



"""simple docstring"""






from typing import List, Optional, Union

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


__snake_case				:  Dict =       logging.get_logger(__name__)

__snake_case				:  List[Any] =       {
    '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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'time_series_transformer'
	SCREAMING_SNAKE_CASE        =      {
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						str = "student_t"			,     _SCREAMING_SNAKE_CASE:						str = "nll"			,     _SCREAMING_SNAKE_CASE:						int = 1			,     _SCREAMING_SNAKE_CASE:						List[int] = [1, 2, 3, 4, 5, 6, 7]			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, bool]] = "mean"			,     _SCREAMING_SNAKE_CASE:						int = 0			,     _SCREAMING_SNAKE_CASE:						int = 0			,     _SCREAMING_SNAKE_CASE:						int = 0			,     _SCREAMING_SNAKE_CASE:						int = 0			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None			,     _SCREAMING_SNAKE_CASE:						int = 32			,     _SCREAMING_SNAKE_CASE:						int = 32			,     _SCREAMING_SNAKE_CASE:						int = 2			,     _SCREAMING_SNAKE_CASE:						int = 2			,     _SCREAMING_SNAKE_CASE:						int = 2			,     _SCREAMING_SNAKE_CASE:						int = 2			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						str = "gelu"			,     _SCREAMING_SNAKE_CASE:						int = 64			,     _SCREAMING_SNAKE_CASE:						float = 0.1			,     _SCREAMING_SNAKE_CASE:						float = 0.1			,     _SCREAMING_SNAKE_CASE:						float = 0.1			,     _SCREAMING_SNAKE_CASE:						float = 0.1			,     _SCREAMING_SNAKE_CASE:						float = 0.1			,     _SCREAMING_SNAKE_CASE:						int = 100			,     _SCREAMING_SNAKE_CASE:						float = 0.02			,     _SCREAMING_SNAKE_CASE:						Dict=True			,     **_SCREAMING_SNAKE_CASE:						Optional[int]			,     )							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			prediction_length
				__lowerCAmelCase :    str				=			context_length or prediction_length
				__lowerCAmelCase :    Optional[Any]				=			distribution_output
				__lowerCAmelCase :    Dict				=			loss
				__lowerCAmelCase :    Union[str, Any]				=			input_size
				__lowerCAmelCase :    Any				=			num_time_features
				__lowerCAmelCase :    Dict				=			lags_sequence
				__lowerCAmelCase :    str				=			scaling
				__lowerCAmelCase :    Optional[int]				=			num_dynamic_real_features
				__lowerCAmelCase :    Optional[int]				=			num_static_real_features
				__lowerCAmelCase :    List[Any]				=			num_static_categorical_features
				if cardinality and num_static_categorical_features > 0:
							if len(_SCREAMING_SNAKE_CASE) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`")
							__lowerCAmelCase :    str				=			cardinality
				else:
							__lowerCAmelCase :    Optional[Any]				=			[0]
				if embedding_dimension and num_static_categorical_features > 0:
							if len(_SCREAMING_SNAKE_CASE) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`")
							__lowerCAmelCase :    Optional[int]				=			embedding_dimension
				else:
							__lowerCAmelCase :    Tuple				=			[min(50			,     (cat + 1) // 2) for cat in self.cardinality]
				__lowerCAmelCase :    Optional[Any]				=			num_parallel_samples

				# Transformer architecture configuration
				__lowerCAmelCase :    int				=			input_size * len(_SCREAMING_SNAKE_CASE) + self._number_of_features
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			encoder_attention_heads
				__lowerCAmelCase :    Dict				=			decoder_attention_heads
				__lowerCAmelCase :    int				=			encoder_ffn_dim
				__lowerCAmelCase :    Union[str, Any]				=			decoder_ffn_dim
				__lowerCAmelCase :    Dict				=			encoder_layers
				__lowerCAmelCase :    List[str]				=			decoder_layers

				__lowerCAmelCase :    Optional[Any]				=			dropout
				__lowerCAmelCase :    List[str]				=			attention_dropout
				__lowerCAmelCase :    str				=			activation_dropout
				__lowerCAmelCase :    Any				=			encoder_layerdrop
				__lowerCAmelCase :    Optional[Any]				=			decoder_layerdrop

				__lowerCAmelCase :    Dict				=			activation_function
				__lowerCAmelCase :    int				=			init_std

				__lowerCAmelCase :    Tuple				=			use_cache

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







	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			int:

				"""simple docstring"""

				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
				)




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)