'''simple docstring'''
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		:			Any	      =logging.get_logger(__name__)

_A		:			Union[str, Any]	      ={
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class _lowerCAmelCase (  snake_case				):
      UpperCAmelCase_  :	List[str]     =					"mbart"
      UpperCAmelCase_  :	Optional[Any]     =					["past_key_values"]
      UpperCAmelCase_  :	Any     =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







      def __init__(     self     ,						lowercase=5_0265     ,						lowercase=1024     ,						lowercase=12     ,						lowercase=4096     ,						lowercase=16     ,						lowercase=12     ,						lowercase=4096     ,						lowercase=16     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=True     ,						lowercase=True     ,						lowercase="gelu"     ,						lowercase=1024     ,						lowercase=0.1     ,						lowercase=0.0     ,						lowercase=0.0     ,						lowercase=0.02     ,						lowercase=0.0     ,						lowercase=False     ,						lowercase=1     ,						lowercase=0     ,						lowercase=2     ,						lowercase=2     ,						**lowercase     ,						):
        lowercase							:       str											=      vocab_size
        lowercase							:       Optional[int]											=      max_position_embeddings
        lowercase							:       Any											=      d_model
        lowercase							:       Optional[int]											=      encoder_ffn_dim
        lowercase							:       List[Any]											=      encoder_layers
        lowercase							:       List[str]											=      encoder_attention_heads
        lowercase							:       Dict											=      decoder_ffn_dim
        lowercase							:       Any											=      decoder_layers
        lowercase							:       Optional[int]											=      decoder_attention_heads
        lowercase							:       str											=      dropout
        lowercase							:       str											=      attention_dropout
        lowercase							:       Tuple											=      activation_dropout
        lowercase							:       Optional[Any]											=      activation_function
        lowercase							:       Union[str, Any]											=      init_std
        lowercase							:       str											=      encoder_layerdrop
        lowercase							:       Dict											=      decoder_layerdrop
        lowercase							:       Any											=      classifier_dropout
        lowercase							:       str											=      use_cache
        lowercase							:       List[str]											=      encoder_layers
        lowercase							:       List[str]											=      scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowercase     ,						bos_token_id=lowercase     ,						eos_token_id=lowercase     ,						is_encoder_decoder=lowercase     ,						forced_eos_token_id=lowercase     ,						**lowercase     ,						)





class _lowerCAmelCase (  snake_case				):







      @property
      def     lowerCamelCase__			(     self    ):
        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       List[str]											=      OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ]    )

          if self.use_past:
            lowercase							:       str											=      {0: '''batch'''}
            lowercase							:       Union[str, Any]											=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
          else:
            lowercase							:       Optional[int]											=      {0: '''batch''', 1: '''decoder_sequence'''}
            lowercase							:       Dict											=      {0: '''batch''', 1: '''decoder_sequence'''}

          if self.use_past:
            self.fill_with_past_key_values_(lowercase     ,						direction='''inputs'''    )
        elif self.task == "causal-lm":
          # TODO: figure this case out.
          lowercase							:       List[str]											=      OrderedDict(
              [
                  ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                  ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ]    )
          if self.use_past:
            lowercase							,			lowercase							:       str											=      self.num_layers
            for i in range(lowercase    ):
              lowercase							:       str											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
              lowercase							:       Optional[Any]											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
          lowercase							:       Union[str, Any]											=      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     lowerCamelCase__			(     self    ):
        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       int											=      super().outputs
        else:
          lowercase							:       List[str]											=      super(lowercase     ,						self    ).outputs
          if self.use_past:
            lowercase							,			lowercase							:       List[str]											=      self.num_layers
            for i in range(lowercase    ):
              lowercase							:       Optional[Any]											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
              lowercase							:       List[str]											=      {0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        lowercase							:       Tuple											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase    )

        # Generate decoder inputs
        lowercase							:       str											=      seq_length if not self.use_past else 1
        lowercase							:       Any											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase    )
        lowercase							:       int											=      {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        lowercase							:       Tuple											=      dict(**lowercase     ,						**lowercase    )

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

          lowercase							:       int											=      torch.cat(
              [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase     ,						lowercase    )]     ,						dim=1    )

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

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







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        lowercase							:       int											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase    )

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

          lowercase							:       Dict											=      common_inputs['''attention_mask'''].dtype
          lowercase							:       Any											=      torch.cat(
              [common_inputs['''attention_mask'''], torch.ones(lowercase     ,						lowercase     ,						dtype=lowercase    )]     ,						dim=1    )
          lowercase							:       List[Any]											=      [
              (torch.zeros(lowercase    ), torch.zeros(lowercase    )) for _ in range(lowercase    )
          ]
        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        # 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
        lowercase							:       str											=      compute_effective_axis_dimension(
            lowercase     ,						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
        lowercase							:       Dict											=      tokenizer.num_special_tokens_to_add(lowercase    )
        lowercase							:       List[Any]											=      compute_effective_axis_dimension(
            lowercase     ,						fixed_dimension=OnnxConfig.default_fixed_sequence     ,						num_token_to_add=lowercase    )

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







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase = -1     ,						lowercase = -1     ,						lowercase = False     ,						lowercase = None     ,						):
        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Optional[Any]											=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase     ,						batch_size=lowercase     ,						seq_length=lowercase     ,						is_pair=lowercase     ,						framework=lowercase    )

        elif self.task == "causal-lm":
          lowercase							:       Optional[Any]											=      self._generate_dummy_inputs_for_causal_lm(
              lowercase     ,						batch_size=lowercase     ,						seq_length=lowercase     ,						is_pair=lowercase     ,						framework=lowercase    )
        else:
          lowercase							:       str											=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase     ,						batch_size=lowercase     ,						seq_length=lowercase     ,						is_pair=lowercase     ,						framework=lowercase    )

        return common_inputs







      def     lowerCamelCase__			(     self     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase    ):
        if self.task in ["default", "seq2seq-lm"]:
          lowercase							:       Union[str, Any]											=      super()._flatten_past_key_values_(lowercase     ,						lowercase     ,						lowercase     ,						lowercase    )
        else:
          lowercase							:       List[str]											=      super(lowercase     ,						self    )._flatten_past_key_values_(
              lowercase     ,						lowercase     ,						lowercase     ,						lowercase    )
'''simple docstring'''
import itertools
from pathlib import Path







def 							__UpperCamelCase   (				__UpperCAmelCase     :				list[str] )       ->  int:
  lowercase							:       List[str]											=      [tuple(__UpperCAmelCase ) for login in logins]

  lowercase							:       List[Any]											=      {char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCAmelCase ):
    lowercase							:       List[str]											=      True
    for login in logins:
      if not (
          permutation.index(login[0] )
          < permutation.index(login[1] )
          < permutation.index(login[2] )
      ):
        lowercase							:       List[str]											=      False
        break

    if satisfied:
      return int(''''''.join(__UpperCAmelCase ) )

  raise Exception('''Unable to find the secret passcode''' )







def 							__UpperCamelCase   (				__UpperCAmelCase     :				str = "keylog.txt" )       ->  int:
  lowercase							:       Union[str, Any]											=      Path(__UpperCAmelCase ).parent.joinpath(__UpperCAmelCase ).read_text().splitlines()

  return find_secret_passcode(__UpperCAmelCase )


if __name__ == "__main__":
 print(f"""{solution() = }""")