"""simple docstring"""


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


class 				a :


    def __init__(    self		:     Union[str, Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					data
      _UpperCAmelCase         =					[0x67_45_23_01, 0xef_cd_ab_89, 0x98_ba_dc_fe, 0x10_32_54_76, 0xc3_d2_e1_f0]


    @staticmethod
    def    lowerCAmelCase_     (    __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Optional[Any]    ):
      return ((n << b) | (n >> (32 - b))) & 0xff_ff_ff_ff


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					b"""\x80""" + b"""\x00""" * (63 - (len(self.data    ) + 8) % 64)
      _UpperCAmelCase         =					self.data + padding + struct.pack(""">Q"""      , 8 * len(self.data    )    )
      return padded_data


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return [
          self.padded_data[i : i + 64] for i in range(0      , len(self.padded_data    )      , 64    )
      ]


    def    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     Dict    ):
      _UpperCAmelCase         =					list(struct.unpack(""">16L"""      , __lowerCAmelCase    )    ) + [0] * 64
      for i in range(16      , 80    ):
        _UpperCAmelCase         =					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])      , 1    )
      return w


    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					self.padding()
      _UpperCAmelCase         =					self.split_blocks()
      for block in self.blocks:
        _UpperCAmelCase         =					self.expand_block(__lowerCAmelCase    )
        _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					self.h
        for i in range(0      , 80    ):
          if 0 <= i < 20:
            _UpperCAmelCase         =					(b & c) | ((~b) & d)
            _UpperCAmelCase         =					0x5a_82_79_99
          elif 20 <= i < 40:
            _UpperCAmelCase         =					b ^ c ^ d
            _UpperCAmelCase         =					0x6e_d9_eb_a1
          elif 40 <= i < 60:
            _UpperCAmelCase         =					(b & c) | (b & d) | (c & d)
            _UpperCAmelCase         =					0x8f_1b_bc_dc
          elif 60 <= i < 80:
            _UpperCAmelCase         =					b ^ c ^ d
            _UpperCAmelCase         =					0xca_62_c1_d6
          _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					(
              self.rotate(__lowerCAmelCase      , 5    ) + f + e + k + expanded_block[i] & 0xff_ff_ff_ff,
              a,
              self.rotate(__lowerCAmelCase      , 30    ),
              c,
              d,
          )
        _UpperCAmelCase         =					(
            self.h[0] + a & 0xff_ff_ff_ff,
            self.h[1] + b & 0xff_ff_ff_ff,
            self.h[2] + c & 0xff_ff_ff_ff,
            self.h[3] + d & 0xff_ff_ff_ff,
            self.h[4] + e & 0xff_ff_ff_ff,
        )
      return ("{:08x}" * 5).format(*self.h    )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					B"""Test String"""
  assert SHAaHash(lowercase						).final_hash() == hashlib.shaa(lowercase						).hexdigest()  # noqa: S324






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					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"""						)
  _UpperCAmelCase         =					parser.parse_args()
  _UpperCAmelCase         =					args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file      ,"""rb"""						) as f:
      _UpperCAmelCase         =					f.read()
  else:
    _UpperCAmelCase         =					bytes(lowercase      ,"""utf-8"""						)
  print(SHAaHash(lowercase						).final_hash()						)


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

    doctest.testmod()

"""simple docstring"""


import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetModel,
    )


class 				a :


