

import argparse
import struct
import unittest

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			)       ->       None:
												_a      :       List[str]   =						data

												# Initialize hash values
												_a      :       int   =						[
												    0x6_a_0_9_e_6_6_7,
												    0xb_b_6_7_a_e_8_5,
												    0x3_c_6_e_f_3_7_2,
												    0xa_5_4_f_f_5_3_a,
												    0x5_1_0_e_5_2_7_f,
												    0x9_b_0_5_6_8_8_c,
												    0x1_f_8_3_d_9_a_b,
												    0x5_b_e_0_c_d_1_9,
												]

												# Initialize round constants
												_a      :       Tuple   =						[
												    0x4_2_8_a_2_f_9_8,
												    0x7_1_3_7_4_4_9_1,
												    0xb_5_c_0_f_b_c_f,
												    0xe_9_b_5_d_b_a_5,
												    0x3_9_5_6_c_2_5_b,
												    0x5_9_f_1_1_1_f_1,
												    0x9_2_3_f_8_2_a_4,
												    0xa_b_1_c_5_e_d_5,
												    0xd_8_0_7_a_a_9_8,
												    0x1_2_8_3_5_b_0_1,
												    0x2_4_3_1_8_5_b_e,
												    0x5_5_0_c_7_d_c_3,
												    0x7_2_b_e_5_d_7_4,
												    0x8_0_d_e_b_1_f_e,
												    0x9_b_d_c_0_6_a_7,
												    0xc_1_9_b_f_1_7_4,
												    0xe_4_9_b_6_9_c_1,
												    0xe_f_b_e_4_7_8_6,
												    0x0_f_c_1_9_d_c_6,
												    0x2_4_0_c_a_1_c_c,
												    0x2_d_e_9_2_c_6_f,
												    0x4_a_7_4_8_4_a_a,
												    0x5_c_b_0_a_9_d_c,
												    0x7_6_f_9_8_8_d_a,
												    0x9_8_3_e_5_1_5_2,
												    0xa_8_3_1_c_6_6_d,
												    0xb_0_0_3_2_7_c_8,
												    0xb_f_5_9_7_f_c_7,
												    0xc_6_e_0_0_b_f_3,
												    0xd_5_a_7_9_1_4_7,
												    0x0_6_c_a_6_3_5_1,
												    0x1_4_2_9_2_9_6_7,
												    0x2_7_b_7_0_a_8_5,
												    0x2_e_1_b_2_1_3_8,
												    0x4_d_2_c_6_d_f_c,
												    0x5_3_3_8_0_d_1_3,
												    0x6_5_0_a_7_3_5_4,
												    0x7_6_6_a_0_a_b_b,
												    0x8_1_c_2_c_9_2_e,
												    0x9_2_7_2_2_c_8_5,
												    0xa_2_b_f_e_8_a_1,
												    0xa_8_1_a_6_6_4_b,
												    0xc_2_4_b_8_b_7_0,
												    0xc_7_6_c_5_1_a_3,
												    0xd_1_9_2_e_8_1_9,
												    0xd_6_9_9_0_6_2_4,
												    0xf_4_0_e_3_5_8_5,
												    0x1_0_6_a_a_0_7_0,
												    0x1_9_a_4_c_1_1_6,
												    0x1_e_3_7_6_c_0_8,
												    0x2_7_4_8_7_7_4_c,
												    0x3_4_b_0_b_c_b_5,
												    0x3_9_1_c_0_c_b_3,
												    0x4_e_d_8_a_a_4_a,
												    0x5_b_9_c_c_a_4_f,
												    0x6_8_2_e_6_f_f_3,
												    0x7_4_8_f_8_2_e_e,
												    0x7_8_a_5_6_3_6_f,
												    0x8_4_c_8_7_8_1_4,
												    0x8_c_c_7_0_2_0_8,
												    0x9_0_b_e_f_f_f_a,
												    0xa_4_5_0_6_c_e_b,
												    0xb_e_f_9_a_3_f_7,
												    0xc_6_7_1_7_8_f_2,
												]

												_a      :       Optional[int]   =						self.preprocessing(self.data			)
												self.final_hash()


