



'''simple docstring'''






from collections.abc import Generator
from math import sin
def    lowerCamelCase__ (		_A       ):
  if len(_A       ) != 32:
    raise ValueError('Input must be of length 32'       )

  a		:      str       =  B''
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian
def    lowerCamelCase__ (		_A       ):
  if i < 0:
    raise ValueError('Input must be non-negative'       )

  a		:      Any       =  format(_A				,    '08x'       )[-8:]
  a		:      int       =  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    lowerCamelCase__ (		_A       ):
  a		:      List[Any]       =  B''
  for char in message:
    bit_string += format(_A				,    '08b'       ).encode('utf-8'       )
  a		:      str       =  format(len(_A       )				,    '064b'       ).encode('utf-8'       )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(_A       ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]       ) + to_little_endian(start_len[:32]       )

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

  for pos in range(0				,    len(_A       )				,    512       ):
    a		:      Optional[int]       =  bit_string[pos : pos + 512]
    a		:      Optional[int]       =  []
    for i in range(0				,    512				,    32       ):
      block_words.append(int(to_little_endian(block[i : i + 32]       )				,    2       )       )
    yield block_words
def    lowerCamelCase__ (		_A       ):
  if i < 0:
    raise ValueError('Input must be non-negative'       )

  a		:      int       =  format(_A				,    '032b'       )
  a		:      Optional[Any]       =  ''
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(_A				,    2       )
def    lowerCamelCase__ (		_A				,    _A       ):
  return (a + b) % 2**32
def    lowerCamelCase__ (		_A				,    _A       ):
  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    lowerCamelCase__ (		_A       ):
  a		:      Tuple       =  preprocess(_A       )

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

  # Starting states
  a		:      Optional[Any]       =  0X67_452_301
  a		:      Tuple       =  0XEF_CDA_B89
  a		:      int       =  0X98_BAD_CFE
  a		:      List[str]       =  0X10_325_476

  a		:      List[Any]       =  [
      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       ):
    a		:      str       =  aa
    a		:      List[str]       =  ba
    a		:      Union[str, Any]       =  ca
    a		:      Any       =  da

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

    # Add hashed chunk to running total
    a		:      Optional[int]       =  sum_aa(_A				,    _A       )
    a		:      List[str]       =  sum_aa(_A				,    _A       )
    a		:      List[str]       =  sum_aa(_A				,    _A       )
    a		:      List[Any]       =  sum_aa(_A				,    _A       )

  a		:      Any       =  reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       ) + reformat_hex(_A       )
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''simple docstring'''






import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.bert.modeling_flax_bert import (
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForNextSentencePrediction,
         FlaxBertForPreTraining,
         FlaxBertForQuestionAnswering,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertModel,
     )

class 							a__(      unittest.TestCase			):







    def __init__(   self				:						Dict		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Any=13		,    __snake_case				:						Any=7		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						Optional[Any]=99		,    __snake_case				:						List[str]=32		,    __snake_case				:						List[Any]=5		,    __snake_case				:						int=4		,    __snake_case				:						List[str]=37		,    __snake_case				:						int="gelu"		,    __snake_case				:						Tuple=0.1		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						Any=5_12		,    __snake_case				:						Tuple=16		,    __snake_case				:						Union[str, Any]=2		,    __snake_case				:						str=0.02		,    __snake_case				:						Union[str, Any]=4		,    ):
      a		:      List[Any]       =  parent
      a		:      List[str]       =  batch_size
      a		:      Dict       =  seq_length
      a		:      str       =  is_training
      a		:      Optional[int]       =  use_attention_mask
      a		:      Union[str, Any]       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Dict       =  vocab_size
      a		:      Tuple       =  hidden_size
      a		:      Optional[Any]       =  num_hidden_layers
      a		:      List[str]       =  num_attention_heads
      a		:      Tuple       =  intermediate_size
      a		:      Dict       =  hidden_act
      a		:      List[str]       =  hidden_dropout_prob
      a		:      str       =  attention_probs_dropout_prob
      a		:      str       =  max_position_embeddings
      a		:      Tuple       =  type_vocab_size
      a		:      Optional[Any]       =  type_sequence_label_size
      a		:      Union[str, Any]       =  initializer_range
      a		:      Union[str, Any]       =  num_choices







    def     lowercase_ (   self				:						Any			):
      a		:      List[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[str]       =  None
      if self.use_attention_mask:
        a		:      List[Any]       =  random_attention_mask([self.batch_size, self.seq_length]			)

      a		:      Dict       =  None
      if self.use_token_type_ids:
        a		:      List[str]       =  ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size			)

      a		:      Dict       =  BertConfig(
          vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    is_decoder=__snake_case		,    initializer_range=self.initializer_range		,    )

      return config, input_ids, token_type_ids, attention_mask







    def     lowercase_ (   self				:						Tuple			):
      a		:      Optional[int]       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Union[str, Any]       =  config_and_inputs
      a		:      str       =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict







    def     lowercase_ (   self				:						List[str]			):
      a		:      int       =  self.prepare_config_and_inputs()
      a     ,			a     ,			a     ,			a		:      Tuple       =  config_and_inputs

      a		:      int       =  True
      a		:      int       =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      a		:      Optional[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2			)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )

@require_flax
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					True

    lowercase__				=					(
        (
            FlaxBertModel,
            FlaxBertForPreTraining,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForQuestionAnswering,
            FlaxBertForNextSentencePrediction,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      str       =  FlaxBertModelTester(self			)







    @slow
    def     lowercase_ (   self				:						Any			):
      # Only check this for base model, not necessary for all model classes.
      # This will also help speed-up tests.
      a		:      Optional[Any]       =  FlaxBertModel.from_pretrained('bert-base-cased'			)
      a		:      Union[str, Any]       =  model(np.ones((1, 1)			)			)
      self.assertIsNotNone(__snake_case			)