
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 	UpperCAmelCase_ (__SCREAMING_SNAKE_CASE							):
	"""simple docstring"""

	lowerCamelCase     :       Any            =			'time_series_transformer'
	lowerCamelCase     :       List[Any]            =			{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}







	def __init__(     self:     Tuple							,   _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 = 32							,   _UpperCAmelCase:     int = 32							,   _UpperCAmelCase:     int = 2							,   _UpperCAmelCase:     int = 2							,   _UpperCAmelCase:     int = 2							,   _UpperCAmelCase:     int = 2							,   _UpperCAmelCase:     bool = True							,   _UpperCAmelCase:     str = "gelu"							,   _UpperCAmelCase:     int = 64							,   _UpperCAmelCase:     float = 0.1							,   _UpperCAmelCase:     float = 0.1							,   _UpperCAmelCase:     float = 0.1							,   _UpperCAmelCase:     float = 0.1							,   _UpperCAmelCase:     float = 0.1							,   _UpperCAmelCase:     int = 100							,   _UpperCAmelCase:     float = 0.0_2							,   _UpperCAmelCase:     Dict=True							,   **_UpperCAmelCase:     Optional[int]							,   ):
								_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			):
								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)