'''simple docstring'''







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				:					str    						=			logging.get_logger(__name__)

A				:					Optional[Any]    						=			{
    "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 			__lowerCamelCase			(	lowercase_	):
    """simple docstring"""
    a													=   "bart"
    a													=   ["past_key_values"]
    a													=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


    def __init__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							Dict=50265				,							SCREAMING_SNAKE_CASE      :							List[Any]=1024				,							SCREAMING_SNAKE_CASE      :							str=12				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=4096				,							SCREAMING_SNAKE_CASE      :							str=16				,							SCREAMING_SNAKE_CASE      :							Dict=12				,							SCREAMING_SNAKE_CASE      :							List[Any]=4096				,							SCREAMING_SNAKE_CASE      :							List[str]=16				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.0				,							SCREAMING_SNAKE_CASE      :							List[str]=0.0				,							SCREAMING_SNAKE_CASE      :							Optional[int]="gelu"				,							SCREAMING_SNAKE_CASE      :							Any=1024				,							SCREAMING_SNAKE_CASE      :							str=0.1				,							SCREAMING_SNAKE_CASE      :							List[Any]=0.0				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0.0				,							SCREAMING_SNAKE_CASE      :							Tuple=0.02				,							SCREAMING_SNAKE_CASE      :							Any=0.0				,							SCREAMING_SNAKE_CASE      :							Optional[int]=False				,							SCREAMING_SNAKE_CASE      :							Tuple=True				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=3				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							Dict=0				,							SCREAMING_SNAKE_CASE      :							List[Any]=2				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							SCREAMING_SNAKE_CASE      :							Dict=2				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=2				,							**SCREAMING_SNAKE_CASE      :							Any				,							):
           _A					:     int     = vocab_size
           _A					:     Optional[Any]     = max_position_embeddings
           _A					:     List[str]     = d_model
           _A					:     Dict     = encoder_ffn_dim
           _A					:     Dict     = encoder_layers
           _A					:     Tuple     = encoder_attention_heads
           _A					:     Optional[int]     = decoder_ffn_dim
           _A					:     int     = decoder_layers
           _A					:     List[Any]     = decoder_attention_heads
           _A					:     int     = dropout
           _A					:     str     = attention_dropout
           _A					:     Dict     = activation_dropout
           _A					:     int     = activation_function
           _A					:     int     = init_std
           _A					:     Dict     = encoder_layerdrop
           _A					:     Dict     = decoder_layerdrop
           _A					:     Dict     = classifier_dropout
           _A					:     Dict     = use_cache
           _A					:     List[str]     = encoder_layers
           _A					:     List[str]     = 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__):
                  _A					:     Dict     = 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 			__lowerCamelCase			(	lowercase_	):
    """simple docstring"""


    @property
    def        A   (  self      :							List[Any]):




           if self.task in ["default", "seq2seq-lm"]:
                  _A					:     Union[str, Any]     = OrderedDict(
                      [
                          ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                          ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ])

                  if self.use_past:
                         _A					:     Tuple     = {0: 'batch'}
                         _A					:     str     = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                  else:
                         _A					:     Any     = {0: 'batch', 1: 'decoder_sequence'}
                         _A					:     Tuple     = {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.
                  _A					:     Tuple     = OrderedDict(
                      [
                          ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                          ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ])
                  if self.use_past:
                         _A  ,			_A					:     Optional[int]     = self.num_layers
                         for i in range(a__):
                                _A					:     int     = {0: 'batch', 2: 'past_sequence + sequence'}
                                _A					:     Optional[int]     = {0: 'batch', 2: 'past_sequence + sequence'}
           else:
                  _A					:     str     = 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      :							Tuple):




           if self.task in ["default", "seq2seq-lm"]:
                  _A					:     Optional[Any]     = super().outputs
           else:
                  _A					:     List[str]     = super(a__				,							self).outputs
                  if self.use_past:
                         _A  ,			_A					:     List[str]     = self.num_layers
                         for i in range(a__):
                                _A					:     int     = {0: 'batch', 2: 'past_sequence + sequence'}
                                _A					:     List[Any]     = {0: 'batch', 2: 'past_sequence + sequence'}
           return common_outputs


