

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





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A							):
												__lowerCAmelCase					=							data
												__lowerCAmelCase					=							[0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]




							@staticmethod
							def        A__  ( _A					,						_A							):
												return ((n << b) | (n >> (3_2 - b))) & 0xFFFFFFFF




							def        A__  ( self							):
												__lowerCAmelCase					=							b'\x80' + b'\x00' * (6_3 - (len(self.data							) + 8) % 6_4)
												__lowerCAmelCase					=							self.data + padding + struct.pack('>Q'					,						8 * len(self.data							)							)
												return padded_data




							def        A__  ( self							):
												return [
												    self.padded_data[i : i + 6_4] for i in range(0					,						len(self.padded_data							)					,						6_4							)
												]




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							list(struct.unpack('>16L'					,						_A							)							) + [0] * 6_4
												for i in range(1_6					,						8_0							):
																	__lowerCAmelCase					=							self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 1_4] ^ w[i - 1_6])					,						1							)
												return w




							def        A__  ( self							):
												__lowerCAmelCase					=							self.padding()
												__lowerCAmelCase					=							self.split_blocks()
												for block in self.blocks:
																	__lowerCAmelCase					=							self.expand_block(_A							)
																	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							self.h
																	for i in range(0					,						8_0							):
																						if 0 <= i < 2_0:
																											__lowerCAmelCase					=							(b & c) | ((~b) & d)
																											__lowerCAmelCase					=							0x5A827999
																						elif 2_0 <= i < 4_0:
																											__lowerCAmelCase					=							b ^ c ^ d
																											__lowerCAmelCase					=							0x6ED9EBA1
																						elif 4_0 <= i < 6_0:
																											__lowerCAmelCase					=							(b & c) | (b & d) | (c & d)
																											__lowerCAmelCase					=							0x8F1BBCDC
																						elif 6_0 <= i < 8_0:
																											__lowerCAmelCase					=							b ^ c ^ d
																											__lowerCAmelCase					=							0xCA62C1D6
																						__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							(
																						    self.rotate(_A					,						5							) + f + e + k + expanded_block[i] & 0xFFFFFFFF,
																						    a,
																						    self.rotate(_A					,						3_0							),
																						    c,
																						    d,
																						)
																	__lowerCAmelCase					=							(
																	    self.h[0] + a & 0xFFFFFFFF,
																	    self.h[1] + b & 0xFFFFFFFF,
																	    self.h[2] + c & 0xFFFFFFFF,
																	    self.h[3] + d & 0xFFFFFFFF,
																	    self.h[4] + e & 0xFFFFFFFF,
																	)
												return ("{:08x}" * 5).format(*self.h							)


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							B'Test String'
					assert SHAaHash(UpperCAmelCase__      ).final_hash() == hashlib.shaa(UpperCAmelCase__      ).hexdigest()  # noqa: S324


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							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'      )
					__lowerCAmelCase					=							parser.parse_args()
					__lowerCAmelCase					=							args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file		,					'rb'      ) as f:
															__lowerCAmelCase					=							f.read()
					else:
										__lowerCAmelCase					=							bytes(UpperCAmelCase__		,					'utf-8'      )
					print(SHAaHash(UpperCAmelCase__      ).final_hash()      )


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

		doctest.testmod()









from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowerCamelCase      				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=8      ):

					"""simple docstring"""






					__lowerCAmelCase					=							height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase					=							width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=512		,					UpperCAmelCase__=512      ):

					"""simple docstring"""






					__lowerCAmelCase					=							pil_image.resize((w, h)		,					resample=Image.BICUBIC		,					reducing_gap=1      )
					__lowerCAmelCase					=							np.array(pil_image.convert('RGB'      )      )
					__lowerCAmelCase					=							arr.astype(np.floataa      ) / 127.5 - 1
					__lowerCAmelCase					=							np.transpose(UpperCAmelCase__		,					[2, 0, 1]      )
					__lowerCAmelCase					=							torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
					return image





class     snake_case_			(				_a							):


							"""simple docstring"""





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

												self.register_modules(
												    unet=_A					,						scheduler=_A					,						movq=_A					,						)
												__lowerCAmelCase					=							2 ** (len(self.movq.config.block_out_channels							) - 1)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												# get the original timestep using init_timestep
												__lowerCAmelCase					=							min(int(num_inference_steps * strength							)					,						_A							)

												__lowerCAmelCase					=							max(num_inference_steps - init_timestep					,						0							)
												__lowerCAmelCase					=							self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=None							):
												if not isinstance(_A					,						(torch.Tensor, PIL.Image.Image, list)							):
																	raise ValueError(
																	    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A							)}"""							)

