
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,
)


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

UpperCAmelCase       =					'''
    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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=8				):
	lowercase	    =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	lowercase	    =       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						,       snake_case						,       snake_case						,       snake_case						,       ):
		super().__init__()

		self.register_modules(
		    unet=snake_case						,       scheduler=snake_case						,       movq=snake_case						,       )
		lowercase	    =       2 ** (len(self.movq.config.block_out_channels			) - 1)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case						,       snake_case			):
		if latents is None:
			lowercase	    =       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}'''			)
			lowercase	    =       latents.to(snake_case			)

		lowercase	    =       latents * scheduler.init_noise_sigma
		return latents





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case=0			):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError('Please install accelerate via `pip install accelerate`'			)

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

		lowercase	    =       [
		    self.unet,
		    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						,       snake_case=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.'			)

		lowercase	    =       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)

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

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





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def       SCREAMING_SNAKE_CASE__		(			self			):
		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						,       snake_case						,       snake_case						,       snake_case						,       snake_case = 512						,       snake_case = 512						,       snake_case = 100						,       snake_case = 4.0						,       snake_case = 1						,       snake_case = None						,       snake_case = None						,       snake_case = "pil"						,       snake_case = True						,       ):
		lowercase	    =       self._execution_device

		lowercase	    =       guidance_scale > 1.0

		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)
		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)
		if isinstance(snake_case						,       snake_case			):
			lowercase	    =       torch.cat(snake_case						,       dim=0			)

		lowercase	    =       image_embeds.shape[0] * num_images_per_prompt

		if do_classifier_free_guidance:
			lowercase	    =       image_embeds.repeat_interleave(snake_case						,       dim=0			)
			lowercase	    =       negative_image_embeds.repeat_interleave(snake_case						,       dim=0			)
			lowercase	    =       hint.repeat_interleave(snake_case						,       dim=0			)

		lowercase	    =       torch.cat([negative_image_embeds, image_embeds]						,       dim=0			).to(dtype=self.unet.dtype						,       device=snake_case			)
		lowercase	    =       torch.cat([hint, hint]						,       dim=0			).to(dtype=self.unet.dtype						,       device=snake_case			)
		self.scheduler.set_timesteps(snake_case						,       device=snake_case			)
		lowercase	    =       self.scheduler.timesteps

		lowercase	    =       self.movq.config.latent_channels

		lowercase    ,   lowercase	    =       downscale_height_and_width(snake_case						,       snake_case						,       self.movq_scale_factor			)

		# create initial latent
		lowercase	    =       self.prepare_latents(
		    (batch_size, num_channels_latents, height, width)						,       image_embeds.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
			lowercase	    =       torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

			lowercase	    =       {'image_embeds': image_embeds, 'hint': hint}
			lowercase	    =       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:
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)
				lowercase    ,   lowercase	    =       noise_pred.chunk(2			)
				lowercase    ,   lowercase	    =       variance_pred.chunk(2			)
				lowercase	    =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
				lowercase	    =       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"]
			):
				lowercase    ,   lowercase	    =       noise_pred.split(latents.shape[1]						,       dim=1			)

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

		if output_type == "pil":
			lowercase	    =       self.numpy_to_pil(snake_case			)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case			)







"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()