


import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A			=						logging.get_logger(__name__)

_A			=						{
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  "bart"
    UpperCAmelCase__						:		str              =  ["past_key_values"]
    UpperCAmelCase__						:		Any              =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(      self ,       A_=50265 ,       A_=1024 ,       A_=12 ,       A_=4096 ,       A_=16 ,       A_=12 ,       A_=4096 ,       A_=16 ,       A_=0.0 ,       A_=0.0 ,       A_="gelu" ,       A_=1024 ,       A_=0.1 ,       A_=0.0 ,       A_=0.0 ,       A_=0.02 ,       A_=0.0 ,       A_=False ,       A_=True ,       A_=3 ,       A_=1 ,       A_=0 ,       A_=2 ,       A_=True ,       A_=2 ,       A_=2 ,       **A_ ,       )  ->	Optional[Any]:
         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=d_model
         __UpperCamelCase   							=encoder_ffn_dim
         __UpperCamelCase   							=encoder_layers
         __UpperCamelCase   							=encoder_attention_heads
         __UpperCamelCase   							=decoder_ffn_dim
         __UpperCamelCase   							=decoder_layers
         __UpperCamelCase   							=decoder_attention_heads
         __UpperCamelCase   							=dropout
         __UpperCamelCase   							=attention_dropout
         __UpperCamelCase   							=activation_dropout
         __UpperCamelCase   							=activation_function
         __UpperCamelCase   							=init_std
         __UpperCamelCase   							=encoder_layerdrop
         __UpperCamelCase   							=decoder_layerdrop
         __UpperCamelCase   							=classifier_dropout
         __UpperCamelCase   							=use_cache
         __UpperCamelCase   							=encoder_layers
         __UpperCamelCase   							=scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=A_ ,       pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       is_encoder_decoder=A_ ,       decoder_start_token_id=A_ ,       forced_eos_token_id=A_ ,       **A_ ,       )

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' ,       A_   ):
              __UpperCamelCase   							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'   )






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    @property
    def _a (      self   )  ->	Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase   							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]   )

              if self.use_past:
                   __UpperCamelCase   							={0: 'batch'}
                   __UpperCamelCase   							={0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   __UpperCamelCase   							={0: 'batch', 1: 'decoder_sequence'}
                   __UpperCamelCase   							={0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(A_ ,       direction='inputs'   )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              __UpperCamelCase   							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]   )
              if self.use_past:
                   __UpperCamelCase							,				__UpperCamelCase   							=self.num_layers
                   for i in range(A_   ):
                        __UpperCamelCase   							={0: 'batch', 2: 'past_sequence + sequence'}
                        __UpperCamelCase   							={0: 'batch', 2: 'past_sequence + sequence'}
         else:
              __UpperCamelCase   							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ]   )

         return common_inputs






    @property
    def _a (      self   )  ->	Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase   							=super().outputs
         else:
              __UpperCamelCase   							=super(A_ ,       self   ).outputs
              if self.use_past:
                   __UpperCamelCase							,				__UpperCamelCase   							=self.num_layers
                   for i in range(A_   ):
                        __UpperCamelCase   							={0: 'batch', 2: 'past_sequence + sequence'}
                        __UpperCamelCase   							={0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def _a (      self ,       A_ ,       A_ = -1 ,       A_ = -1 ,       A_ = False ,       A_ = None ,       )  ->	Mapping[str, Any]:
         __UpperCamelCase   							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_ ,       A_ ,       A_ ,       A_ ,       A_   )

         # Generate decoder inputs
         __UpperCamelCase   							=seq_length if not self.use_past else 1
         __UpperCamelCase   							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_ ,       A_ ,       A_ ,       A_ ,       A_   )
         __UpperCamelCase   							={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         __UpperCamelCase   							=dict(**A_ ,       **A_   )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
              else:
                   import torch
              __UpperCamelCase							,				__UpperCamelCase   							=common_inputs['input_ids'].shape
              __UpperCamelCase   							=common_inputs['decoder_input_ids'].shape[1]
              __UpperCamelCase							,				__UpperCamelCase   							=self.num_attention_heads
              __UpperCamelCase   							=(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              __UpperCamelCase   							=decoder_seq_length + 3
              __UpperCamelCase   							=(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              __UpperCamelCase   							=torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(A_ ,       A_   )] ,       dim=1   )

