


"""simple docstring"""







from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_  :  Optional[Any]     		=    logging.get_logger(__name__)

lowerCamelCase_  :  int     		=    {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "longt5"
 __lowerCAmelCase					   =    ["past_key_values"]
 __lowerCAmelCase					   =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


 def __init__(						self					,       __A=3_2128					,       __A=512					,       __A=64					,       __A=2048					,       __A=6					,       __A=None					,       __A=8					,       __A=127					,       __A=16					,       __A=32					,       __A=128					,       __A=0.1					,       __A=1E-6					,       __A=1.0					,       __A="relu"					,       __A=True					,       __A="local"					,       __A=True					,       __A=0					,       __A=1					,       **__A					,       )   ->	Dict:
     a											=vocab_size
     a											=d_model
     a											=d_kv
     a											=d_ff
     a											=num_layers
     # default = symmetry
     a											=num_decoder_layers if num_decoder_layers is not None else self.num_layers
     a											=num_heads
     a											=local_radius
     a											=global_block_size
     a											=relative_attention_num_buckets
     a											=relative_attention_max_distance
     a											=dropout_rate
     a											=layer_norm_epsilon
     a											=initializer_factor
     a											=feed_forward_proj
     a											=encoder_attention_type
     a											=use_cache

     a											=self.feed_forward_proj.split('''-'''		)
     a											=act_info[-1]
     a											=act_info[0] == '''gated'''

     if len(__A		) > 1 and act_info[0] != "gated" or len(__A		) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
             '''\'gated-gelu\' or \'relu\''''		)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         a											='''gelu_new'''

     super().__init__(
         pad_token_id=__A					,       eos_token_id=__A					,       is_encoder_decoder=__A					,       **__A					,       )






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Mapping[str, Mapping[int, str]]:
     a											={
         '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
         '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
     }
     if self.use_past:
         a											='''past_encoder_sequence + sequence'''
         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_(__A					,       direction='''inputs'''		)

     return common_inputs





 @property
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     return 13



"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

    a											={char for login in split_logins for char in login}

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

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

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


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