

from collections.abc import Generator
from math import sin
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if len(_A       ) != 32:
          raise ValueError('''Input must be of length 32'''       )

     SCREAMING_SNAKE_CASE__					=					B''''''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )

     SCREAMING_SNAKE_CASE__					=					format(_A   ,	'''08x'''       )[-8:]
     SCREAMING_SNAKE_CASE__					=					B''''''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''       )
     return little_endian_hex
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					B''''''
     for char in message:
          bit_string += format(_A   ,	'''08b'''       ).encode('''utf-8'''       )
     SCREAMING_SNAKE_CASE__					=					format(len(_A       )   ,	'''064b'''       ).encode('''utf-8'''       )

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_A       ) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]       ) + to_little_endian(start_len[:32]       )

     return bit_string
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if len(_A       ) % 5_12 != 0:
          raise ValueError('''Input must have length that\'s a multiple of 512'''       )

     for pos in range(0   ,	len(_A       )   ,	5_12       ):
          SCREAMING_SNAKE_CASE__					=					bit_string[pos : pos + 5_12]
          SCREAMING_SNAKE_CASE__					=					[]
          for i in range(0   ,	5_12   ,	32       ):
               block_words.append(int(to_little_endian(block[i : i + 32]       )   ,	2       )       )
          yield block_words
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )

     SCREAMING_SNAKE_CASE__					=					format(_A   ,	'''032b'''       )
     SCREAMING_SNAKE_CASE__					=					''''''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_A   ,	2       )
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     return (a + b) % 2**32
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if i < 0:
          raise ValueError('''Input must be non-negative'''       )
     if shift < 0:
          raise ValueError('''Shift must be non-negative'''       )
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					preprocess(_A       )

     SCREAMING_SNAKE_CASE__					=					[int(2**32 * abs(sin(i + 1       )       )       ) for i in range(64       )]

     # Starting states
     SCREAMING_SNAKE_CASE__					=					0x6_7_4_5_2_3_0_1
     SCREAMING_SNAKE_CASE__					=					0xe_f_c_d_a_b_8_9
     SCREAMING_SNAKE_CASE__					=					0x9_8_b_a_d_c_f_e
     SCREAMING_SNAKE_CASE__					=					0x1_0_3_2_5_4_7_6

     SCREAMING_SNAKE_CASE__					=					[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_A       ):
          SCREAMING_SNAKE_CASE__					=					aa
          SCREAMING_SNAKE_CASE__					=					ba
          SCREAMING_SNAKE_CASE__					=					ca
          SCREAMING_SNAKE_CASE__					=					da

          # Hash current chunk
          for i in range(64       ):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    SCREAMING_SNAKE_CASE__					=					d ^ (b & (c ^ d))
                    SCREAMING_SNAKE_CASE__					=					i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    SCREAMING_SNAKE_CASE__					=					c ^ (d & (b ^ c))
                    SCREAMING_SNAKE_CASE__					=					(5 * i + 1) % 16
               elif i <= 47:
                    SCREAMING_SNAKE_CASE__					=					b ^ c ^ d
                    SCREAMING_SNAKE_CASE__					=					(3 * i + 5) % 16
               else:
                    SCREAMING_SNAKE_CASE__					=					c ^ (b | not_aa(_A       ))
                    SCREAMING_SNAKE_CASE__					=					(7 * i) % 16
               SCREAMING_SNAKE_CASE__					=					(f + a + added_consts[i] + block_words[g]) % 2**32
               SCREAMING_SNAKE_CASE__					=					d
               SCREAMING_SNAKE_CASE__					=					c
               SCREAMING_SNAKE_CASE__					=					b
               SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	left_rotate_aa(_A   ,	shift_amounts[i]       )       )

          # Add hashed chunk to running total
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					sum_aa(_A   ,	_A       )

     SCREAMING_SNAKE_CASE__					=					reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       )
     return digest


if __name__ == "__main__":
       import doctest

       doctest.testmod()





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    ['''RobertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [
           '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RobertaForCausalLM''',
           '''RobertaForMaskedLM''',
           '''RobertaForMultipleChoice''',
           '''RobertaForQuestionAnswering''',
           '''RobertaForSequenceClassification''',
           '''RobertaForTokenClassification''',
           '''RobertaModel''',
           '''RobertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRobertaForCausalLM''',
           '''TFRobertaForMaskedLM''',
           '''TFRobertaForMultipleChoice''',
           '''TFRobertaForQuestionAnswering''',
           '''TFRobertaForSequenceClassification''',
           '''TFRobertaForTokenClassification''',
           '''TFRobertaMainLayer''',
           '''TFRobertaModel''',
           '''TFRobertaPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Optional[Any]         =    [
           '''FlaxRobertaForCausalLM''',
           '''FlaxRobertaForMaskedLM''',
           '''FlaxRobertaForMultipleChoice''',
           '''FlaxRobertaForQuestionAnswering''',
           '''FlaxRobertaForSequenceClassification''',
           '''FlaxRobertaForTokenClassification''',
           '''FlaxRobertaModel''',
           '''FlaxRobertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						str         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


