






import argparse
import struct
import unittest



class 							_UpperCamelCase			:


      def __init__(	self					,						__UpperCamelCase	)->	None:
           __lowerCAmelCase								=					data

           # Initialize hash values
           __lowerCAmelCase								=					[
               0x6A_09E_667,
               0xBB_67A_E85,
               0x3C_6EF_372,
               0xA5_4FF_53A,
               0x51_0E5_27F,
               0x9B_056_88C,
               0x1F_83D_9AB,
               0x5B_E0C_D19,
           ]

           # Initialize round constants
           __lowerCAmelCase								=					[
               0x42_8A2_F98,
               0x71_374_491,
               0xB5_C0F_BCF,
               0xE9_B5D_BA5,
               0x39_56C_25B,
               0x59_F11_1F1,
               0x92_3F8_2A4,
               0xAB_1C5_ED5,
               0xD8_07A_A98,
               0x12_835_B01,
               0x24_318_5BE,
               0x55_0C7_DC3,
               0x72_BE5_D74,
               0x80_DEB_1FE,
               0x9B_DC0_6A7,
               0xC1_9BF_174,
               0xE4_9B6_9C1,
               0xEF_BE4_786,
               0x0F_C19_DC6,
               0x24_0CA_1CC,
               0x2D_E92_C6F,
               0x4A_748_4AA,
               0x5C_B0A_9DC,
               0x76_F98_8DA,
               0x98_3E5_152,
               0xA8_31C_66D,
               0xB0_032_7C8,
               0xBF_597_FC7,
               0xC6_E00_BF3,
               0xD5_A79_147,
               0x06_CA6_351,
               0x14_292_967,
               0x27_B70_A85,
               0x2E_1B2_138,
               0x4D_2C6_DFC,
               0x53_380_D13,
               0x65_0A7_354,
               0x76_6A0_ABB,
               0x81_C2C_92E,
               0x92_722_C85,
               0xA2_BFE_8A1,
               0xA8_1A6_64B,
               0xC2_4B8_B70,
               0xC7_6C5_1A3,
               0xD1_92E_819,
               0xD6_990_624,
               0xF4_0E3_585,
               0x10_6AA_070,
               0x19_A4C_116,
               0x1E_376_C08,
               0x27_487_74C,
               0x34_B0B_CB5,
               0x39_1C0_CB3,
               0x4E_D8A_A4A,
               0x5B_9CC_A4F,
               0x68_2E6_FF3,
               0x74_8F8_2EE,
               0x78_A56_36F,
               0x84_C87_814,
               0x8C_C70_208,
               0x90_BEF_FFA,
               0xA4_506_CEB,
               0xBE_F9A_3F7,
               0xC6_717_8F2,
           ]

           __lowerCAmelCase								=					self.preprocessing(self.data	)
           self.final_hash()


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	bytes:
           __lowerCAmelCase								=					b"\x80" + (b"\x00" * (6_3 - (len(__UpperCamelCase	) + 8) % 6_4))
           __lowerCAmelCase								=					struct.pack(">Q"					,						(len(__UpperCamelCase	) * 8)	)
           return data + padding + big_endian_integer


      def  __UpperCAmelCase		(	self	)->	None:
           # Convert into blocks of 64 bytes
           __lowerCAmelCase								=					[
               self.preprocessed_data[x : x + 6_4]
               for x in range(0					,						len(self.preprocessed_data	)					,						6_4	)
           ]

           for block in self.blocks:
                # Convert the given block into a list of 4 byte integers
                __lowerCAmelCase								=					list(struct.unpack(">16L"					,						__UpperCamelCase	)	)
                # add 48 0-ed integers
                words += [0] * 4_8

                __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					self.hashes

                for index in range(0					,						6_4	):
                     if index > 1_5:
                          # modify the zero-ed indexes at the end of the array
                          __lowerCAmelCase								=					(
                              self.ror(words[index - 1_5]					,						7	)
                              ^ self.ror(words[index - 1_5]					,						1_8	)
                              ^ (words[index - 1_5] >> 3)
                          )
                          __lowerCAmelCase								=					(
                              self.ror(words[index - 2]					,						1_7	)
                              ^ self.ror(words[index - 2]					,						1_9	)
                              ^ (words[index - 2] >> 1_0)
                          )

                          __lowerCAmelCase								=					(
                              words[index - 1_6] + sa + words[index - 7] + sa
                          ) % 0x100_000_000

                     # Compression
                     __lowerCAmelCase								=					self.ror(__UpperCamelCase					,						6	) ^ self.ror(__UpperCamelCase					,						1_1	) ^ self.ror(__UpperCamelCase					,						2_5	)
                     __lowerCAmelCase								=					(e & f) ^ ((~e & 0xFF_FFF_FFF) & g)
                     __lowerCAmelCase								=					(
                         h + sa + ch + self.round_constants[index] + words[index]
                     ) % 0x100_000_000
                     __lowerCAmelCase								=					self.ror(__UpperCamelCase					,						2	) ^ self.ror(__UpperCamelCase					,						1_3	) ^ self.ror(__UpperCamelCase					,						2_2	)
                     __lowerCAmelCase								=					(a & b) ^ (a & c) ^ (b & c)
                     __lowerCAmelCase								=					(sa + maj) % 0x100_000_000

                     __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					(
                         g,
                         f,
                         e,
                         ((d + tempa) % 0x100_000_000),
                         c,
                         b,
                         a,
                         ((tempa + tempa) % 0x100_000_000),
                     )

