


"""simple docstring"""

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

a_			   =					'''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''







@dataclass
class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[PIL.Image.Image, np.ndarray]







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											super().__init__()

											self.register_modules(
											    prior=lowercase__   ,		image_encoder=lowercase__   ,		image_processor=lowercase__   ,		scheduler=lowercase__   ,		renderer=lowercase__   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											if latents is None:
																snake_case_					:		Any 					=					randn_tensor(lowercase__   ,		generator=lowercase__   ,		device=lowercase__   ,		dtype=lowercase__       )
											else:
																if latents.shape != shape:
																					raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'       )
																snake_case_					:		Tuple 					=					latents.to(lowercase__       )

											snake_case_					:		Any 					=					latents * scheduler.init_noise_sigma
											return latents







						def 						__UpperCamelCase							(self   ,		lowercase__=0       ):
											if is_accelerate_available():
																from accelerate import cpu_offload
											else:
																raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

											snake_case_					:		List[Any] 					=					torch.device(f'cuda:{gpu_id}'       )

											snake_case_					:		List[str] 					=					[self.image_encoder, self.prior]
											for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																					cpu_offload(lowercase__   ,		lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											if self.device != torch.device("""meta"""       ) or not hasattr(self.image_encoder   ,		"""_hf_hook"""       ):
																return self.device
											for module in self.image_encoder.modules():
																if (
																    hasattr(lowercase__   ,		"""_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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											if isinstance(lowercase__   ,		lowercase__       ) and isinstance(image[0]   ,		torch.Tensor       ):
																snake_case_					:		Tuple 					=					torch.cat(lowercase__   ,		axis=0       ) if image[0].ndim == 4 else torch.stack(lowercase__   ,		axis=0       )

											if not isinstance(lowercase__   ,		torch.Tensor       ):
																snake_case_					:		List[str] 					=					self.image_processor(lowercase__   ,		return_tensors="""pt"""       ).pixel_values[0].unsqueeze(0       )

											snake_case_					:		List[Any] 					=					image.to(dtype=self.image_encoder.dtype   ,		device=lowercase__       )

											snake_case_					:		int 					=					self.image_encoder(lowercase__       )["""last_hidden_state"""]
											snake_case_					:		Any 					=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

											snake_case_					:		List[str] 					=					image_embeds.repeat_interleave(lowercase__   ,		dim=0       )

											if do_classifier_free_guidance:
																snake_case_					:		Any 					=					torch.zeros_like(lowercase__       )

																# 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
																snake_case_					:		List[str] 					=					torch.cat([negative_image_embeds, image_embeds]       )

											return image_embeds







						@torch.no_grad()
						@replace_example_docstring(lowercase__       )
						def __call__(self   ,		lowercase__   ,		lowercase__ = 1   ,		lowercase__ = 25   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = 4.0   ,		lowercase__ = 64   ,		lowercase__ = "pil"   ,		lowercase__ = True   ,		):

											if isinstance(lowercase__   ,		PIL.Image.Image       ):
																snake_case_					:		Dict 					=					1
											elif isinstance(lowercase__   ,		torch.Tensor       ):
																snake_case_					:		str 					=					image.shape[0]
											elif isinstance(lowercase__   ,		lowercase__       ) and isinstance(image[0]   ,		(torch.Tensor, PIL.Image.Image)       ):
																snake_case_					:		Union[str, Any] 					=					len(lowercase__       )
											else:
																raise ValueError(
																    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase__       )}'       )

											snake_case_					:		Tuple 					=					self._execution_device

											snake_case_					:		int 					=					batch_size * num_images_per_prompt

											snake_case_					:		Optional[int] 					=					guidance_scale > 1.0
											snake_case_					:		List[str] 					=					self._encode_image(lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__       )

											# prior

											self.scheduler.set_timesteps(lowercase__   ,		device=lowercase__       )
											snake_case_					:		Any 					=					self.scheduler.timesteps

											snake_case_					:		int 					=					self.prior.config.num_embeddings
											snake_case_					:		Optional[int] 					=					self.prior.config.embedding_dim

											snake_case_					:		Tuple 					=					self.prepare_latents(
											    (batch_size, num_embeddings * embedding_dim)   ,		image_embeds.dtype   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		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
											snake_case_					:		Optional[int] 					=					latents.reshape(latents.shape[0]   ,		lowercase__   ,		lowercase__       )

