



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	StableDiffusionInpaintPipeline
	lowerCAmelCase	:   str				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	lowerCAmelCase	:   Any				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowerCAmelCase	:   str				=	frozenset(
	    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowerCAmelCase	:   Optional[Any]				=	frozenset([]     )



	def 			__A							(       self		):
							torch.manual_seed(0		)
							A__							=       UNetaDConditionModel(
							    block_out_channels=(32, 64)			,	layers_per_block=2			,	sample_size=32			,	in_channels=9			,	out_channels=4			,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,	cross_attention_dim=32			,	attention_head_dim=(2, 4)			,	use_linear_projection=UpperCAmelCase__			,	)
							A__							=       PNDMScheduler(skip_prk_steps=UpperCAmelCase__		)
							torch.manual_seed(0		)
							A__							=       AutoencoderKL(
							    block_out_channels=[32, 64]			,	in_channels=3			,	out_channels=3			,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,	latent_channels=4			,	sample_size=128			,	)
							torch.manual_seed(0		)
							A__							=       CLIPTextConfig(
							    bos_token_id=0			,	eos_token_id=2			,	hidden_size=32			,	intermediate_size=37			,	layer_norm_eps=1e-0_5			,	num_attention_heads=4			,	num_hidden_layers=5			,	pad_token_id=1			,	vocab_size=1_000			,	hidden_act="gelu"			,	projection_dim=512			,	)
							A__							=       CLIPTextModel(UpperCAmelCase__		)
							A__							=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

							A__							=       {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
							return components



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0		):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							A__							=       floats_tensor((1, 3, 32, 32)			,	rng=random.Random(UpperCAmelCase__		)		).to(UpperCAmelCase__		)
							A__							=       image.cpu().permute(0			,	2			,	3			,	1		)[0]
							A__							=       Image.fromarray(np.uinta(UpperCAmelCase__		)		).convert("RGB"		).resize((64, 64)		)
							A__							=       Image.fromarray(np.uinta(image + 4		)		).convert("RGB"		).resize((64, 64)		)
							if str(UpperCAmelCase__		).startswith("mps"		):
													A__							=       torch.manual_seed(UpperCAmelCase__		)
							else:
													A__							=       torch.Generator(device=UpperCAmelCase__		).manual_seed(UpperCAmelCase__		)
							A__							=       {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs



	def 			__A							(       self		):
							A__							=       "cpu"  # ensure determinism for the device-dependent torch.Generator
							A__							=       self.get_dummy_components()
							A__							=       StableDiffusionInpaintPipeline(**UpperCAmelCase__		)
							A__							=       sd_pipe.to(UpperCAmelCase__		)
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

							A__							=       self.get_dummy_inputs(UpperCAmelCase__		)
							A__							=       sd_pipe(**UpperCAmelCase__		).images
							A__							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							A__							=       np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2



	def 			__A							(       self		):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self		):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



	def 			__A							(       self		):
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"		)
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
							A__							=       load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
							    "/yellow_cat_sitting_on_a_park_bench.npy"		)

							A__							=       "stabilityai/stable-diffusion-2-inpainting"
							A__							=       StableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase__			,	safety_checker=UpperCAmelCase__		)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
							pipe.enable_attention_slicing()

							A__							=       "Face of a yellow cat, high resolution, sitting on a park bench"

							A__							=       torch.manual_seed(0		)
							A__							=       pipe(
							    prompt=UpperCAmelCase__			,	image=UpperCAmelCase__			,	mask_image=UpperCAmelCase__			,	generator=UpperCAmelCase__			,	output_type="np"			,	)
							A__							=       output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 9e-3



	def 			__A							(       self		):
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"		)
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)
							A__							=       load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
							    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"		)

							A__							=       "stabilityai/stable-diffusion-2-inpainting"
							A__							=       StableDiffusionInpaintPipeline.from_pretrained(
							    UpperCAmelCase__			,	torch_dtype=torch.floataa			,	safety_checker=UpperCAmelCase__			,	)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
							pipe.enable_attention_slicing()

