


'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__magic_name__   :   int  							='\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'






@dataclass
class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					Any	,				_lowerCamelCase				:					PriorTransformer	,				_lowerCamelCase				:					CLIPTextModelWithProjection	,				_lowerCamelCase				:					CLIPTokenizer	,				_lowerCamelCase				:					HeunDiscreteScheduler	,				_lowerCamelCase				:					ShapERenderer	,				)							->					Tuple:
										super().__init__()

										self.register_modules(
										    prior=_lowerCamelCase	,				text_encoder=_lowerCamelCase	,				tokenizer=_lowerCamelCase	,				scheduler=_lowerCamelCase	,				renderer=_lowerCamelCase	,				)
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					List[Any]  )							->					Tuple:
										if latents is None:
														__magic_name__	  = 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}'  )
														__magic_name__	  = latents.to(_lowerCamelCase  )

										__magic_name__	  = latents * scheduler.init_noise_sigma
										return latents
						def 					__A					( self				:					str	,				_lowerCamelCase				:					Optional[Any]=0  )							->					Union[str, Any]:
										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError("Please install accelerate via `pip install accelerate`"  )

										__magic_name__	  = torch.device(f'cuda:{gpu_id}'  )

										__magic_name__	  = [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(_lowerCamelCase	,				_lowerCamelCase  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Dict=0  )							->					List[str]:
										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."  )

										__magic_name__	  = 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)

										__magic_name__	  = None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
														__magic_name__  ,      __magic_name__	  = cpu_offload_with_hook(_lowerCamelCase	,				_lowerCamelCase	,				prev_module_hook=_lowerCamelCase  )

										if self.safety_checker is not None:
														__magic_name__  ,      __magic_name__	  = cpu_offload_with_hook(self.safety_checker	,				_lowerCamelCase	,				prev_module_hook=_lowerCamelCase  )

										# We'll offload the last model manually.
										__magic_name__	  = hook
						@property
						def 					__A					( self				:					str  )							->					Optional[int]:
										if self.device != torch.device("meta"  ) or not hasattr(self.text_encoder	,				"_hf_hook"  ):
														return self.device
										for module in self.text_encoder.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 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					str	,				)							->					Any:
										len(_lowerCamelCase  ) if isinstance(_lowerCamelCase	,				_lowerCamelCase  ) else 1

										# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
										__magic_name__	  = 0
										# get prompt text embeddings
										__magic_name__	  = self.tokenizer(
										    _lowerCamelCase	,				padding="max_length"	,				max_length=self.tokenizer.model_max_length	,				truncation=_lowerCamelCase	,				return_tensors="pt"	,				)
										__magic_name__	  = text_inputs.input_ids
										__magic_name__	  = self.tokenizer(_lowerCamelCase	,				padding="longest"	,				return_tensors="pt"  ).input_ids

										if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = 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}'  )

										__magic_name__	  = self.text_encoder(text_input_ids.to(_lowerCamelCase  )  )
										__magic_name__	  = text_encoder_output.text_embeds

										__magic_name__	  = prompt_embeds.repeat_interleave(_lowerCamelCase	,				dim=0  )
										# in Shap-E it normalize the prompt_embeds and then later rescale it
										__magic_name__	  = prompt_embeds / torch.linalg.norm(_lowerCamelCase	,				dim=-1	,				keepdim=_lowerCamelCase  )

										if do_classifier_free_guidance:
														__magic_name__	  = torch.zeros_like(_lowerCamelCase  )

														# 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
														__magic_name__	  = torch.cat([negative_prompt_embeds, prompt_embeds]  )

										# Rescale the features to have unit variance
										__magic_name__	  = math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

										return prompt_embeds

						@torch.no_grad()
						@replace_example_docstring(_lowerCamelCase  )
						def __call__( self				:					str	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					int = 1	,				_lowerCamelCase				:					int = 25	,				_lowerCamelCase				:					Optional[Union[torch.Generator, List[torch.Generator]]] = None	,				_lowerCamelCase				:					Optional[torch.FloatTensor] = None	,				_lowerCamelCase				:					float = 4.0	,				_lowerCamelCase				:					int = 64	,				_lowerCamelCase				:					Optional[str] = "pil"	,				_lowerCamelCase				:					bool = True	,				)							->					Any:

										if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = 1
										elif isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = len(_lowerCamelCase  )
										else:
														raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_lowerCamelCase  )}'  )

										__magic_name__	  = self._execution_device

										__magic_name__	  = batch_size * num_images_per_prompt

										__magic_name__	  = guidance_scale > 1.0
										__magic_name__	  = self._encode_prompt(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										# prior

										self.scheduler.set_timesteps(_lowerCamelCase	,				device=_lowerCamelCase  )
										__magic_name__	  = self.scheduler.timesteps

										__magic_name__	  = self.prior.config.num_embeddings
										__magic_name__	  = self.prior.config.embedding_dim

										__magic_name__	  = self.prepare_latents(
										    (batch_size, num_embeddings * embedding_dim)	,				prompt_embeds.dtype	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				self.scheduler	,				)

										# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
										__magic_name__	  = latents.reshape(latents.shape[0]	,				_lowerCamelCase	,				_lowerCamelCase  )

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

														__magic_name__	  = self.prior(
														    _lowerCamelCase	,				timestep=_lowerCamelCase	,				proj_embedding=_lowerCamelCase	,				).predicted_image_embedding

														# remove the variance
														__magic_name__  ,      __magic_name__	  = noise_pred.split(
														    scaled_model_input.shape[2]	,				dim=2  )  # batch_size, num_embeddings, embedding_dim

														if do_classifier_free_guidance is not None:
																		__magic_name__  ,      __magic_name__	  = noise_pred.chunk(2  )
																		__magic_name__	  = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

														__magic_name__	  = self.scheduler.step(
														    _lowerCamelCase	,				timestep=_lowerCamelCase	,				sample=_lowerCamelCase	,				).prev_sample

										if output_type == "latent":
														return ShapEPipelineOutput(images=_lowerCamelCase  )

										__magic_name__	  = []
										for i, latent in enumerate(_lowerCamelCase  ):
														__magic_name__	  = self.renderer.decode(
														    latent[None, :]	,				_lowerCamelCase	,				size=_lowerCamelCase	,				ray_batch_size=40_96	,				n_coarse_samples=64	,				n_fine_samples=1_28	,				)
														images.append(_lowerCamelCase  )

										__magic_name__	  = torch.stack(_lowerCamelCase  )

										if output_type not in ["np", "pil"]:
														raise ValueError(f'Only the output types `pil` and `np` are supported not output_type={output_type}'  )

										__magic_name__	  = images.cpu().numpy()

										if output_type == "pil":
														__magic_name__	  = [self.numpy_to_pil(_lowerCamelCase  ) for image in images]

										# 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 (images,)

										return ShapEPipelineOutput(images=_lowerCamelCase  )







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

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

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




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




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


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=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.''')



