





import argparse
import struct
import unittest


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[int]							,	__lowerCAmelCase   :    bytes   )	->	None:







					"""simple docstring"""






					A__        =   data

					# Initialize hash values
					A__        =   [
					    0X6A09E667,
					    0XBB67AE85,
					    0X3C6EF372,
					    0XA54FF53A,
					    0X510E527F,
					    0X9B05688C,
					    0X1F83D9AB,
					    0X5BE0CD19,
					]

					# Initialize round constants
					A__        =   [
					    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,
					]

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






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    bytes   )	->	bytes:







					"""simple docstring"""






					A__        =   B"""\x80""" + (B"""\x00""" * (63 - (len(__lowerCAmelCase   ) + 8) % 64))
					A__        =   struct.pack(""">Q"""							,	(len(__lowerCAmelCase   ) * 8)   )
					return data + padding + big_endian_integer






			def    a_       (			self   :    Optional[int]   )	->	None:







					"""simple docstring"""






					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"""							,	__lowerCAmelCase   )   )
							# 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
											) % 0X100000000

									# Compression
									A__        =   self.ror(__lowerCAmelCase							,	6   ) ^ self.ror(__lowerCAmelCase							,	11   ) ^ self.ror(__lowerCAmelCase							,	25   )
									A__        =   (e & f) ^ ((~e & 0XFFFFFFFF) & g)
									A__        =   (
									    h + sa + ch + self.round_constants[index] + words[index]
									) % 0X100000000
									A__        =   self.ror(__lowerCAmelCase							,	2   ) ^ self.ror(__lowerCAmelCase							,	13   ) ^ self.ror(__lowerCAmelCase							,	22   )
									A__        =   (a & b) ^ (a & c) ^ (b & c)
									A__        =   (sa + maj) % 0X100000000

									A__      ,			A__      ,			A__      ,			A__      ,			A__      ,			A__      ,			A__      ,			A__        =   (
									    g,
									    f,
									    e,
									    ((d + tempa) % 0X100000000),
									    c,
									    b,
									    a,
									    ((tempa + tempa) % 0X100000000),
									)

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

							# Modify final values
							A__        =   [
							    ((element + mutated_hash_values[index]) % 0X100000000)
							    for index, element in enumerate(self.hashes   )
							]

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







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	int:







					"""simple docstring"""






					return 0XFFFFFFFF & (value << (32 - rotations)) | (value >> rotations)


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    List[Any]   )	->	None:







					"""simple docstring"""






					import hashlib

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







def    __lowerCamelCase					(							)		->				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()










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0   )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