							A__							=       "Face of a yellow cat, high resolution, sitting on a park bench"

							A__							=       torch.manual_seed(0		)
							A__							=       pipe(
							    prompt=UpperCAmelCase__			,	image=UpperCAmelCase__			,	mask_image=UpperCAmelCase__			,	generator=UpperCAmelCase__			,	output_type="np"			,	)
							A__							=       output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image		).max() < 5e-1



	def 			__A							(       self		):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-inpaint/init_image.png"		)
							A__							=       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"		)

							A__							=       "stabilityai/stable-diffusion-2-inpainting"
							A__							=       PNDMScheduler.from_pretrained(UpperCAmelCase__			,	subfolder="scheduler"		)
							A__							=       StableDiffusionInpaintPipeline.from_pretrained(
							    UpperCAmelCase__			,	safety_checker=UpperCAmelCase__			,	scheduler=UpperCAmelCase__			,	torch_dtype=torch.floataa			,	)
							pipe.to(UpperCAmelCase__		)
							pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
							pipe.enable_attention_slicing(1		)
							pipe.enable_sequential_cpu_offload()

							A__							=       "Face of a yellow cat, high resolution, sitting on a park bench"

							A__							=       torch.manual_seed(0		)
							A__							=       pipe(
							    prompt=UpperCAmelCase__			,	image=UpperCAmelCase__			,	mask_image=UpperCAmelCase__			,	generator=UpperCAmelCase__			,	num_inference_steps=2			,	output_type="np"			,	)

							A__							=       torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9










import argparse
import struct
import unittest






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__		):
							A__							=       data

							# Initialize hash values
							A__							=       [
							    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
							A__							=       [
							    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,
							]

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



	@staticmethod
	def 			__A							(       UpperCAmelCase__		):
							A__							=       b"\x80" + (b"\x00" * (63 - (len(UpperCAmelCase__		) + 8) % 64))
							A__							=       struct.pack(">Q"			,	(len(UpperCAmelCase__		) * 8)		)
							return data + padding + big_endian_integer



	def 			__A							(       self		):
							# Convert into blocks of 64 bytes
							A__							=       [
							    self.preprocessed_data[x : x + 64]
							    for x in range(0			,	len(self.preprocessed_data		)			,	64		)
							]

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

													A__						,       A__						,       A__						,       A__						,       A__						,       A__						,       A__						,       A__							=       self.hashes

													for index in range(0			,	64		):
																			if index > 15:
																									# modify the zero-ed indexes at the end of the array
																									A__							=       (
																									    self.ror(words[index - 15]			,	7		)
																									    ^ self.ror(words[index - 15]			,	18		)
																									    ^ (words[index - 15] >> 3)
																									)
																									A__							=       (
																									    self.ror(words[index - 2]			,	17		)
																									    ^ self.ror(words[index - 2]			,	19		)
																									    ^ (words[index - 2] >> 10)
																									)

																									A__							=       (
																									    words[index - 16] + sa + words[index - 7] + sa
																									) % 0x100_000_000

																			# Compression
																			A__							=       self.ror(UpperCAmelCase__			,	6		) ^ self.ror(UpperCAmelCase__			,	11		) ^ self.ror(UpperCAmelCase__			,	25		)
																			A__							=       (e & f) ^ ((~e & 0xFF_FFF_FFF) & g)
																			A__							=       (
																			    h + sa + ch + self.round_constants[index] + words[index]
																			) % 0x100_000_000
																			A__							=       self.ror(UpperCAmelCase__			,	2		) ^ self.ror(UpperCAmelCase__			,	13		) ^ self.ror(UpperCAmelCase__			,	22		)
																			A__							=       (a & b) ^ (a & c) ^ (b & c)
																			A__							=       (sa + maj) % 0x100_000_000

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

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

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

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							return 0xFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)






class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self		):
							import hashlib

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





def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						import doctest

						doctest.testmod()

						A__							=       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"    )

						A__							=       parser.parse_args()

						A__							=       args.input_string

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

						print(SHAaaa(_A    ).hash    )


if __name__ == "__main__":
						main()





