





from typing import List, Optional

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


lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"autoformer"
						UpperCAmelCase_ =			{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(self   ,							__a = None   ,							__a = None   ,							__a = "student_t"   ,							__a = "nll"   ,							__a = 1   ,							__a = [1, 2, 3, 4, 5, 6, 7]   ,							__a = True   ,							__a = 0   ,							__a = 0   ,							__a = 0   ,							__a = 0   ,							__a = None   ,							__a = None   ,							__a = 64   ,							__a = 2   ,							__a = 2   ,							__a = 2   ,							__a = 2   ,							__a = 32   ,							__a = 32   ,							__a = "gelu"   ,							__a = 0.1   ,							__a = 0.1   ,							__a = 0.1   ,							__a = 0.1   ,							__a = 0.1   ,							__a = 1_00   ,							__a = 0.02   ,							__a = True   ,							__a=True   ,							__a = 10   ,							__a = 25   ,							__a = 3   ,							**__a   ,							) ->   Tuple:
								# time series specific configuration
								UpperCamelCase   =	prediction_length
								UpperCamelCase   =	context_length if context_length is not None else prediction_length
								UpperCamelCase   =	distribution_output
								UpperCamelCase   =	loss
								UpperCamelCase   =	input_size
								UpperCamelCase   =	num_time_features
								UpperCamelCase   =	lags_sequence
								UpperCamelCase   =	scaling
								UpperCamelCase   =	num_dynamic_real_features
								UpperCamelCase   =	num_static_real_features
								UpperCamelCase   =	num_static_categorical_features
								if cardinality is not None and num_static_categorical_features > 0:
										if len(__a		) != num_static_categorical_features:
												raise ValueError(
												    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
										UpperCamelCase   =	cardinality
								else:
										UpperCamelCase   =	[0]
								if embedding_dimension is not None and num_static_categorical_features > 0:
										if len(__a		) != num_static_categorical_features:
												raise ValueError(
												    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
										UpperCamelCase   =	embedding_dimension
								else:
										UpperCamelCase   =	[min(50   ,							(cat + 1) // 2		) for cat in self.cardinality]
								UpperCamelCase   =	num_parallel_samples

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

								UpperCamelCase   =	dropout
								UpperCamelCase   =	attention_dropout
								UpperCamelCase   =	activation_dropout
								UpperCamelCase   =	encoder_layerdrop
								UpperCamelCase   =	decoder_layerdrop

								UpperCamelCase   =	activation_function
								UpperCamelCase   =	init_std

								UpperCamelCase   =	use_cache

								# Autoformer
								UpperCamelCase   =	label_length
								UpperCamelCase   =	moving_average
								UpperCamelCase   =	autocorrelation_factor

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




						@property
						def     snake_case_   (self		) ->   int:
								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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	F"{file}_{class_name}_{test_name}"
		done_test[_id] += 1

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	F"class {class_name}("
		UpperCamelCase   =	F"{4 * ' '}def {test_name}("
		UpperCamelCase   =	F"{8 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	F"{16 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	0
		UpperCamelCase   =	0

		UpperCamelCase   =	[]
		for line in lines:
				if line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and in_func and (line.startswith(_SCREAMING_SNAKE_CASE    ) or line.startswith(_SCREAMING_SNAKE_CASE    )):
						UpperCamelCase   =	len(line.split(correct_line.split()[0]    )[0]    )
						count += 1

						if count == done_test[_id]:
								UpperCamelCase   =	True

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

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F"{spaces * ' '}{correct_line}"    )
						UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	False
				else:
						new_lines.append(_SCREAMING_SNAKE_CASE    )

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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		if fail is not None:
				with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
						UpperCamelCase   =	{l.strip() for l in f.readlines()}
		else:
				UpperCamelCase   =	None

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	defaultdict(_SCREAMING_SNAKE_CASE    )
		for line in correct_lines:
				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	line.split(";"    )
				if test_failures is None or "::".join([file, class_name, test_name]    ) in test_failures:
						overwrite_file(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			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)
						lowerCAmelCase__     			=			parser.parse_args()

						main(args.correct_filename, args.fail_filename)






