



'''simple docstring'''





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

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


A			:   Dict			=      logging.get_logger(__name__)

A			:   Tuple			=      {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	'm2m_100'
    __UpperCAmelCase							=	['past_key_values']
    __UpperCAmelCase							=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





    def __init__(    self    :    Dict,    _snake_case    :    Dict=128_112,    _snake_case    :    int=1_024,    _snake_case    :    List[Any]=12,    _snake_case    :    List[Any]=4_096,    _snake_case    :    str=16,    _snake_case    :    List[str]=12,    _snake_case    :    str=4_096,    _snake_case    :    Tuple=16,    _snake_case    :    str=0.05,    _snake_case    :    Any=0.05,    _snake_case    :    Any=True,    _snake_case    :    List[Any]=True,    _snake_case    :    Optional[int]="relu",    _snake_case    :    Tuple=1_024,    _snake_case    :    Optional[int]=0.1,    _snake_case    :    Tuple=0.1,    _snake_case    :    List[Any]=0.0,    _snake_case    :    Optional[Any]=0.02,    _snake_case    :    Union[str, Any]=2,    _snake_case    :    Optional[Any]=True,    _snake_case    :    Union[str, Any]=1,    _snake_case    :    str=0,    _snake_case    :    Union[str, Any]=2,    **_snake_case    :    Union[str, Any],    ):



       '''simple docstring'''







       snake_case   :							int						  =vocab_size
       snake_case   :							Optional[int]						  =max_position_embeddings
       snake_case   :							Dict						  =d_model
       snake_case   :							Dict						  =encoder_ffn_dim
       snake_case   :							Tuple						  =encoder_layers
       snake_case   :							Any						  =encoder_attention_heads
       snake_case   :							int						  =decoder_ffn_dim
       snake_case   :							Optional[int]						  =decoder_layers
       snake_case   :							Optional[Any]						  =decoder_attention_heads
       snake_case   :							Union[str, Any]						  =dropout
       snake_case   :							List[Any]						  =attention_dropout
       snake_case   :							Optional[int]						  =activation_dropout
       snake_case   :							Optional[int]						  =activation_function
       snake_case   :							List[Any]						  =init_std
       snake_case   :							Optional[int]						  =encoder_layerdrop
       snake_case   :							Union[str, Any]						  =decoder_layerdrop
       snake_case   :							int						  =use_cache
       snake_case   :							Optional[int]						  =encoder_layers
       snake_case   :							Tuple						  =scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_snake_case,    bos_token_id=_snake_case,    eos_token_id=_snake_case,    is_encoder_decoder=_snake_case,    decoder_start_token_id=_snake_case,    **_snake_case,    )





class 							lowerCAmelCase_		(	a_						):





    @property
    def 					__snake_case       (    self    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]     )

       if self.use_past:
          snake_case   :							int						  ={0: '''batch'''}
          snake_case   :							Tuple						  ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
          snake_case   :							Tuple						  ={0: '''batch''', 1: '''decoder_sequence'''}
          snake_case   :							str						  ={0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
          self.fill_with_past_key_values_(_snake_case,    direction='''inputs'''     )
       return common_inputs





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    PreTrainedTokenizer,    _snake_case    :    int = -1,    _snake_case    :    int = -1,    _snake_case    :    bool = False,    _snake_case    :    Optional[TensorType] = None,    ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =compute_effective_axis_dimension(
           _snake_case,    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
       snake_case   :							Any						  =tokenizer.num_special_tokens_to_add(_snake_case     )
       snake_case   :							Any						  =compute_effective_axis_dimension(
           _snake_case,    fixed_dimension=OnnxConfig.default_fixed_sequence,    num_token_to_add=_snake_case     )

       # Generate dummy inputs according to compute batch and sequence
       snake_case   :							Tuple						  =[''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
       snake_case   :							Optional[int]						  =dict(tokenizer(_snake_case,    return_tensors=_snake_case     )     )
       return common_inputs





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    PreTrainedTokenizer,    _snake_case    :    int = -1,    _snake_case    :    int = -1,    _snake_case    :    bool = False,    _snake_case    :    Optional[TensorType] = None,    ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _snake_case,    _snake_case,    _snake_case,    _snake_case,    _snake_case     )

       # Generate decoder inputs
       snake_case   :							Optional[int]						  =seq_length if not self.use_past else 1
       snake_case   :							Optional[Any]						  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _snake_case,    _snake_case,    _snake_case,    _snake_case,    _snake_case     )
       snake_case   :							List[str]						  ={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       snake_case   :							str						  =dict(**_snake_case,    **_snake_case     )

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

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

          snake_case   :							Dict						  =[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          snake_case  ,    snake_case   :							str						  =self.num_layers
          snake_case   :							Any						  =min(_snake_case,    _snake_case     )
          snake_case   :							List[Any]						  =max(_snake_case,    _snake_case     ) - min_num_layers
          snake_case   :							str						  ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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






    __UpperCAmelCase							=	_generate_dummy_inputs_for_default_and_seqaseq_lm










'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





    def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
       snake_case   :							List[str]						  =self.__make_key_list()
       snake_case   :							Dict						  =self.__make_shift_key()





    def __str__(    self    :    List[Any]     ):



       '''simple docstring'''







       return "".join(self.__passcode     )





    def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



       '''simple docstring'''







       for i in range(1,    len(_snake_case     ),    2     ):
          iterlist[i] *= -1
       return iterlist





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							int						  =string.ascii_letters + string.digits
       snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
       return password





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Dict						  =(
           string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
       )

       snake_case   :							Any						  =[]

       # creates points known as breakpoints to break the key_list_options at those
       # points and pivot each substring
       snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
       snake_case   :							list[str]						  =[]

       # algorithm for creating a new shuffled list, keys_l, out of key_list_options
       for i in key_list_options:
          temp_list.extend(_snake_case     )

          # checking breakpoints at which to pivot temporary sublist and add it into
          # keys_l
          if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]     )
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
       return keys_l





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
       return num if num > 0 else len(self.__passcode     )





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =''''''

       # decoding shift like Caesar cipher algorithm implementing negative shift or
       # reverse shift or left shift
       for i in encoded_message:
          snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
          decoded_message += self.__key_list[
              (position - self.__shift_key) % -len(self.__key_list     )
          ]

       return decoded_message






    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



       '''simple docstring'''







       snake_case   :							int						  =''''''

       # encoding shift like Caesar cipher algorithm implementing positive shift or
       # forward shift or right shift
       for i in plaintext:
          snake_case   :							int						  =self.__key_list.index(_snake_case     )
          encoded_message += self.__key_list[
              (position + self.__shift_key) % len(self.__key_list     )
          ]

       return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
   snake_case   :							List[Any]						  =ShuffledShiftCipher()
   return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





