

from typing import List, Optional, Union

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


A_	:			Tuple  	=      logging.get_logger(__name__)

A_	:			Optional[int]  	=      {
    '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						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''time_series_transformer'''
					UpperCAmelCase__:       Union[str, Any]  							=	{
					    '''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__ = "mean"   ,	A__ = 0   ,	A__ = 0   ,	A__ = 0   ,	A__ = 0   ,	A__ = None   ,	A__ = None   ,	A__ = 32   ,	A__ = 32   ,	A__ = 2   ,	A__ = 2   ,	A__ = 2   ,	A__ = 2   ,	A__ = True   ,	A__ = "gelu"   ,	A__ = 64   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 0.1   ,	A__ = 100   ,	A__ = 0.0_2   ,	A__=True   ,	**A__   ,	):
						# time series specific configuration
						A__							:   str					       =		prediction_length
						A__							:   Optional[Any]					       =		context_length or prediction_length
						A__							:   Any					       =		distribution_output
						A__							:   Union[str, Any]					       =		loss
						A__							:   Optional[Any]					       =		input_size
						A__							:   List[str]					       =		num_time_features
						A__							:   int					       =		lags_sequence
						A__							:   Optional[Any]					       =		scaling
						A__							:   str					       =		num_dynamic_real_features
						A__							:   Tuple					       =		num_static_real_features
						A__							:   int					       =		num_static_categorical_features
						if cardinality 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`"""      )
							A__							:   Tuple					       =		cardinality
						else:
							A__							:   Optional[Any]					       =		[0]
						if embedding_dimension 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`"""      )
							A__							:   Optional[Any]					       =		embedding_dimension
						else:
							A__							:   Union[str, Any]					       =		[min(50   ,	(cat + 1) // 2      ) for cat in self.cardinality]
						A__							:   str					       =		num_parallel_samples

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

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

						A__							:   List[Any]					       =		activation_function
						A__							:   Optional[Any]					       =		init_std

						A__							:   Any					       =		use_cache

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






					@property
					def   __A      (			self      ):
						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
						)






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

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

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

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

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

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

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

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

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


if __name__ == "__main__":
	A_	:			Optional[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)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