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Dict = -1				,							SCREAMING_SNAKE_CASE      :							Optional[int] = -1				,							SCREAMING_SNAKE_CASE      :							List[Any] = False				,							SCREAMING_SNAKE_CASE      :							Dict = None				,							):
           _A					:     List[Any]     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a__				,							a__				,							a__				,							a__				,							a__)

           # Generate decoder inputs
           _A					:     Optional[Any]     = seq_length if not self.use_past else 1
           _A					:     str     = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               a__				,							a__				,							a__				,							a__				,							a__)
           _A					:     Dict     = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
           _A					:     Optional[Any]     = 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
                  _A  ,			_A					:     List[Any]     = common_inputs['input_ids'].shape
                  _A					:     List[str]     = common_inputs['decoder_input_ids'].shape[1]
                  _A  ,			_A					:     Union[str, Any]     = self.num_attention_heads
                  _A					:     Any     = (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  _A					:     Optional[int]     = decoder_seq_length + 3
                  _A					:     Tuple     = (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  _A					:     List[Any]     = torch.cat(
                      [common_inputs['decoder_attention_mask'], torch.ones(a__				,							a__)]				,							dim=1)

                  _A					:     Tuple     = []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  _A  ,			_A					:     Union[str, Any]     = self.num_layers
                  _A					:     Tuple     = min(a__				,							a__)
                  _A					:     List[Any]     = max(a__				,							a__) - min_num_layers
                  _A					:     Optional[Any]     = '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.
                  _A					:     Optional[int]     = 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      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Tuple = -1				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = -1				,							SCREAMING_SNAKE_CASE      :							Dict = False				,							SCREAMING_SNAKE_CASE      :							Union[str, Any] = None				,							):
           _A					:     List[Any]     = 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
                  _A  ,			_A					:     List[Any]     = common_inputs['input_ids'].shape
                  # Not using the same length for past_key_values
                  _A					:     Optional[int]     = seqlen + 2
                  _A  ,			_A					:     List[Any]     = self.num_layers
                  _A  ,			_A					:     Any     = self.num_attention_heads
                  _A					:     List[Any]     = (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  _A					:     int     = common_inputs['attention_mask'].dtype
                  _A					:     List[Any]     = torch.cat(
                      [common_inputs['attention_mask'], torch.ones(a__				,							a__				,							dtype=a__)]				,							dim=1)
                  _A					:     Union[str, Any]     = [
                      (torch.zeros(a__), torch.zeros(a__)) for _ in range(a__)
                  ]
           return common_inputs


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict = -1				,							SCREAMING_SNAKE_CASE      :							str = -1				,							SCREAMING_SNAKE_CASE      :							Optional[Any] = False				,							SCREAMING_SNAKE_CASE      :							Any = None				,							):
           _A					:     Optional[Any]     = 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
           _A					:     Optional[int]     = tokenizer.num_special_tokens_to_add(a__)
           _A					:     int     = 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
           _A					:     List[Any]     = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
           _A					:     Dict     = dict(tokenizer(a__				,							return_tensors=a__))
           return common_inputs


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any] = -1				,							SCREAMING_SNAKE_CASE      :							List[str] = -1				,							SCREAMING_SNAKE_CASE      :							Optional[int] = False				,							SCREAMING_SNAKE_CASE      :							List[Any] = None				,							):




           if self.task in ["default", "seq2seq-lm"]:
                  _A					:     Any     = 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":
                  _A					:     Dict     = self._generate_dummy_inputs_for_causal_lm(
                      a__				,							batch_size=a__				,							seq_length=a__				,							is_pair=a__				,							framework=a__)
           else:
                  _A					:     List[Any]     = 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      :							int				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Any):




           if self.task in ["default", "seq2seq-lm"]:
                  _A					:     Dict     = super()._flatten_past_key_values_(a__				,							a__				,							a__				,							a__)
           else:
                  _A					:     str     = super(a__				,							self)._flatten_past_key_values_(
                      a__				,							a__				,							a__				,							a__)