												__lowerCAmelCase					=							image.to(device=_A					,						dtype=_A							)

												__lowerCAmelCase					=							batch_size * num_images_per_prompt

												if image.shape[1] == 4:
																	__lowerCAmelCase					=							image

												else:
																	if isinstance(_A					,						_A							) and len(_A							) != batch_size:
																						raise ValueError(
																						    F"""You have passed a list of generators of length {len(_A							)}, but requested an effective batch"""
																						    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

																	elif isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[
																						    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_A							)
																						]
																						__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
																	else:
																						__lowerCAmelCase					=							self.movq.encode(_A							).latent_dist.sample(_A							)

																	__lowerCAmelCase					=							self.movq.config.scaling_factor * init_latents

												__lowerCAmelCase					=							torch.cat([init_latents]					,						dim=0							)

												__lowerCAmelCase					=							init_latents.shape
												__lowerCAmelCase					=							randn_tensor(_A					,						generator=_A					,						device=_A					,						dtype=_A							)

												# get latents
												__lowerCAmelCase					=							self.scheduler.add_noise(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							init_latents

												return latents




							def        A__  ( self					,						_A=0							):
												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.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(_A					,						_A							)




							def        A__  ( self					,						_A=0							):
												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.'							)

												__lowerCAmelCase					=							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)

												__lowerCAmelCase					=							None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	__lowerCAmelCase,	__lowerCAmelCase					=							cpu_offload_with_hook(_A					,						_A					,						prev_module_hook=_A							)

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        A__  ( self							):
												if not hasattr(self.unet					,						'_hf_hook'							):
																	return self.device
												for module in self.unet.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




							@torch.no_grad()
							@replace_example_docstring(_A							)
							def __call__( self					,						_A					,						_A					,						_A					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 1_0_0					,						_A = 4.0					,						_A = 0.3					,						_A = 1					,						_A = None					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							self._execution_device

												__lowerCAmelCase					=							guidance_scale > 1.0

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)
												__lowerCAmelCase					=							image_embeds.shape[0]
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							torch.cat(_A					,						dim=0							)

												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							image_embeds.repeat_interleave(_A					,						dim=0							)
																	__lowerCAmelCase					=							negative_image_embeds.repeat_interleave(_A					,						dim=0							)

												__lowerCAmelCase					=							torch.cat([negative_image_embeds, image_embeds]					,						dim=0							).to(dtype=self.unet.dtype					,						device=_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[image]
												if not all(isinstance(_A					,						(PIL.Image.Image, torch.Tensor)							) for i in image							):
																	raise ValueError(
																	    F"""Input is in incorrect format: {[type(_A							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

												__lowerCAmelCase					=							torch.cat([prepare_image(_A					,						_A					,						_A							) for i in image]					,						dim=0							)
												__lowerCAmelCase					=							image.to(dtype=image_embeds.dtype					,						device=_A							)

												__lowerCAmelCase					=							self.movq.encode(_A							)['latents']
												__lowerCAmelCase					=							latents.repeat_interleave(_A					,						dim=0							)
												self.scheduler.set_timesteps(_A					,						device=_A							)
												__lowerCAmelCase,	__lowerCAmelCase					=							self.get_timesteps(_A					,						_A					,						_A							)
												__lowerCAmelCase					=							timesteps[:1].repeat(batch_size * num_images_per_prompt							)
												__lowerCAmelCase,	__lowerCAmelCase					=							downscale_height_and_width(_A					,						_A					,						self.movq_scale_factor							)
												__lowerCAmelCase					=							self.prepare_latents(
												    _A					,						_A					,						_A					,						_A					,						image_embeds.dtype					,						_A					,						_A							)
												for i, t in enumerate(self.progress_bar(_A							)							):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase					=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																	__lowerCAmelCase					=							{'image_embeds': image_embeds}
																	__lowerCAmelCase					=							self.unet(
																	    sample=_A					,						timestep=_A					,						encoder_hidden_states=_A					,						added_cond_kwargs=_A					,						return_dict=_A					,						)[0]

																	if do_classifier_free_guidance:
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.chunk(2							)
																						__lowerCAmelCase,	__lowerCAmelCase					=							variance_pred.chunk(2							)
																						__lowerCAmelCase					=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						__lowerCAmelCase					=							torch.cat([noise_pred, variance_pred_text]					,						dim=1							)

																	if not (
																	    hasattr(self.scheduler.config					,						'variance_type'							)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.split(latents.shape[1]					,						dim=1							)

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(
																	    _A					,						_A					,						_A					,						generator=_A					,						)[0]

												# post-processing
												__lowerCAmelCase					=							self.movq.decode(_A					,						force_not_quantize=_A							)['sample']

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

												if output_type in ["np", "pil"]:
																	__lowerCAmelCase					=							image * 0.5 + 0.5
																	__lowerCAmelCase					=							image.clamp(0					,						1							)
																	__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

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

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)






