import argparse
import struct
import unittest


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				List[Any]     ,   __magic_name__       :				bytes      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     data

        # Initialize hash values
        SCREAMING_SNAKE_CASE_      =     [
            0X6_A_0_9_E_6_6_7,
            0XB_B_6_7_A_E_8_5,
            0X3_C_6_E_F_3_7_2,
            0XA_5_4_F_F_5_3_A,
            0X5_1_0_E_5_2_7_F,
            0X9_B_0_5_6_8_8_C,
            0X1_F_8_3_D_9_A_B,
            0X5_B_E_0_C_D_1_9,
        ]

        # Initialize round constants
        SCREAMING_SNAKE_CASE_      =     [
            0X4_2_8_A_2_F_9_8,
            0X7_1_3_7_4_4_9_1,
            0XB_5_C_0_F_B_C_F,
            0XE_9_B_5_D_B_A_5,
            0X3_9_5_6_C_2_5_B,
            0X5_9_F_1_1_1_F_1,
            0X9_2_3_F_8_2_A_4,
            0XA_B_1_C_5_E_D_5,
            0XD_8_0_7_A_A_9_8,
            0X1_2_8_3_5_B_0_1,
            0X2_4_3_1_8_5_B_E,
            0X5_5_0_C_7_D_C_3,
            0X7_2_B_E_5_D_7_4,
            0X8_0_D_E_B_1_F_E,
            0X9_B_D_C_0_6_A_7,
            0XC_1_9_B_F_1_7_4,
            0XE_4_9_B_6_9_C_1,
            0XE_F_B_E_4_7_8_6,
            0X0_F_C_1_9_D_C_6,
            0X2_4_0_C_A_1_C_C,
            0X2_D_E_9_2_C_6_F,
            0X4_A_7_4_8_4_A_A,
            0X5_C_B_0_A_9_D_C,
            0X7_6_F_9_8_8_D_A,
            0X9_8_3_E_5_1_5_2,
            0XA_8_3_1_C_6_6_D,
            0XB_0_0_3_2_7_C_8,
            0XB_F_5_9_7_F_C_7,
            0XC_6_E_0_0_B_F_3,
            0XD_5_A_7_9_1_4_7,
            0X0_6_C_A_6_3_5_1,
            0X1_4_2_9_2_9_6_7,
            0X2_7_B_7_0_A_8_5,
            0X2_E_1_B_2_1_3_8,
            0X4_D_2_C_6_D_F_C,
            0X5_3_3_8_0_D_1_3,
            0X6_5_0_A_7_3_5_4,
            0X7_6_6_A_0_A_B_B,
            0X8_1_C_2_C_9_2_E,
            0X9_2_7_2_2_C_8_5,
            0XA_2_B_F_E_8_A_1,
            0XA_8_1_A_6_6_4_B,
            0XC_2_4_B_8_B_7_0,
            0XC_7_6_C_5_1_A_3,
            0XD_1_9_2_E_8_1_9,
            0XD_6_9_9_0_6_2_4,
            0XF_4_0_E_3_5_8_5,
            0X1_0_6_A_A_0_7_0,
            0X1_9_A_4_C_1_1_6,
            0X1_E_3_7_6_C_0_8,
            0X2_7_4_8_7_7_4_C,
            0X3_4_B_0_B_C_B_5,
            0X3_9_1_C_0_C_B_3,
            0X4_E_D_8_A_A_4_A,
            0X5_B_9_C_C_A_4_F,
            0X6_8_2_E_6_F_F_3,
            0X7_4_8_F_8_2_E_E,
            0X7_8_A_5_6_3_6_F,
            0X8_4_C_8_7_8_1_4,
            0X8_C_C_7_0_2_0_8,
            0X9_0_B_E_F_F_F_A,
            0XA_4_5_0_6_C_E_B,
            0XB_E_F_9_A_3_F_7,
            0XC_6_7_1_7_8_F_2,
        ]

        SCREAMING_SNAKE_CASE_      =     self.preprocessing(self.data      )
        self.final_hash()







  @staticmethod
  def       __A   (							__magic_name__       :				bytes      )     ->		bytes:
        SCREAMING_SNAKE_CASE_      =     b"\x80" + (b"\x00" * (63 - (len(__magic_name__      ) + 8) % 64))
        SCREAMING_SNAKE_CASE_      =     struct.pack(">Q"     ,   (len(__magic_name__      ) * 8)      )
        return data + padding + big_endian_integer







  def       __A   (							self       :				Any      )     ->		None:
        # Convert into blocks of 64 bytes
        SCREAMING_SNAKE_CASE_      =     [
            self.preprocessed_data[x : x + 64]
            for x in range(0     ,   len(self.preprocessed_data      )     ,   64      )
        ]

        for block in self.blocks:
              # Convert the given block into a list of 4 byte integers
              SCREAMING_SNAKE_CASE_      =     list(struct.unpack(">16L"     ,   __magic_name__      )      )
              # add 48 0-ed integers
              words += [0] * 48

              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.hashes

              for index in range(0     ,   64      ):
                    if index > 15:
                          # modify the zero-ed indexes at the end of the array
                          SCREAMING_SNAKE_CASE_      =     (
                              self.ror(words[index - 15]     ,   7      )
                              ^ self.ror(words[index - 15]     ,   18      )
                              ^ (words[index - 15] >> 3)
                          )
                          SCREAMING_SNAKE_CASE_      =     (
                              self.ror(words[index - 2]     ,   17      )
                              ^ self.ror(words[index - 2]     ,   19      )
                              ^ (words[index - 2] >> 10)
                          )

