


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,
)


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

__lowerCAmelCase     :			Dict      ='\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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__=8				):
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__=512   ,	lowercase__=512				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		pil_image.resize((w, h)   ,	resample=Image.BICUBIC   ,	reducing_gap=1				)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.array(pil_image.convert('''RGB'''				)				)
						__SCREAMING_SNAKE_CASE						:					int		      =		arr.astype(np.floataa				) / 127.5 - 1
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.transpose(lowercase__   ,	[2, 0, 1]				)
						__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.from_numpy(lowercase__				).unsqueeze(0				)
						return image
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :str      ,      lowerCAmelCase__      :UNetaDConditionModel      ,      lowerCAmelCase__      :DDPMScheduler      ,      lowerCAmelCase__      :VQModel      ,      )		->      List[Any]:
										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase__      ,      scheduler=lowerCAmelCase__      ,      movq=lowerCAmelCase__      ,      )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		2 ** (len(self.movq.config.block_out_channels     ) - 1)


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Any     )		->      Optional[Any]:
										# get the original timestep using init_timestep
										__SCREAMING_SNAKE_CASE						:					str		      =		min(int(num_inference_steps * strength     )      ,      lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		max(num_inference_steps - init_timestep      ,      0     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Optional[int]=None     )		->      Optional[Any]:
										if not isinstance(lowerCAmelCase__      ,      (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(lowerCAmelCase__     )}'''     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		image.to(device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		batch_size * num_images_per_prompt

										if image.shape[1] == 4:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		image

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

																elif isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					str		      =		[
																						    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowerCAmelCase__     )
																						]
																						__SCREAMING_SNAKE_CASE						:					int		      =		torch.cat(lowerCAmelCase__      ,      dim=0     )
																else:
																						__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.movq.encode(lowerCAmelCase__     ).latent_dist.sample(lowerCAmelCase__     )

																__SCREAMING_SNAKE_CASE						:					Dict		      =		self.movq.config.scaling_factor * init_latents

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.cat([init_latents]      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		init_latents.shape
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		randn_tensor(lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )

										# get latents
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.scheduler.add_noise(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		init_latents

										return latents


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Any=0     )		->      Dict:
										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						:					int		      =		[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__      ,      lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :List[str]=0     )		->      Optional[int]:
										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						:					Optional[Any]		      =		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)

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

										# We'll offload the last model manually.
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		hook


				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 		__magic_name__(	self      :int     )		->      Dict:
										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      :Dict      ,      lowerCAmelCase__      :Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      lowerCAmelCase__      :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,      lowerCAmelCase__      :Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :int = 512      ,      lowerCAmelCase__      :int = 512      ,      lowerCAmelCase__      :int = 100      ,      lowerCAmelCase__      :float = 4.0      ,      lowerCAmelCase__      :float = 0.3      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					str		      =		self._execution_device

										__SCREAMING_SNAKE_CASE						:					str		      =		guidance_scale > 1.0

										if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.cat(lowerCAmelCase__      ,      dim=0     )
										if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.cat(lowerCAmelCase__      ,      dim=0     )
										if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		torch.cat(lowerCAmelCase__      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image_embeds.shape[0]

										if do_classifier_free_guidance:
																__SCREAMING_SNAKE_CASE						:					int		      =		image_embeds.repeat_interleave(lowerCAmelCase__      ,      dim=0     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		negative_image_embeds.repeat_interleave(lowerCAmelCase__      ,      dim=0     )
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		hint.repeat_interleave(lowerCAmelCase__      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.cat([negative_image_embeds, image_embeds]      ,      dim=0     ).to(dtype=self.unet.dtype      ,      device=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat([hint, hint]      ,      dim=0     ).to(dtype=self.unet.dtype      ,      device=lowerCAmelCase__     )

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

										__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.cat([prepare_image(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     ) for i in image]      ,      dim=0     )
										__SCREAMING_SNAKE_CASE						:					int		      =		image.to(dtype=image_embeds.dtype      ,      device=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.movq.encode(lowerCAmelCase__     )['''latents''']
										__SCREAMING_SNAKE_CASE						:					str		      =		latents.repeat_interleave(lowerCAmelCase__      ,      dim=0     )
										self.scheduler.set_timesteps(lowerCAmelCase__      ,      device=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		self.get_timesteps(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		timesteps[:1].repeat(batch_size * num_images_per_prompt     )
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		downscale_height_and_width(lowerCAmelCase__      ,      lowerCAmelCase__      ,      self.movq_scale_factor     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.prepare_latents(
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      image_embeds.dtype      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )
										for i, t in enumerate(self.progress_bar(lowerCAmelCase__     )     ):
																# expand the latents if we are doing classifier free guidance
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		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						:					Union[str, Any]		      =		self.unet(
																    sample=lowerCAmelCase__      ,      timestep=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      added_cond_kwargs=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__      ,      )[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						:					Optional[int]		      =		noise_pred.chunk(2     )
																						__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		variance_pred.chunk(2     )
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						__SCREAMING_SNAKE_CASE						:					int		      =		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[Any]		      =		self.scheduler.step(
																    lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      )[0]

										# post-processing
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		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"]:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image * 0.5 + 0.5
																__SCREAMING_SNAKE_CASE						:					Dict		      =		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						:					Union[str, Any]		      =		self.numpy_to_pil(lowerCAmelCase__     )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__     )




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
