






import argparse
import struct
import unittest



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Any							,	__A  :						bytes ):
						snake_case__				:   Optional[Any]     =						data

						# Initialize hash values
						snake_case__				:   Union[str, Any]     =						[
						    0x6A09E667,
						    0xBB67AE85,
						    0x3C6EF372,
						    0xA54FF53A,
						    0x510E527F,
						    0x9B05688C,
						    0x1F83D9AB,
						    0x5BE0CD19,
						]

						# Initialize round constants
						snake_case__				:   List[str]     =						[
						    0x428A2F98,
						    0x71374491,
						    0xB5C0FBCF,
						    0xE9B5DBA5,
						    0x3956C25B,
						    0x59F111F1,
						    0x923F82A4,
						    0xAB1C5ED5,
						    0xD807AA98,
						    0x12835B01,
						    0x243185BE,
						    0x550C7DC3,
						    0x72BE5D74,
						    0x80DEB1FE,
						    0x9BDC06A7,
						    0xC19BF174,
						    0xE49B69C1,
						    0xEFBE4786,
						    0x0FC19DC6,
						    0x240CA1CC,
						    0x2DE92C6F,
						    0x4A7484AA,
						    0x5CB0A9DC,
						    0x76F988DA,
						    0x983E5152,
						    0xA831C66D,
						    0xB00327C8,
						    0xBF597FC7,
						    0xC6E00BF3,
						    0xD5A79147,
						    0x06CA6351,
						    0x14292967,
						    0x27B70A85,
						    0x2E1B2138,
						    0x4D2C6DFC,
						    0x53380D13,
						    0x650A7354,
						    0x766A0ABB,
						    0x81C2C92E,
						    0x92722C85,
						    0xA2BFE8A1,
						    0xA81A664B,
						    0xC24B8B70,
						    0xC76C51A3,
						    0xD192E819,
						    0xD6990624,
						    0xF40E3585,
						    0x106AA070,
						    0x19A4C116,
						    0x1E376C08,
						    0x2748774C,
						    0x34B0BCB5,
						    0x391C0CB3,
						    0x4ED8AA4A,
						    0x5B9CCA4F,
						    0x682E6FF3,
						    0x748F82EE,
						    0x78A5636F,
						    0x84C87814,
						    0x8CC70208,
						    0x90BEFFFA,
						    0xA4506CEB,
						    0xBEF9A3F7,
						    0xC67178F2,
						]

						snake_case__				:   Tuple     =						self.preprocessing(self.data )
						self.final_hash()



					@staticmethod
					def  _lowercase  (      __A  :						bytes ):
						snake_case__				:   Optional[int]     =						B"\x80" + (B"\x00" * (6_3 - (len(__A ) + 8) % 6_4))
						snake_case__				:   List[str]     =						struct.pack(">Q"							,	(len(__A ) * 8) )
						return data + padding + big_endian_integer



					def  _lowercase  (      self  :						Optional[int] ):
						# Convert into blocks of 64 bytes
						snake_case__				:   str     =						[
						    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
							snake_case__				:   Any     =						list(struct.unpack(">16L"							,	__A ) )
							# add 48 0-ed integers
							words += [0] * 4_8

							snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__				:   Optional[int]     =						self.hashes

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

									snake_case__				:   List[Any]     =						(
									    words[index - 1_6] + sa + words[index - 7] + sa
									) % 0x100000000

								# Compression
								snake_case__				:   Optional[int]     =						self.ror(__A							,	6 ) ^ self.ror(__A							,	1_1 ) ^ self.ror(__A							,	2_5 )
								snake_case__				:   str     =						(e & f) ^ ((~e & 0xFFFFFFFF) & g)
								snake_case__				:   str     =						(
								    h + sa + ch + self.round_constants[index] + words[index]
								) % 0x100000000
								snake_case__				:   Any     =						self.ror(__A							,	2 ) ^ self.ror(__A							,	1_3 ) ^ self.ror(__A							,	2_2 )
								snake_case__				:   Optional[int]     =						(a & b) ^ (a & c) ^ (b & c)
								snake_case__				:   List[Any]     =						(sa + maj) % 0x100000000

								snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__				:   Optional[int]     =						(
								    g,
								    f,
								    e,
								    ((d + tempa) % 0x100000000),
								    c,
								    b,
								    a,
								    ((tempa + tempa) % 0x100000000),
								)

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

							# Modify final values
							snake_case__				:   List[str]     =						[
							    ((element + mutated_hash_values[index]) % 0x100000000)
							    for index, element in enumerate(self.hashes )
							]

						snake_case__				:   Optional[int]     =						"".join([hex(__A )[2:].zfill(8 ) for value in self.hashes] )






					def  _lowercase  (      self  :						List[Any]							,	__A  :						int							,	__A  :						int ):
						return 0xFFFFFFFF & (value << (3_2 - rotations)) | (value >> rotations)



