torch.cat([negative_image_embeds, image_embeds]	,    dim=0  ).to(
							    dtype=prompt_embeds.dtype	,    device=snake_case__  )

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

							lowerCAmelCase__   = self.unet.config.in_channels

							lowerCAmelCase__ ,       lowerCAmelCase__   = get_new_h_w(snake_case__	,    snake_case__	,    self.movq_scale_factor  )

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

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

									lowerCAmelCase__   = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
									lowerCAmelCase__   = 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:
											lowerCAmelCase__ ,       lowerCAmelCase__   = noise_pred.split(latents.shape[1]	,    dim=1  )
											lowerCAmelCase__ ,       lowerCAmelCase__   = noise_pred.chunk(2  )
											lowerCAmelCase__ ,       lowerCAmelCase__   = variance_pred.chunk(2  )
											lowerCAmelCase__   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											lowerCAmelCase__   = 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__   = noise_pred.split(latents.shape[1]	,    dim=1  )

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

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

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=snake_case__  )





"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if (ksize % 2) == 0:
				lowerCAmelCase__   = ksize + 1
		lowerCAmelCase__   = np.zeros((ksize, ksize)      ,	dtype=np.floataa  )

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

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

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

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

						# fill kernel
						lowerCAmelCase__   = 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
			__lowerCAmelCase     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

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

			waitKey(0)