                __lowerCAmelCase								=					[a, b, c, d, e, f, g, h]

                # Modify final values
                __lowerCAmelCase								=					[
                    ((element + mutated_hash_values[index]) % 0x100_000_000)
                    for index, element in enumerate(self.hashes	)
                ]

           __lowerCAmelCase								=					"".join([hex(__UpperCamelCase	)[2:].zfill(8	) for value in self.hashes]	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           return 0xFF_FFF_FFF & (value << (3_2 - rotations)) | (value >> rotations)



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	None:
           import hashlib

           __lowerCAmelCase								=					bytes("Test String"					,						"utf-8"	)
           self.assertEqual(SHAaaa(__UpperCamelCase	).hash					,						hashlib.shaaaa(__UpperCamelCase	).hexdigest()	)





def       __lowerCAmelCase      (						):
     import doctest

     doctest.testmod()

     __lowerCAmelCase								=					argparse.ArgumentParser()
     parser.add_argument(
         "-s"					, "--string"					, dest="input_string"					, default="Hello World!! Welcome to Cryptography"					, help="Hash the string"					, )
     parser.add_argument(
         "-f"					, "--file"					, dest="input_file"					, help="Hash contents of a file"       )

     __lowerCAmelCase								=					parser.parse_args()

     __lowerCAmelCase								=					args.input_string

     # hash input should be a bytestring
     if args.input_file:
          with open(args.input_file					, "rb"       ) as f:
               __lowerCAmelCase								=					f.read()
     else:
          __lowerCAmelCase								=					bytes(__snake_case					, "utf-8"       )

     print(SHAaaa(__snake_case       ).hash       )


if __name__ == "__main__":
 main()














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


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

lowerCamelCase			:					str         =	'''
    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 							_UpperCamelCase			(a_			):
      snake_case_  =							42



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Dict:
           super().__init__()

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if latents is None:
                __lowerCAmelCase								=					randn_tensor(__UpperCamelCase					,						generator=__UpperCamelCase					,						device=__UpperCamelCase					,						dtype=__UpperCamelCase	)
           else:
                if latents.shape != shape:
                     raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
                __lowerCAmelCase								=					latents.to(__UpperCamelCase	)

           __lowerCAmelCase								=					latents * scheduler.init_noise_sigma
           return latents


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

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

           __lowerCAmelCase								=					[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                     cpu_offload(__UpperCamelCase					,						__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           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(__UpperCamelCase					,						"_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					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Union[str, Any]:
           if isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						torch.Tensor	):
                __lowerCAmelCase								=					torch.cat(__UpperCamelCase					,						axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCamelCase					,						axis=0	)

           if not isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors="pt"	).pixel_values[0].unsqueeze(0	)

           __lowerCAmelCase								=					image.to(dtype=self.image_encoder.dtype					,						device=__UpperCamelCase	)

           __lowerCAmelCase								=					self.image_encoder(__UpperCamelCase	)["last_hidden_state"]
           __lowerCAmelCase								=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           __lowerCAmelCase								=					image_embeds.repeat_interleave(__UpperCamelCase					,						dim=0	)

           if do_classifier_free_guidance:
                __lowerCAmelCase								=					torch.zeros_like(__UpperCamelCase	)

                # 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
                __lowerCAmelCase								=					torch.cat([negative_image_embeds, image_embeds]	)

           return image_embeds


      @torch.no_grad()
      @replace_example_docstring(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 2_5					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 4.0					,						__UpperCamelCase = 6_4					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						)->	Dict:

           if isinstance(__UpperCamelCase					,						PIL.Image.Image	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					image.shape[0]
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						(torch.Tensor, PIL.Image.Image)	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           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(__UpperCamelCase	)}"""	)

           __lowerCAmelCase								=					self._execution_device

           __lowerCAmelCase								=					batch_size * num_images_per_prompt

           __lowerCAmelCase								=					guidance_scale > 1.0
           __lowerCAmelCase								=					self._encode_image(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # prior

           self.scheduler.set_timesteps(__UpperCamelCase					,						device=__UpperCamelCase	)
           __lowerCAmelCase								=					self.scheduler.timesteps

           __lowerCAmelCase								=					self.prior.config.num_embeddings
           __lowerCAmelCase								=					self.prior.config.embedding_dim

           __lowerCAmelCase								=					self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)					,						image_embeds.dtype					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						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
           __lowerCAmelCase								=					latents.reshape(latents.shape[0]					,						__UpperCamelCase					,						__UpperCamelCase	)

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

                __lowerCAmelCase								=					self.prior(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						proj_embedding=__UpperCamelCase					,						).predicted_image_embedding

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

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

                __lowerCAmelCase								=					self.scheduler.step(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						sample=__UpperCamelCase					,						).prev_sample

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

           __lowerCAmelCase								=					[]
           for i, latent in enumerate(__UpperCamelCase	):
                print()
                __lowerCAmelCase								=					self.renderer.decode(
                    latent[None, :]					,						__UpperCamelCase					,						size=__UpperCamelCase					,						ray_batch_size=4_0_9_6					,						n_coarse_samples=6_4					,						n_fine_samples=1_2_8					,						)

                images.append(__UpperCamelCase	)

           __lowerCAmelCase								=					torch.stack(__UpperCamelCase	)

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

           __lowerCAmelCase								=					images.cpu().numpy()

           if output_type == "pil":
                __lowerCAmelCase								=					[self.numpy_to_pil(__UpperCamelCase	) 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=__UpperCamelCase	)






