






import argparse
import hashlib  # hashlib is only used inside the Test class
import struct



class 							SCREAMING_SNAKE_CASE__		:





     """simple docstring"""






     def __init__(      self  :						Any							,	__A  :						List[str] ):
      snake_case__				:   List[Any]     =						data
      snake_case__				:   Tuple     =						[0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0]



     @staticmethod
     def  _lowercase  (      __A  :						str							,	__A  :						Union[str, Any] ):
      return ((n << b) | (n >> (3_2 - b))) & 0xFFFFFFFF



     def  _lowercase  (      self  :						Tuple ):
      snake_case__				:   List[Any]     =						B"\x80" + B"\x00" * (6_3 - (len(self.data ) + 8) % 6_4)
      snake_case__				:   Union[str, Any]     =						self.data + padding + struct.pack(">Q"							,	8 * len(self.data ) )
      return padded_data



     def  _lowercase  (      self  :						Union[str, Any] ):
      return [
          self.padded_data[i : i + 6_4] for i in range(0							,	len(self.padded_data )							,	6_4 )
      ]



     def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						str ):
      snake_case__				:   Dict     =						list(struct.unpack(">16L"							,	__a ) ) + [0] * 6_4
      for i in range(1_6							,	8_0 ):
       snake_case__				:   Dict     =						self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 1_4] ^ w[i - 1_6])							,	1 )
      return w






     def  _lowercase  (      self  :						Any ):
      snake_case__				:   Tuple     =						self.padding()
      snake_case__				:   List[Any]     =						self.split_blocks()
      for block in self.blocks:
       snake_case__				:   Optional[Any]     =						self.expand_block(__a )
       snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__				:   Tuple     =						self.h
       for i in range(0							,	8_0 ):
        if 0 <= i < 2_0:
         snake_case__				:   Any     =						(b & c) | ((~b) & d)
         snake_case__				:   Optional[Any]     =						0x5A827999
        elif 2_0 <= i < 4_0:
         snake_case__				:   Optional[int]     =						b ^ c ^ d
         snake_case__				:   Dict     =						0x6ED9EBA1
        elif 4_0 <= i < 6_0:
         snake_case__				:   List[str]     =						(b & c) | (b & d) | (c & d)
         snake_case__				:   Tuple     =						0x8F1BBCDC
        elif 6_0 <= i < 8_0:
         snake_case__				:   Union[str, Any]     =						b ^ c ^ d
         snake_case__				:   Tuple     =						0xCA62C1D6
        snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__				:   Dict     =						(
            self.rotate(__a							,	5 ) + f + e + k + expanded_block[i] & 0xFFFFFFFF,
            a,
            self.rotate(__a							,	3_0 ),
            c,
            d,
        )
       snake_case__				:   List[Any]     =						(
           self.h[0] + a & 0xFFFFFFFF,
           self.h[1] + b & 0xFFFFFFFF,
           self.h[2] + c & 0xFFFFFFFF,
           self.h[3] + d & 0xFFFFFFFF,
           self.h[4] + e & 0xFFFFFFFF,
       )
      return ("{:08x}" * 5).format(*self.h )







def   SCREAMING_SNAKE_CASE       (				):
 snake_case__				:   Union[str, Any]     =						b"Test String"
 assert SHAaHash(__snake_case     ).final_hash() == hashlib.shaa(__snake_case     ).hexdigest()  # noqa: S324







def   SCREAMING_SNAKE_CASE       (				):
 snake_case__				:   List[str]     =						argparse.ArgumentParser(description="Process some strings or files"     )
 parser.add_argument(
     "--string"  ,						dest="input_string"  ,						default="Hello World!! Welcome to Cryptography"  ,						help="Hash the string"  ,						)
 parser.add_argument("--file"  ,						dest="input_file"  ,						help="Hash contents of a file"     )
 snake_case__				:   List[Any]     =						parser.parse_args()
 snake_case__				:   Dict     =						args.input_string
 # In any case 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__				:   Any     =						bytes(__snake_case  ,						"utf-8"     )
 print(SHAaHash(__snake_case     ).final_hash()     )


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

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