    def __init__(    self		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     List[str]=13      , __lowerCAmelCase		:     List[Any]=7      , __lowerCAmelCase		:     Any=True      , __lowerCAmelCase		:     List[Any]=True      , __lowerCAmelCase		:     Tuple=False      , __lowerCAmelCase		:     List[str]=True      , __lowerCAmelCase		:     Optional[Any]=99      , __lowerCAmelCase		:     int=64      , __lowerCAmelCase		:     Optional[int]=5      , __lowerCAmelCase		:     Union[str, Any]=4      , __lowerCAmelCase		:     Union[str, Any]=64      , __lowerCAmelCase		:     Optional[Any]="gelu"      , __lowerCAmelCase		:     int=0.1      , __lowerCAmelCase		:     Optional[int]=0.1      , __lowerCAmelCase		:     str=512      , __lowerCAmelCase		:     Any=16      , __lowerCAmelCase		:     Tuple=2      , __lowerCAmelCase		:     Tuple=0.02      , __lowerCAmelCase		:     List[str]=3      , __lowerCAmelCase		:     int=4      , __lowerCAmelCase		:     str=None      , ):
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					seq_length
      _UpperCAmelCase         =					is_training
      _UpperCAmelCase         =					use_input_mask
      _UpperCAmelCase         =					use_token_type_ids
      _UpperCAmelCase         =					use_labels
      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					type_vocab_size
      _UpperCAmelCase         =					type_sequence_label_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					num_labels
      _UpperCAmelCase         =					num_choices
      _UpperCAmelCase         =					scope


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size    )

      _UpperCAmelCase         =					None
      if self.use_input_mask:
        _UpperCAmelCase         =					random_attention_mask([self.batch_size, self.seq_length]    )

      _UpperCAmelCase         =					None
      _UpperCAmelCase         =					None
      _UpperCAmelCase         =					None
      if self.use_labels:
        _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.type_sequence_label_size    )
        _UpperCAmelCase         =					ids_tensor([self.batch_size, self.seq_length]      , self.num_labels    )
        _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.num_choices    )

      _UpperCAmelCase         =					self.get_config()
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      return MPNetConfig(
          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      , initializer_range=self.initializer_range      , )


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					MPNetModel(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )
      _UpperCAmelCase         =					model(__lowerCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)    )
      self.parent.assertEqual(result.pooler_output.shape      , (self.batch_size, self.hidden_size)    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					MPNetForQuestionAnswering(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(
          __lowerCAmelCase      , attention_mask=__lowerCAmelCase      , start_positions=__lowerCAmelCase      , end_positions=__lowerCAmelCase      , )
      self.parent.assertEqual(result.start_logits.shape      , (self.batch_size, self.seq_length)    )
      self.parent.assertEqual(result.end_logits.shape      , (self.batch_size, self.seq_length)    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					MPNetForSequenceClassification(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.num_choices
      _UpperCAmelCase         =					MPNetForMultipleChoice(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					input_ids.unsqueeze(1    ).expand(-1      , self.num_choices      , -1    ).contiguous()
      _UpperCAmelCase         =					input_mask.unsqueeze(1    ).expand(-1      , self.num_choices      , -1    ).contiguous()
      _UpperCAmelCase         =					model(
          __lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase      , )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_choices)    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Any    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					MPNetForTokenClassification(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.prepare_config_and_inputs()
      ((_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase))         =					config_and_inputs
      _UpperCAmelCase         =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     List[Any]   							=							(
        (
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetModel,
        )
        if is_torch_available()
        else ()
    )
    _snake_case   :     Union[str, Any]   							=							(
        {
            'feature-extraction': MPNetModel,
            'fill-mask': MPNetForMaskedLM,
            'question-answering': MPNetForQuestionAnswering,
            'text-classification': MPNetForSequenceClassification,
            'token-classification': MPNetForTokenClassification,
            'zero-shot': MPNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case   :     int   							=							False
    _snake_case   :     List[Any]   							=							True


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					MPNetModelTester(self    )
      _UpperCAmelCase         =					ConfigTester(self      , config_class=__lowerCAmelCase      , hidden_size=37    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      self.config_tester.run_common_tests()


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCAmelCase    )



@require_torch
class 				a (						unittest.TestCase      ):


    @slow
    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					MPNetModel.from_pretrained("""microsoft/mpnet-base"""    )
      _UpperCAmelCase         =					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
      _UpperCAmelCase         =					model(__lowerCAmelCase    )[0]
      _UpperCAmelCase         =					torch.Size((1, 11, 768)    )
      self.assertEqual(output.shape      , __lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(
          [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]    )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3]      , __lowerCAmelCase      , atol=1e-4    )    )
