



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


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

UpperCAmelCase__  =			"\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      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	42







class      __lowerCAmelCase       (     A		):



			def __init__(		self  :    Dict					,						A  :    PriorTransformer					,						A  :    CLIPTextModelWithProjection					,						A  :    CLIPTokenizer					,						A  :    HeunDiscreteScheduler					,						A  :    ShapERenderer					,						)  ->       Any:



						"""simple docstring"""






						super().__init__()

						self.register_modules(
						    prior=A					,						text_encoder=A					,						tokenizer=A					,						scheduler=A					,						renderer=A					,						)



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    List[str]					,						A  :    Optional[Any]					,						A  :    Tuple					,						A  :    Optional[Any]					,						A  :    List[Any]					,						A  :    List[Any])  ->       Union[str, Any]:



						"""simple docstring"""






						if latents is None:
									_UpperCAmelCase									=  randn_tensor(A					,						generator=A					,						device=A					,						dtype=A)
						else:
									if latents.shape != shape:
												raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
									_UpperCAmelCase									=  latents.to(A)

						_UpperCAmelCase									=  latents * scheduler.init_noise_sigma
						return latents



			def        _lowerCamelCase			(		self  :    Tuple					,						A  :    Any=0)  ->       List[str]:



						"""simple docstring"""






						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError('Please install accelerate via `pip install accelerate`')

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

						_UpperCAmelCase									=  [self.text_encoder, self.prior]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(A					,						A)



			def        _lowerCamelCase			(		self  :    List[Any]					,						A  :    Any=0)  ->       Union[str, Any]:



						"""simple docstring"""






						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.')

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

						if self.device.type != "cpu":
									self.to('cpu'					,						silence_dtype_warnings=A)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

						if self.safety_checker is not None:
									_UpperCAmelCase			,	_UpperCAmelCase									=  cpu_offload_with_hook(self.safety_checker					,						A					,						prev_module_hook=A)

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



			@property
			def        _lowerCamelCase			(		self  :    str)  ->       Optional[Any]:



						"""simple docstring"""






						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(A					,						'_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        _lowerCamelCase			(		self  :    List[Any]					,						A  :    str					,						A  :    int					,						A  :    Dict					,						A  :    Any					,						)  ->       Optional[Any]:



						"""simple docstring"""






						len(A) if isinstance(A					,						A) else 1

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

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,						A):
									_UpperCAmelCase									=  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}")

						_UpperCAmelCase									=  self.text_encoder(text_input_ids.to(A))
						_UpperCAmelCase									=  text_encoder_output.text_embeds

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

						if do_classifier_free_guidance:
									_UpperCAmelCase									=  torch.zeros_like(A)

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

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

						return prompt_embeds



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



						"""simple docstring"""






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

						_UpperCAmelCase									=  self._execution_device

						_UpperCAmelCase									=  batch_size * num_images_per_prompt

						_UpperCAmelCase									=  guidance_scale > 1.0
						_UpperCAmelCase									=  self._encode_prompt(A					,						A					,						A					,						A)

						# prior

						self.scheduler.set_timesteps(A					,						device=A)
						_UpperCAmelCase									=  self.scheduler.timesteps

						_UpperCAmelCase									=  self.prior.config.num_embeddings
						_UpperCAmelCase									=  self.prior.config.embedding_dim

						_UpperCAmelCase									=  self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim)					,						prompt_embeds.dtype					,						A					,						A					,						A					,						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
						_UpperCAmelCase									=  latents.reshape(latents.shape[0]					,						A					,						A)

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

									_UpperCAmelCase									=  self.prior(
									    A					,						timestep=A					,						proj_embedding=A					,						).predicted_image_embedding

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

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

									_UpperCAmelCase									=  self.scheduler.step(
									    A					,						timestep=A					,						sample=A					,						).prev_sample

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

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

						_UpperCAmelCase									=  torch.stack(A)

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

						_UpperCAmelCase									=  images.cpu().numpy()

						if output_type == "pil":
									_UpperCAmelCase									=  [self.numpy_to_pil(A) 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=A)





import sys

sys.set_int_max_str_digits(0)  # type: ignore





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			if not f:
						return f

			# mark first 9 numbers
			_UpperCAmelCase									=  int(str(_UpperCAmelCase       )[:9]       )

			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10

			# check first 9 numbers for pandigitality

			for x in range(9       ):
						if not check_front[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	)							->					int:







			'''simple docstring'''
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers

			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers mod 1e9

			# mod m=1e9, done for fast optimisation
			_UpperCAmelCase									=  [0] * 1_000_000
			_UpperCAmelCase									=  1_000_000_000

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  (aa + ba) % m
						_UpperCAmelCase									=  ba % m
						_UpperCAmelCase									=  ca % m
						if checka(_UpperCAmelCase       ):
									_UpperCAmelCase									=  1

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  a + b
						_UpperCAmelCase									=  b
						_UpperCAmelCase									=  c
						# perform check only if in tocheck
						if tocheck[x + 3] and check(_UpperCAmelCase       ):
									return x + 3  # first 2 already done
			return -1


if __name__ == "__main__":
							print(f"""{solution() = }""")
