






'''simple docstring'''







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__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					KandinskyInpaintPipeline
		_UpperCamelCase				  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		_UpperCamelCase				  =					[
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		_UpperCamelCase				  =					[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		_UpperCamelCase				  =					False






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return 32






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return 32






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return self.time_input_dim






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return self.time_input_dim * 4






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return 1_00






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								lowerCamelCase__         =  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""       )
								return tokenizer






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								torch.manual_seed(0       )
								lowerCamelCase__         =  MCLIPConfig(
								    numDims=self.cross_attention_dim   ,transformerDimensions=self.text_embedder_hidden_size   ,hidden_size=self.text_embedder_hidden_size   ,intermediate_size=37   ,num_attention_heads=4   ,num_hidden_layers=5   ,vocab_size=10_05   ,)

								lowerCamelCase__         =  MultilingualCLIP(_lowerCAmelCase       )
								lowerCamelCase__         =  text_encoder.eval()

								return text_encoder






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								torch.manual_seed(0       )

								lowerCamelCase__         =  {
								    """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,
								}

								lowerCamelCase__         =  UNetaDConditionModel(**_lowerCAmelCase       )
								return model






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								return {
								    "block_out_channels": [32, 64],
								    "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": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}






		@property
		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								torch.manual_seed(0       )
								lowerCamelCase__         =  VQModel(**self.dummy_movq_kwargs       )
								return model






		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								lowerCamelCase__         =  self.dummy_text_encoder
								lowerCamelCase__         =  self.dummy_tokenizer
								lowerCamelCase__         =  self.dummy_unet
								lowerCamelCase__         =  self.dummy_movq

								lowerCamelCase__         =  DDIMScheduler(
								    num_train_timesteps=10_00   ,beta_schedule="""linear"""   ,beta_start=0.0_0085   ,beta_end=0.012   ,clip_sample=_lowerCAmelCase   ,set_alpha_to_one=_lowerCAmelCase   ,steps_offset=1   ,prediction_type="""epsilon"""   ,thresholding=_lowerCAmelCase   ,)

								lowerCamelCase__         =  {
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=0       ):

								'''simple docstring'''





								lowerCamelCase__         =  floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(_lowerCAmelCase       )       ).to(_lowerCAmelCase       )
								lowerCamelCase__         =  floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(seed + 1       )       ).to(_lowerCAmelCase       )
								# create init_image
								lowerCamelCase__         =  floats_tensor((1, 3, 64, 64)   ,rng=random.Random(_lowerCAmelCase       )       ).to(_lowerCAmelCase       )
								lowerCamelCase__         =  image.cpu().permute(0   ,2   ,3   ,1       )[0]
								lowerCamelCase__         =  Image.fromarray(np.uinta(_lowerCAmelCase       )       ).convert("""RGB"""       ).resize((2_56, 2_56)       )
								# create mask
								lowerCamelCase__         =  np.ones((64, 64)   ,dtype=np.floataa       )
								lowerCamelCase__         =  0

								if str(_lowerCAmelCase       ).startswith("""mps"""       ):
														lowerCamelCase__         =  torch.manual_seed(_lowerCAmelCase       )
								else:
														lowerCamelCase__         =  torch.Generator(device=_lowerCAmelCase       ).manual_seed(_lowerCAmelCase       )
								lowerCamelCase__         =  {
								    """prompt""": """horse""",
								    """image""": init_image,
								    """mask_image""": mask,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 4.0,
								    """output_type""": """np""",
								}
								return inputs






		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								lowerCamelCase__         =  """cpu"""

								lowerCamelCase__         =  self.get_dummy_components()

								lowerCamelCase__         =  self.pipeline_class(**_lowerCAmelCase       )
								lowerCamelCase__         =  pipe.to(_lowerCAmelCase       )

								pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

								lowerCamelCase__         =  pipe(**self.get_dummy_inputs(_lowerCAmelCase       )       )
								lowerCamelCase__         =  output.images

								lowerCamelCase__         =  pipe(
								    **self.get_dummy_inputs(_lowerCAmelCase       )   ,return_dict=_lowerCAmelCase   ,)[0]

								lowerCamelCase__         =  image[0, -3:, -3:, -1]
								lowerCamelCase__         =  image_from_tuple[0, -3:, -3:, -1]

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

								assert image.shape == (1, 64, 64, 3)

								lowerCamelCase__         =  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 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								super().test_inference_batch_single_identical(expected_max_diff=3E-3       )




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




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






		def 			UpperCamelCase_					(      self       ):

								'''simple docstring'''





								lowerCamelCase__         =  load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""       )

								lowerCamelCase__         =  load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
								lowerCamelCase__         =  np.ones((7_68, 7_68)   ,dtype=np.floataa       )
								lowerCamelCase__         =  0

								lowerCamelCase__         =  """a hat"""

								lowerCamelCase__         =  KandinskyPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1-prior"""   ,torch_dtype=torch.floataa       )
								pipe_prior.to(_lowerCAmelCase       )

								lowerCamelCase__         =  KandinskyInpaintPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1-inpaint"""   ,torch_dtype=torch.floataa       )
								lowerCamelCase__         =  pipeline.to(_lowerCAmelCase       )
								pipeline.set_progress_bar_config(disable=_lowerCAmelCase       )

								lowerCamelCase__         =  torch.Generator(device="""cpu"""       ).manual_seed(0       )
								lowerCamelCase__       ,			lowerCamelCase__         =  pipe_prior(
								    _lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=5   ,negative_prompt=""""""   ,).to_tuple()

								lowerCamelCase__         =  pipeline(
								    _lowerCAmelCase   ,image=_lowerCAmelCase   ,mask_image=_lowerCAmelCase   ,image_embeds=_lowerCAmelCase   ,negative_image_embeds=_lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=1_00   ,height=7_68   ,width=7_68   ,output_type="""np"""   ,)

								lowerCamelCase__         =  output.images[0]

								assert image.shape == (7_68, 7_68, 3)

								assert_mean_pixel_difference(_lowerCAmelCase   ,_lowerCAmelCase       )