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

																snake_case_					:		List[Any] 					=					self.prior(
																    lowercase__   ,		timestep=lowercase__   ,		proj_embedding=lowercase__   ,		).predicted_image_embedding

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

																if do_classifier_free_guidance is not None:
																					snake_case_					:		List[Any] 					=					noise_pred.chunk(2       )
																					snake_case_					:		Tuple 					=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																snake_case_					:		int 					=					self.scheduler.step(
																    lowercase__   ,		timestep=lowercase__   ,		sample=lowercase__   ,		).prev_sample

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

											snake_case_					:		List[str] 					=					[]
											for i, latent in enumerate(lowercase__       ):
																print()
																snake_case_					:		int 					=					self.renderer.decode(
																    latent[None, :]   ,		lowercase__   ,		size=lowercase__   ,		ray_batch_size=40_96   ,		n_coarse_samples=64   ,		n_fine_samples=1_28   ,		)

																images.append(lowercase__       )

											snake_case_					:		Any 					=					torch.stack(lowercase__       )

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

											snake_case_					:		Optional[Any] 					=					images.cpu().numpy()

											if output_type == "pil":
																snake_case_					:		List[str] 					=					[self.numpy_to_pil(lowercase__       ) 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=lowercase__       )









"""simple docstring"""

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					data
											snake_case_					:		List[str] 					=					[0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]







						@staticmethod
						def 						__UpperCamelCase							(lowercase__   ,		lowercase__       ):
											return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					B"""\x80""" + B"""\x00""" * (63 - (len(self.data       ) + 8) % 64)
											snake_case_					:		Tuple 					=					self.data + padding + struct.pack(""">Q"""   ,		8 * len(self.data       )       )
											return padded_data







						def 						__UpperCamelCase							(self       ):
											return [
											    self.padded_data[i : i + 64] for i in range(0   ,		len(self.padded_data       )   ,		64       )
											]







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					list(struct.unpack(""">16L"""   ,		lowercase__       )       ) + [0] * 64
											for i in range(16   ,		80       ):
																snake_case_					:		Dict 					=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])   ,		1       )
											return w







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.padding()
											snake_case_					:		Any 					=					self.split_blocks()
											for block in self.blocks:
																snake_case_					:		Any 					=					self.expand_block(lowercase__       )
																snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					self.h
																for i in range(0   ,		80       ):
																					if 0 <= i < 20:
																										snake_case_					:		Optional[Any] 					=					(b & c) | ((~b) & d)
																										snake_case_					:		List[str] 					=					0X5_a_8_2_7_9_9_9
																					elif 20 <= i < 40:
																										snake_case_					:		Union[str, Any] 					=					b ^ c ^ d
																										snake_case_					:		Tuple 					=					0X6_e_d_9_e_b_a_1
																					elif 40 <= i < 60:
																										snake_case_					:		str 					=					(b & c) | (b & d) | (c & d)
																										snake_case_					:		List[str] 					=					0X8_f_1_b_b_c_d_c
																					elif 60 <= i < 80:
																										snake_case_					:		Tuple 					=					b ^ c ^ d
																										snake_case_					:		str 					=					0Xc_a_6_2_c_1_d_6
																					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Optional[Any] 					=					(
																					    self.rotate(lowercase__   ,		5       ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																					    a,
																					    self.rotate(lowercase__   ,		30       ),
																					    c,
																					    d,
																					)
																snake_case_					:		Any 					=					(
																    self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
																    self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
																    self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
																    self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
																    self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
																)
											return ("{:08x}" * 5).format(*self.h       )







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					b"""Test String"""
					assert SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__			).hexdigest()  # noqa: S324







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					argparse.ArgumentParser(description="""Process some strings or files"""			)
					parser.add_argument(
					    """--string"""				,    dest="""input_string"""				,    default="""Hello World!! Welcome to Cryptography"""				,    help="""Hash the string"""				,    )
					parser.add_argument("""--file"""				,    dest="""input_file"""				,    help="""Hash contents of a file"""			)
					snake_case_					:		Optional[int] 					=					parser.parse_args()
					snake_case_					:		Optional[int] 					=					args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file				,    """rb"""			) as f:
															snake_case_					:		List[str] 					=					f.read()
					else:
										snake_case_					:		Dict 					=					bytes(SCREAMING_SNAKE_CASE__				,    """utf-8"""			)
					print(SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash()			)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod()