'''simple docstring'''







import string

import numpy






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int       ):
       return b if a == 0 else greatest_common_divisor(b % a  ,lowerCamelCase       )


class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    a													=   numpy.vectorize(lambda a_	: x % 36	)

    a													=   numpy.vectorize(a_	)


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							numpy.ndarray):
           _A					:     Union[str, Any]     = self.modulus(SCREAMING_SNAKE_CASE)  # mod36 calc's on the encrypt key
           self.check_determinant()  # validate the determinant of the encryption key
           _A					:     Optional[int]     = encrypt_key.shape[0]


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str):
           return self.key_string.index(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int):
           return self.key_string[round(SCREAMING_SNAKE_CASE)]


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     int     = det % len(self.key_string)

           _A					:     str     = len(self.key_string)
           if greatest_common_divisor(SCREAMING_SNAKE_CASE				,							len(self.key_string)) != 1:
                  _A					:     Optional[int]     = (
                      F'determinant modular {req_l} of encryption key({det}) '
                      F'is not co prime w.r.t {req_l}.\nTry another key.'
                  )
                  raise ValueError(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = [char for char in text.upper() if char in self.key_string]

           _A					:     List[str]     = chars[-1]
           while len(SCREAMING_SNAKE_CASE) % self.break_key != 0:
                  chars.append(SCREAMING_SNAKE_CASE)

           return "".join(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     Optional[int]     = self.process_text(text.upper())
           _A					:     List[str]     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[Any]     = text[i : i + self.break_key]
                  _A					:     Optional[int]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     List[str]     = self.modulus(self.encrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[
                      0
                  ]
                  _A					:     str     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_encrypted)
                  encrypted += encrypted_batch

           return encrypted


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     Optional[int]     = det % len(self.key_string)
           _A					:     List[str]     = None
           for i in range(len(self.key_string)):
                  if (det * i) % len(self.key_string) == 1:
                         _A					:     Union[str, Any]     = i
                         break

           _A					:     Dict     = (
               det_inv
               * numpy.linalg.det(self.encrypt_key)
               * numpy.linalg.inv(self.encrypt_key)
           )

           return self.to_int(self.modulus(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[str]     = self.make_decrypt_key()
           _A					:     Dict     = self.process_text(text.upper())
           _A					:     str     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[int]     = text[i : i + self.break_key]
                  _A					:     Union[str, Any]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     Optional[int]     = self.modulus(decrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[0]
                  _A					:     Tuple     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_decrypted)
                  decrypted += decrypted_batch

           return decrypted






def 				lowerCAmelCase__	(    ):
       _A					:     List[Any]     = int(input('Enter the order of the encryption key: '       )       )
       _A					:     List[str]     = []

       print('Enter each row of the encryption key with space separated integers'       )
       for _ in range(lowerCamelCase       ):
              _A					:     str     = [int(lowerCamelCase       ) for x in input().split()]
              hill_matrix.append(lowerCamelCase       )

       _A					:     Dict     = HillCipher(numpy.array(lowerCamelCase       )       )

       print('Would you like to encrypt or decrypt some text? (1 or 2)'       )
       _A					:     List[str]     = input('\n1. Encrypt\n2. Decrypt\n'       )
       if option == "1":
              _A					:     int     = input('What text would you like to encrypt?: '       )
              print('Your encrypted text is:'       )
              print(hc.encrypt(lowerCamelCase       )       )
       elif option == "2":
              _A					:     int     = input('What text would you like to decrypt?: '       )
              print('Your decrypted text is:'       )
              print(hc.decrypt(lowerCamelCase       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   main()