'''simple docstring'''







import argparse
import struct
import unittest




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  data

								# Initialize hash values
								lowerCamelCase__         =  [
								    0x6a_09_e6_67,
								    0xbb_67_ae_85,
								    0x3c_6e_f3_72,
								    0xa5_4f_f5_3a,
								    0x51_0e_52_7f,
								    0x9b_05_68_8c,
								    0x1f_83_d9_ab,
								    0x5b_e0_cd_19,
								]

								# Initialize round constants
								lowerCamelCase__         =  [
								    0x42_8a_2f_98,
								    0x71_37_44_91,
								    0xb5_c0_fb_cf,
								    0xe9_b5_db_a5,
								    0x39_56_c2_5b,
								    0x59_f1_11_f1,
								    0x92_3f_82_a4,
								    0xab_1c_5e_d5,
								    0xd8_07_aa_98,
								    0x12_83_5b_01,
								    0x24_31_85_be,
								    0x55_0c_7d_c3,
								    0x72_be_5d_74,
								    0x80_de_b1_fe,
								    0x9b_dc_06_a7,
								    0xc1_9b_f1_74,
								    0xe4_9b_69_c1,
								    0xef_be_47_86,
								    0x0f_c1_9d_c6,
								    0x24_0c_a1_cc,
								    0x2d_e9_2c_6f,
								    0x4a_74_84_aa,
								    0x5c_b0_a9_dc,
								    0x76_f9_88_da,
								    0x98_3e_51_52,
								    0xa8_31_c6_6d,
								    0xb0_03_27_c8,
								    0xbf_59_7f_c7,
								    0xc6_e0_0b_f3,
								    0xd5_a7_91_47,
								    0x06_ca_63_51,
								    0x14_29_29_67,
								    0x27_b7_0a_85,
								    0x2e_1b_21_38,
								    0x4d_2c_6d_fc,
								    0x53_38_0d_13,
								    0x65_0a_73_54,
								    0x76_6a_0a_bb,
								    0x81_c2_c9_2e,
								    0x92_72_2c_85,
								    0xa2_bf_e8_a1,
								    0xa8_1a_66_4b,
								    0xc2_4b_8b_70,
								    0xc7_6c_51_a3,
								    0xd1_92_e8_19,
								    0xd6_99_06_24,
								    0xf4_0e_35_85,
								    0x10_6a_a0_70,
								    0x19_a4_c1_16,
								    0x1e_37_6c_08,
								    0x27_48_77_4c,
								    0x34_b0_bc_b5,
								    0x39_1c_0c_b3,
								    0x4e_d8_aa_4a,
								    0x5b_9c_ca_4f,
								    0x68_2e_6f_f3,
								    0x74_8f_82_ee,
								    0x78_a5_63_6f,
								    0x84_c8_78_14,
								    0x8c_c7_02_08,
								    0x90_be_ff_fa,
								    0xa4_50_6c_eb,
								    0xbe_f9_a3_f7,
								    0xc6_71_78_f2,
								]

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






		@staticmethod
		def 			UpperCamelCase_					(      _lowerCAmelCase       ):
								lowerCamelCase__         =  B"""\x80""" + (B"""\x00""" * (63 - (len(_lowerCAmelCase       ) + 8) % 64))
								lowerCamelCase__         =  struct.pack(""">Q"""   ,(len(_lowerCAmelCase       ) * 8)       )
								return data + padding + big_endian_integer






		def 			UpperCamelCase_					(      self       ):
								# Convert into blocks of 64 bytes
								lowerCamelCase__         =  [
								    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
														lowerCamelCase__         =  list(struct.unpack(""">16L"""   ,_lowerCAmelCase       )       )
														# add 48 0-ed integers
														words += [0] * 48

														lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  self.hashes

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

																										lowerCamelCase__         =  (
																										    words[index - 16] + sa + words[index - 7] + sa
																										) % 0x1_00_00_00_00

																				# Compression
																				lowerCamelCase__         =  self.ror(_lowerCAmelCase   ,6       ) ^ self.ror(_lowerCAmelCase   ,11       ) ^ self.ror(_lowerCAmelCase   ,25       )
																				lowerCamelCase__         =  (e & f) ^ ((~e & 0xff_ff_ff_ff) & g)
																				lowerCamelCase__         =  (
																				    h + sa + ch + self.round_constants[index] + words[index]
																				) % 0x1_00_00_00_00
																				lowerCamelCase__         =  self.ror(_lowerCAmelCase   ,2       ) ^ self.ror(_lowerCAmelCase   ,13       ) ^ self.ror(_lowerCAmelCase   ,22       )
																				lowerCamelCase__         =  (a & b) ^ (a & c) ^ (b & c)
																				lowerCamelCase__         =  (sa + maj) % 0x1_00_00_00_00

																				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  (
																				    g,
																				    f,
																				    e,
																				    ((d + tempa) % 0x1_00_00_00_00),
																				    c,
																				    b,
																				    a,
																				    ((tempa + tempa) % 0x1_00_00_00_00),
																				)

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

														# Modify final values
														lowerCamelCase__         =  [
														    ((element + mutated_hash_values[index]) % 0x1_00_00_00_00)
														    for index, element in enumerate(self.hashes       )
														]

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								return 0xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								import hashlib

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



def 						A__			(				):
						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(__lowerCAmelCase   ,    """utf-8"""						)

						print(SHAaaa(__lowerCAmelCase						).hash						)


if __name__ == "__main__":
							main()

