


from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__lowerCAmelCase     :			Any      ='\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'
class 	_lowercase				(      A__	):






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :UNetaDModel      ,      lowerCAmelCase__      :CMStochasticIterativeScheduler     )		->      None:
										super().__init__()

										self.register_modules(
										    unet=lowerCAmelCase__      ,      scheduler=lowerCAmelCase__      ,      )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		None


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :List[Any]=0     )		->      Optional[Any]:
										if is_accelerate_available() and is_accelerate_version('''>='''      ,      '''0.14.0'''     ):
																from accelerate import cpu_offload
										else:
																raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''     )

										__SCREAMING_SNAKE_CASE						:					int		      =		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)

										for cpu_offloaded_model in [self.unet]:
																cpu_offload(lowerCAmelCase__      ,      lowerCAmelCase__     )

										if self.safety_checker is not None:
																cpu_offload(self.safety_checker      ,      execution_device=lowerCAmelCase__      ,      offload_buffers=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						:					Dict		      =		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						:					int		      =		None
										for cpu_offloaded_model in [self.unet]:
																__SCREAMING_SNAKE_CASE						:					str		      =		cpu_offload_with_hook(lowerCAmelCase__      ,      lowerCAmelCase__      ,      prev_module_hook=lowerCAmelCase__     )

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

										# 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 		__magic_name__(	self      :List[str]     )		->      Optional[int]:
										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


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]=None     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					int		      =		(batch_size, num_channels, height, width)
										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.'''     )

										if latents is None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		randn_tensor(lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		latents.to(device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )

										# scale the initial noise by the standard deviation required by the scheduler
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		latents * self.scheduler.init_noise_sigma
										return latents


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :str = "pil"     )		->      int:
										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(
																    f'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''     )

										# Equivalent to diffusers.VaeImageProcessor.denormalize
										__SCREAMING_SNAKE_CASE						:					int		      =		(sample / 2 + 0.5).clamp(0      ,      1     )
										if output_type == "pt":
																return sample

										# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
										__SCREAMING_SNAKE_CASE						:					Dict		      =		sample.cpu().permute(0      ,      2      ,      3      ,      1     ).numpy()
										if output_type == "np":
																return sample

										# Output_type must be 'pil'
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.numpy_to_pil(lowerCAmelCase__     )
										return sample


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Tuple=None     )		->      Optional[int]:
										if self.unet.config.num_class_embeds is not None:
																if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor(lowerCAmelCase__      ,      dtype=torch.int     )
																elif isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.tensor([class_labels]      ,      dtype=torch.int     )
																elif class_labels is None:
																						# Randomly generate batch_size class labels
																						# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.randint(0      ,      self.unet.config.num_class_embeds      ,      size=(batch_size,)     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		class_labels.to(lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Any		      =		None
										return class_labels


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Any     )		->      Union[str, Any]:
										if num_inference_steps is None and timesteps is None:
																raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''     )

										if num_inference_steps is not None and timesteps is not None:
																logger.warning(
																    f'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
																    ''' `timesteps` will be used over `num_inference_steps`.'''     )

										if latents is not None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(batch_size, 3, img_size, img_size)
																if latents.shape != expected_shape:
																						raise ValueError(f'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''     )

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ) or callback_steps <= 0)
										):
																raise ValueError(
																    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																    f''' {type(lowerCAmelCase__     )}.'''     )



				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__     )
				def __call__(	self      :str      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :Optional[Union[torch.Tensor, List[int], int]] = None      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :List[int] = None      ,      lowerCAmelCase__      :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase__      :Optional[torch.FloatTensor] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      lowerCAmelCase__      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCAmelCase__      :int = 1      ,      )		->      Any:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.unet.config.sample_size
										__SCREAMING_SNAKE_CASE						:					int		      =		self._execution_device

										# 1. Check inputs
										self.check_inputs(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

										# 2. Prepare image latents
										# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.prepare_latents(
										    batch_size=lowerCAmelCase__      ,      num_channels=self.unet.config.in_channels      ,      height=lowerCAmelCase__      ,      width=lowerCAmelCase__      ,      dtype=self.unet.dtype      ,      device=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      latents=lowerCAmelCase__      ,      )

										# 3. Handle class_labels for class-conditional models
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.prepare_class_labels(lowerCAmelCase__      ,      lowerCAmelCase__      ,      class_labels=lowerCAmelCase__     )

										# 4. Prepare timesteps
										if timesteps is not None:
																self.scheduler.set_timesteps(timesteps=lowerCAmelCase__      ,      device=lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.scheduler.timesteps
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		len(lowerCAmelCase__     )
										else:
																self.scheduler.set_timesteps(lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.scheduler.timesteps

										# 5. Denoising loop
										# Multistep sampling: implements Algorithm 1 in the paper
										with self.progress_bar(total=lowerCAmelCase__     ) as progress_bar:
																for i, t in enumerate(lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.scheduler.scale_model_input(lowerCAmelCase__      ,      lowerCAmelCase__     )
																						__SCREAMING_SNAKE_CASE						:					Dict		      =		self.unet(lowerCAmelCase__      ,      lowerCAmelCase__      ,      class_labels=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__     )[0]

																						__SCREAMING_SNAKE_CASE						:					int		      =		self.scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      generator=lowerCAmelCase__     )[0]

																						# call the callback, if provided
																						progress_bar.update()
																						if callback is not None and i % callback_steps == 0:
																												callback(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )

        # 6. Post-process image sample
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.postprocess_image(lowerCAmelCase__      ,      output_type=lowerCAmelCase__     )

										# Offload last model to CPU
										if hasattr(self      ,      '''final_offload_hook'''     ) and self.final_offload_hook is not None:
																self.final_offload_hook.offload()

										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.""")