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Any ):
						import hashlib

						snake_case__				:   int     =						bytes("Test String"							,	"utf-8" )
						self.assertEqual(SHAaaa(__A ).hash							,	hashlib.shaaaa(__A ).hexdigest() )







def   SCREAMING_SNAKE_CASE       (				):
	import doctest

	doctest.testmod()

	snake_case__				:   int     =						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"     )

	snake_case__				:   Dict     =						parser.parse_args()

	snake_case__				:   Optional[int]     =						args.input_string

	# hash input should be a bytestring
	if args.input_file:
		with open(args.input_file  ,						"rb"     ) as f:
			snake_case__				:   Tuple     =						f.read()
	else:
		snake_case__				:   List[str]     =						bytes(snake_case_  ,						"utf-8"     )

	print(SHAaaa(snake_case_     ).hash     )


if __name__ == "__main__":
	main()











import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DeformableDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						int							,	__A  :						List[str]							,	__A  :						Union[str, Any]=7							,	__A  :						Any=3							,	__A  :						Optional[Any]=3_0							,	__A  :						List[str]=4_0_0							,	__A  :						str=True							,	__A  :						Optional[Any]=None							,	__A  :						Optional[int]=True							,	__A  :						int=[0.5, 0.5, 0.5]							,	__A  :						Dict=[0.5, 0.5, 0.5]							,	__A  :						Optional[int]=True							,	__A  :						int=1 / 2_5_5							,	__A  :						List[str]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Optional[Any]     =						parent
						snake_case__				:   str     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   Optional[Any]     =						min_resolution
						snake_case__				:   List[str]     =						max_resolution
						snake_case__				:   Tuple     =						do_resize
						snake_case__				:   str     =						size
						snake_case__				:   str     =						do_normalize
						snake_case__				:   Optional[Any]     =						image_mean
						snake_case__				:   List[str]     =						image_std
						snake_case__				:   List[str]     =						do_rescale
						snake_case__				:   Tuple     =						rescale_factor
						snake_case__				:   Tuple     =						do_pad



					def  _lowercase  (      self  :						str ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[Any]							,	__A  :						List[Any]=False ):
						if not batched:
							snake_case__				:   List[Any]     =						image_inputs[0]
							if isinstance(__A							,	Image.Image ):
								snake_case__,		snake_case__				:   str     =						image.size
							else:
								snake_case__,		snake_case__				:   Dict     =						image.shape[1], image.shape[2]
							if w < h:
								snake_case__				:   Any     =						int(self.size["shortest_edge"] * h / w )
								snake_case__				:   Any     =						self.size["shortest_edge"]
							elif w > h:
								snake_case__				:   Optional[int]     =						self.size["shortest_edge"]
								snake_case__				:   Any     =						int(self.size["shortest_edge"] * w / h )
							else:
								snake_case__				:   Tuple     =						self.size["shortest_edge"]
								snake_case__				:   int     =						self.size["shortest_edge"]

						else:
							snake_case__				:   Any     =						[]
							for image in image_inputs:
								snake_case__,		snake_case__				:   Optional[Any]     =						self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							snake_case__				:   List[Any]     =						max(__A							,	key=lambda __A : item[0] )[0]
							snake_case__				:   int     =						max(__A							,	key=lambda __A : item[1] )[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	DeformableDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						str ):
						snake_case__				:   Optional[Any]     =						DeformableDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						List[Any] ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"do_rescale" ) )
						self.assertTrue(hasattr(__A							,	"do_pad" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Union[str, Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Tuple     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						str ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Optional[Any]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	Image.Image )

						# Test not batched input
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   List[str]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__,		snake_case__				:   List[Any]     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						image_processing(__A							,	return_tensors="pt" ).pixel_values
						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						int ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   str     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	np.ndarray )

						# Test not batched input
						snake_case__				:   Optional[Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Union[str, Any] ):
						# Initialize image_processing
						snake_case__				:   Optional[int]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   List[str]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	torch.Tensor )

						# Test not batched input
						snake_case__				:   str     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					@slow
					def  _lowercase  (      self  :						Optional[Any] ):
						# prepare image and target
						snake_case__				:   Tuple     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Tuple     =						json.loads(f.read() )

						snake_case__				:   Union[str, Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   str     =						DeformableDetrImageProcessor()
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Union[str, Any]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   str     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Union[str, Any]     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   Any     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   int     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   List[str]     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Tuple     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						Optional[int] ):
						# prepare image, target and masks_path
						snake_case__				:   Dict     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Any     =						json.loads(f.read() )

						snake_case__				:   Dict     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   int     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   List[str]     =						DeformableDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   List[Any]     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   List[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Optional[int]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Any     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   Any     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   List[str]     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Any     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   List[str]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   Union[str, Any]     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Union[str, Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



