



'''simple docstring'''




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			float:


							def get_matched_characters(_lowercase	,				_lowercase		) -> str:
														UpperCAmelCase     :		List[Any]							     =		[]
														UpperCAmelCase     :		Tuple							     =		min(len(_stra		)	,				len(_stra		)		) // 2
														for i, l in enumerate(_stra		):
																					UpperCAmelCase     :		List[str]							     =		int(max(0	,				i - limit		)		)
																					UpperCAmelCase     :		Any							     =		int(min(i + limit + 1	,				len(_stra		)		)		)
																					if l in _stra[left:right]:
																												matched.append(UpperCamelCase_		)
																												UpperCAmelCase     :		Optional[Any]							     =		F'''{_stra[0:_stra.index(UpperCamelCase_		)]} {_stra[_stra.index(UpperCamelCase_		) + 1:]}'''

														return "".join(UpperCamelCase_		)

							# matching characters
							UpperCAmelCase     :		List[Any]							     =		get_matched_characters(UpperCamelCase_	,				UpperCamelCase_		)
							UpperCAmelCase     :		Union[str, Any]							     =		get_matched_characters(UpperCamelCase_	,				UpperCamelCase_		)
							UpperCAmelCase     :		List[Any]							     =		len(UpperCamelCase_		)

							# transposition
							UpperCAmelCase     :		Dict							     =		(
							    len([(ca, ca) for ca, ca in zip(UpperCamelCase_	,				UpperCamelCase_		) if ca != ca]		) // 2
							)

							if not match_count:
														UpperCAmelCase     :		Tuple							     =		0.0
							else:
														UpperCAmelCase     :		Any							     =		(
														    1
														    / 3
														    * (
														        match_count / len(UpperCamelCase_		)
														        + match_count / len(UpperCamelCase_		)
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							UpperCAmelCase     :		Any							     =		0
							for ca, ca in zip(stra[:4]	,				stra[:4]		):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(jaro_winkler("""hello""", """world"""))




"""simple docstring"""





from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a       =							logging.get_logger(__name__)

_a       =							{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	"time_series_transformer"
 __UpperCAmelCase	:					Any   		=	{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(			self							:			int,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			str = "student_t",					UpperCAmelCase__							:			str = "nll",					UpperCAmelCase__							:			int = 1,					UpperCAmelCase__							:			List[int] = [1, 2, 3, 4, 5, 6, 7],					UpperCAmelCase__							:			Optional[Union[str, bool]] = "mean",					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			Optional[List[int]] = None,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 3_2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			int = 2,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			str = "gelu",					UpperCAmelCase__							:			int = 6_4,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			int = 1_0_0,					UpperCAmelCase__							:			float = 0.02,					UpperCAmelCase__							:			Any=True,					**UpperCAmelCase__							:			List[str],					):
    # time series specific configuration
    __lowercase								=						prediction_length
    __lowercase								=						context_length or prediction_length
    __lowercase								=						distribution_output
    __lowercase								=						loss
    __lowercase								=						input_size
    __lowercase								=						num_time_features
    __lowercase								=						lags_sequence
    __lowercase								=						scaling
    __lowercase								=						num_dynamic_real_features
    __lowercase								=						num_static_real_features
    __lowercase								=						num_static_categorical_features
    if cardinality and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The cardinality should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						cardinality
    else:
       __lowercase								=						[0]
    if embedding_dimension and num_static_categorical_features > 0:
       if len(UpperCAmelCase__	) != num_static_categorical_features:
          raise ValueError(
              "The embedding dimension should be a list of the same length as `num_static_categorical_features`"	)
       __lowercase								=						embedding_dimension
    else:
       __lowercase								=						[min(5_0,					(cat + 1) // 2	) for cat in self.cardinality]
    __lowercase								=						num_parallel_samples

    # Transformer architecture configuration
    __lowercase								=						input_size * len(UpperCAmelCase__	) + self._number_of_features
    __lowercase								=						d_model
    __lowercase								=						encoder_attention_heads
    __lowercase								=						decoder_attention_heads
    __lowercase								=						encoder_ffn_dim
    __lowercase								=						decoder_ffn_dim
    __lowercase								=						encoder_layers
    __lowercase								=						decoder_layers

    __lowercase								=						dropout
    __lowercase								=						attention_dropout
    __lowercase								=						activation_dropout
    __lowercase								=						encoder_layerdrop
    __lowercase								=						decoder_layerdrop

    __lowercase								=						activation_function
    __lowercase								=						init_std

    __lowercase								=						use_cache

    super().__init__(is_encoder_decoder=UpperCAmelCase__,					**UpperCAmelCase__	)



 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    return (
        sum(self.embedding_dimension	)
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )




