




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


__UpperCamelCase      : Dict					      =   logging.get_logger(__name__)

__UpperCamelCase      : List[str]					      =   {
    '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			(			UpperCAmelCase_				):
       UpperCamelCase__        =						'''m2m_100'''
       UpperCamelCase__        =						['''past_key_values''']
       UpperCamelCase__        =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
       def __init__(		self		:List[str]	,   __magic_name__		:Dict=12_8112	,   __magic_name__		:List[Any]=1024	,   __magic_name__		:Union[str, Any]=12	,   __magic_name__		:int=4096	,   __magic_name__		:Tuple=16	,   __magic_name__		:Dict=12	,   __magic_name__		:Dict=4096	,   __magic_name__		:Dict=16	,   __magic_name__		:str=0.05	,   __magic_name__		:Union[str, Any]=0.05	,   __magic_name__		:int=True	,   __magic_name__		:str=True	,   __magic_name__		:Optional[int]="relu"	,   __magic_name__		:Tuple=1024	,   __magic_name__		:Optional[Any]=0.1	,   __magic_name__		:int=0.1	,   __magic_name__		:Optional[Any]=0.0	,   __magic_name__		:int=0.02	,   __magic_name__		:List[Any]=2	,   __magic_name__		:str=True	,   __magic_name__		:Union[str, Any]=1	,   __magic_name__		:List[str]=0	,   __magic_name__		:Optional[int]=2	,   **__magic_name__		:Dict	,   ):







        '''simple docstring'''







        a		       =							vocab_size
        a		       =							max_position_embeddings
        a		       =							d_model
        a		       =							encoder_ffn_dim
        a		       =							encoder_layers
        a		       =							encoder_attention_heads
        a		       =							decoder_ffn_dim
        a		       =							decoder_layers
        a		       =							decoder_attention_heads
        a		       =							dropout
        a		       =							attention_dropout
        a		       =							activation_dropout
        a		       =							activation_function
        a		       =							init_std
        a		       =							encoder_layerdrop
        a		       =							decoder_layerdrop
        a		       =							use_cache
        a		       =							encoder_layers
        a		       =							scale_embedding  # scale factor will be sqrt(d_model) if True

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







class 	__lowerCAmelCase			(			UpperCAmelCase_				):
       @property
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        a		       =							OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]       )

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

        if self.use_past:
         self.fill_with_past_key_values_(__UpperCamelCase	,   direction="""inputs"""       )
        return common_inputs
       def    lowerCamelCase__							(		self		:int	,   __magic_name__		:List[Any]	,   __magic_name__		:Dict = -1	,   __magic_name__		:Optional[int] = -1	,   __magic_name__		:Tuple = False	,   __magic_name__		:Optional[int] = None	,   ):







        '''simple docstring'''







        a		       =							compute_effective_axis_dimension(
            __UpperCamelCase	,   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		       =							tokenizer.num_special_tokens_to_add(__UpperCamelCase       )
        a		       =							compute_effective_axis_dimension(
            __UpperCamelCase	,   fixed_dimension=OnnxConfig.default_fixed_sequence	,   num_token_to_add=__UpperCamelCase       )

        # Generate dummy inputs according to compute batch and sequence
        a		       =							[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
        a		       =							dict(tokenizer(__UpperCamelCase	,   return_tensors=__UpperCamelCase       )       )
        return common_inputs
       def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:int = -1	,   __magic_name__		:Any = -1	,   __magic_name__		:List[Any] = False	,   __magic_name__		:Optional[Any] = None	,   ):







        '''simple docstring'''







        a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __UpperCamelCase	,   __UpperCamelCase	,   __UpperCamelCase	,   __UpperCamelCase	,   __UpperCamelCase       )

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

        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		       =							common_inputs["""input_ids"""].shape
         a		       =							common_inputs["""decoder_input_ids"""].shape[1]
         a       ,				a		       =							self.num_attention_heads
         a		       =							(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         a		       =							decoder_seq_length + 3
         a		       =							(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         a		       =							torch.cat(
             [common_inputs["""decoder_attention_mask"""], torch.ones(__UpperCamelCase	,   __UpperCamelCase       )]	,   dim=1       )

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

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





       UpperCamelCase__        =						_generate_dummy_inputs_for_default_and_seqaseq_lm







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