                          SCREAMING_SNAKE_CASE_      =     (
                              words[index - 16] + sa + words[index - 7] + sa
                          ) % 0X1_0_0_0_0_0_0_0_0

                    # Compression
                    SCREAMING_SNAKE_CASE_      =     self.ror(__magic_name__     ,   6      ) ^ self.ror(__magic_name__     ,   11      ) ^ self.ror(__magic_name__     ,   25      )
                    SCREAMING_SNAKE_CASE_      =     (e & f) ^ ((~e & 0XF_F_F_F_F_F_F_F) & g)
                    SCREAMING_SNAKE_CASE_      =     (
                        h + sa + ch + self.round_constants[index] + words[index]
                    ) % 0X1_0_0_0_0_0_0_0_0
                    SCREAMING_SNAKE_CASE_      =     self.ror(__magic_name__     ,   2      ) ^ self.ror(__magic_name__     ,   13      ) ^ self.ror(__magic_name__     ,   22      )
                    SCREAMING_SNAKE_CASE_      =     (a & b) ^ (a & c) ^ (b & c)
                    SCREAMING_SNAKE_CASE_      =     (sa + maj) % 0X1_0_0_0_0_0_0_0_0

                    SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     (
                        g,
                        f,
                        e,
                        ((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
                        c,
                        b,
                        a,
                        ((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
                    )

              SCREAMING_SNAKE_CASE_      =     [a, b, c, d, e, f, g, h]

              # Modify final values
              SCREAMING_SNAKE_CASE_      =     [
                  ((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
                  for index, element in enumerate(self.hashes      )
              ]

        SCREAMING_SNAKE_CASE_      =     "".join([hex(__magic_name__      )[2:].zfill(8      ) for value in self.hashes]      )







  def       __A   (							self       :				Any     ,   __magic_name__       :				int     ,   __magic_name__       :				int      )     ->		int:
        return 0XF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)



class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				List[Any]      )     ->		None:
        import hashlib

        SCREAMING_SNAKE_CASE_      =     bytes("Test String"     ,   "utf-8"      )
        self.assertEqual(SHAaaa(__magic_name__      ).hash     ,   hashlib.shaaaa(__magic_name__      ).hexdigest()      )



def    a__       (						):
      import doctest

      doctest.testmod()

      SCREAMING_SNAKE_CASE_      =     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" )

      SCREAMING_SNAKE_CASE_      =     parser.parse_args()

      SCREAMING_SNAKE_CASE_      =     args.input_string

      # hash input should be a bytestring
      if args.input_file:
            with open(args.input_file   ,					"rb" ) as f:
                  SCREAMING_SNAKE_CASE_      =     f.read()
      else:
            SCREAMING_SNAKE_CASE_      =     bytes(__UpperCamelCase   ,					"utf-8" )

      print(SHAaaa(__UpperCamelCase ).hash )


if __name__ == "__main__":
      main()



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					encoder="bert"   ,					max_pos=5_1_2   ,					enc_layers=6   ,					enc_hidden_size=5_1_2   ,					enc_heads=8   ,					enc_ff_size=5_1_2   ,					enc_dropout=0.2   ,					dec_layers=6   ,					dec_hidden_size=7_6_8   ,					dec_heads=8   ,					dec_ff_size=2_0_4_8   ,					dec_dropout=0.2   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     BertAbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" ) )
      new_model.eval()

      # -------------------
      # Convert the weights
      # -------------------

      logging.info("convert the model" )
      new_model.bert.load_state_dict(original.bert.state_dict() )
      new_model.decoder.load_state_dict(original.decoder.state_dict() )
      new_model.generator.load_state_dict(original.generator.state_dict() )

      # ----------------------------------
      # Make sure the outpus are identical
      # ----------------------------------

      logging.info("Make sure that the models' outputs are identical" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

      # prepare the model inputs
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample éàalj'-." )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )
      SCREAMING_SNAKE_CASE_      =     tokenizer.encode("This is sample 3 éàalj'-." )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__UpperCamelCase )) )
      SCREAMING_SNAKE_CASE_      =     torch.tensor(__UpperCamelCase ).unsqueeze(0 )

      # failsafe to make sure the weights reset does not affect the
      # loaded weights.
      assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0

      # forward pass
      SCREAMING_SNAKE_CASE_      =     encoder_input_ids
      SCREAMING_SNAKE_CASE_      =     decoder_input_ids
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     None
      SCREAMING_SNAKE_CASE_      =     None

      # The original model does not apply the geneator layer immediatly but rather in
      # the beam search (where it combines softmax + linear layer). Since we already
      # apply the softmax in our generation process we only apply the linear layer here.
      # We make sure that the outputs of the full stack are identical
      SCREAMING_SNAKE_CASE_      =     original(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     original.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )
      SCREAMING_SNAKE_CASE_      =     torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
      print("Maximum absolute difference beween weights: {:.2f}".format(__UpperCamelCase ) )

      SCREAMING_SNAKE_CASE_      =     torch.allclose(__UpperCamelCase   ,					__UpperCamelCase   ,					atol=1E-3 )
      if are_identical:
            logging.info("all weights are equal up to 1e-3" )
      else:
            raise ValueError("the weights are different. The new model is likely different from the original one." )

      # The model has been saved with torch.save(model) and this is bound to the exact
      # directory structure. We save the state_dict instead.
      logging.info("saving the model's state dictionary" )
      torch.save(
          new_model.state_dict()   ,					"./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )


if __name__ == "__main__":
      A   :     Any       =				argparse.ArgumentParser()
      parser.add_argument(
          "--bertabs_checkpoint_path",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the output PyTorch model.",
      )
      A   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