							@staticmethod
							def 							__lowercase							(     _a			)       ->       bytes:
												_a      :       Any   =						b'''\x80''' + (b'''\x00''' * (6_3 - (len(_a			) + 8) % 6_4))
												_a      :       int   =						struct.pack('''>Q'''			,				(len(_a			) * 8)			)
												return data + padding + big_endian_integer


							def 							__lowercase							(     self			)       ->       None:
												# Convert into blocks of 64 bytes
												_a      :       Union[str, Any]   =						[
												    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
																	_a      :       Optional[Any]   =						list(struct.unpack('''>16L'''			,				_a			)			)
																	# add 48 0-ed integers
																	words += [0] * 4_8

																	_a ,   _a ,   _a ,   _a ,   _a ,   _a ,   _a ,   _a      :       Any   =						self.hashes

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

																											_a      :       Any   =						(
																											    words[index - 1_6] + sa + words[index - 7] + sa
																											) % 0x1_0_0_0_0_0_0_0_0

																						# Compression
																						_a      :       Any   =						self.ror(_a			,				6			) ^ self.ror(_a			,				1_1			) ^ self.ror(_a			,				2_5			)
																						_a      :       Tuple   =						(e & f) ^ ((~e & 0xf_f_f_f_f_f_f_f) & g)
																						_a      :       str   =						(
																						    h + sa + ch + self.round_constants[index] + words[index]
																						) % 0x1_0_0_0_0_0_0_0_0
																						_a      :       str   =						self.ror(_a			,				2			) ^ self.ror(_a			,				1_3			) ^ self.ror(_a			,				2_2			)
																						_a      :       Optional[int]   =						(a & b) ^ (a & c) ^ (b & c)
																						_a      :       int   =						(sa + maj) % 0x1_0_0_0_0_0_0_0_0

																						_a ,   _a ,   _a ,   _a ,   _a ,   _a ,   _a ,   _a      :       Tuple   =						(
																						    g,
																						    f,
																						    e,
																						    ((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
																						    c,
																						    b,
																						    a,
																						    ((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
																						)

																	_a      :       Any   =						[a, b, c, d, e, f, g, h]

																	# Modify final values
																	_a      :       Optional[int]   =						[
																	    ((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
																	    for index, element in enumerate(self.hashes			)
																	]

												_a      :       Optional[Any]   =						''''''.join([hex(_a			)[2:].zfill(8			) for value in self.hashes]			)




							def 							__lowercase							(     self			,				_a			,				_a			)       ->       int:
												return 0xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)



class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       None:
												import hashlib

												_a      :       List[str]   =						bytes('''Test String'''			,				'''utf-8'''			)
												self.assertEqual(SHAaaa(_a			).hash			,				hashlib.shaaaa(_a			).hexdigest()			)



def   __UpperCAmelCase					(      )     ->	None:

					"""simple docstring"""

					import doctest

					doctest.testmod()

					_a      :       Union[str, Any]   =						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      :       Optional[Any]   =						parser.parse_args()

					_a      :       Dict   =						args.input_string

					# hash input should be a bytestring
					if args.input_file:
										with open(args.input_file						,'''rb'''      ) as f:
															_a      :       Dict   =						f.read()
					else:
										_a      :       Optional[Any]   =						bytes(__a						,'''utf-8'''      )

					print(SHAaaa(__a      ).hash      )


if __name__ == "__main__":
			main()






import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       KandinskyInpaintPipeline
							UpperCAmelCase__			:		Optional[int]             =       ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		Optional[Any]             =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		Optional[int]             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Any             =       False


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						MCLIPConfig(
												    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=3_7			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1_0_0_5			,				)

												_a      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)

												_a      :       List[str]   =						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''text_image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''text_image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a      :       Dict   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[Any]   =						self.dummy_text_encoder
												_a      :       Optional[Any]   =						self.dummy_tokenizer
												_a      :       Optional[Any]   =						self.dummy_unet
												_a      :       Union[str, Any]   =						self.dummy_movq

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

												_a      :       List[Any]   =						self.get_dummy_components()

												_a      :       Tuple   =						self.pipeline_class(**_a			)
												_a      :       int   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

												_a      :       Tuple   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

												print(F"""image.shape {image.shape}"""			)

												assert image.shape == (1, 6_4, 6_4, 3)

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												assert (
												    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




							def 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

												_a      :       Optional[Any]   =						KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)