              __UpperCamelCase   							=[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              __UpperCamelCase							,				__UpperCamelCase   							=self.num_layers
              __UpperCamelCase   							=min(A_ ,       A_   )
              __UpperCamelCase   							=max(A_ ,       A_   ) - min_num_layers
              __UpperCamelCase   							='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(A_   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(A_   ),
                           torch.zeros(A_   ),
                           torch.zeros(A_   ),
                           torch.zeros(A_   ),
                       )   )
              # TODO: test this.
              __UpperCamelCase   							=encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(A_ ,       A_   ):
                   common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
         return common_inputs






    def _a (      self ,       A_ ,       A_ = -1 ,       A_ = -1 ,       A_ = False ,       A_ = None ,       )  ->	Mapping[str, Any]:
         __UpperCamelCase   							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_ ,       A_ ,       A_ ,       A_ ,       A_   )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
              else:
                   import torch
              __UpperCamelCase							,				__UpperCamelCase   							=common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              __UpperCamelCase   							=seqlen + 2
              __UpperCamelCase							,				__UpperCamelCase   							=self.num_layers
              __UpperCamelCase							,				__UpperCamelCase   							=self.num_attention_heads
              __UpperCamelCase   							=(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              __UpperCamelCase   							=common_inputs['attention_mask'].dtype
              __UpperCamelCase   							=torch.cat(
                  [common_inputs['attention_mask'], torch.ones(A_ ,       A_ ,       dtype=A_   )] ,       dim=1   )
              __UpperCamelCase   							=[
                  (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
              ]
         return common_inputs






    def _a (      self ,       A_ ,       A_ = -1 ,       A_ = -1 ,       A_ = False ,       A_ = None ,       )  ->	Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __UpperCamelCase   							=compute_effective_axis_dimension(
             A_ ,       fixed_dimension=OnnxConfig.default_fixed_batch ,       num_token_to_add=0   )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __UpperCamelCase   							=tokenizer.num_special_tokens_to_add(A_   )
         __UpperCamelCase   							=compute_effective_axis_dimension(
             A_ ,       fixed_dimension=OnnxConfig.default_fixed_sequence ,       num_token_to_add=A_   )

         # Generate dummy inputs according to compute batch and sequence
         __UpperCamelCase   							=[' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
         __UpperCamelCase   							=dict(tokenizer(A_ ,       return_tensors=A_   )   )
         return common_inputs






    def _a (      self ,       A_ ,       A_ = -1 ,       A_ = -1 ,       A_ = False ,       A_ = None ,       )  ->	Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase   							=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  A_ ,       batch_size=A_ ,       seq_length=A_ ,       is_pair=A_ ,       framework=A_   )

         elif self.task == "causal-lm":
              __UpperCamelCase   							=self._generate_dummy_inputs_for_causal_lm(
                  A_ ,       batch_size=A_ ,       seq_length=A_ ,       is_pair=A_ ,       framework=A_   )
         else:
              __UpperCamelCase   							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  A_ ,       batch_size=A_ ,       seq_length=A_ ,       is_pair=A_ ,       framework=A_   )

         return common_inputs






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_   )  ->	Optional[int]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase   							=super()._flatten_past_key_values_(A_ ,       A_ ,       A_ ,       A_   )
         else:
              __UpperCamelCase   							=super(A_ ,       self   )._flatten_past_key_values_(
                  A_ ,       A_ ,       A_ ,       A_   )









import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812

def 						_UpperCAmelCase    (      ):
     print('Making key files...'    )
     make_key_files('rsa'      ,					10_24    )
     print('Key files generation successful.'    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     print('Generating prime p...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     print('Generating prime q...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=p * q

     print('Generating e that is relatively prime to (p - 1) * (q - 1)...'    )
     while True:
          __UpperCamelCase   							=random.randrange(2 ** (key_size - 1)      ,					2 ** (key_size)    )
          if cryptoMath.gcd(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    ) == 1:
               break

     print('Calculating d that is mod inverse of e...'    )
     __UpperCamelCase   							=cryptoMath.find_mod_inverse(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    )

     __UpperCamelCase   							=(n, e)
     __UpperCamelCase   							=(n, d)
     return (public_key, private_key)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if os.path.exists(F'{name}_pubkey.txt'    ) or os.path.exists(F'{name}_privkey.txt'    ):
          print('\nWARNING:'    )
          print(
              F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
              'Use a different name or delete these files and re-run this program.'    )
          sys.exit()

     __UpperCamelCase							,				__UpperCamelCase   							=generate_key(SCREAMING_SNAKE_CASE__    )
     print(F'\nWriting public key to file {name}_pubkey.txt...'    )
     with open(F'{name}_pubkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'    )

     print(F'Writing private key to file {name}_privkey.txt...'    )
     with open(F'{name}_privkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'    )


if __name__ == "__main__":
   main()





