






import argparse
import struct
import unittest



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






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

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

						# Initialize round constants
						snake_case__				:   List[Any]     =						[
						    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__				:   List[str]     =						self.preprocessing(self.data )
						self.final_hash()



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



					def  _lowercase  (      self  :						Tuple ):
						# Convert into blocks of 64 bytes
						snake_case__				:   List[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
							snake_case__				:   Dict     =						list(struct.unpack(">16L"							,	__UpperCamelCase ) )
							# 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__				:   str     =						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__				:   Tuple     =						(
									    self.ror(words[index - 1_5]							,	7 )
									    ^ self.ror(words[index - 1_5]							,	1_8 )
									    ^ (words[index - 1_5] >> 3)
									)
									snake_case__				:   Union[str, Any]     =						(
									    self.ror(words[index - 2]							,	1_7 )
									    ^ self.ror(words[index - 2]							,	1_9 )
									    ^ (words[index - 2] >> 1_0)
									)

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

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

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

						snake_case__				:   str     =						"".join([hex(__UpperCamelCase )[2:].zfill(8 ) for value in self.hashes] )






					def  _lowercase  (      self  :						str							,	__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(__UpperCamelCase ).hash							,	hashlib.shaaaa(__UpperCamelCase ).hexdigest() )







def   SCREAMING_SNAKE_CASE       (				):
	import doctest

	doctest.testmod()

	snake_case__				:   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"     )

	snake_case__				:   Optional[int]     =						parser.parse_args()

	snake_case__				:   str     =						args.input_string

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

	print(SHAaaa(_SCREAMING_SNAKE_CASE     ).hash     )


if __name__ == "__main__":
	main()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



