"""simple docstring"""




import argparse
from collections import defaultdict






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							F'''{file}_{class_name}_{test_name}'''
							done_test[_id] += 1

							with open(UpperCamelCase__				,			'r' ) as f:
														A__		   =							f.readlines()

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

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

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

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

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

							with open(UpperCamelCase__				,			'w' ) as f:
														for line in new_lines:
																					f.write(UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=None ):






							"""simple docstring"""





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

							with open(UpperCamelCase__				,			'r' ) as f:
														A__		   =							f.readlines()

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


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)






"""simple docstring"""




from typing import List, Optional, Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "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__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						'time_series_transformer'
						lowerCAmelCase__     :  Dict  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "student_t"						,__UpperCAmelCase = "nll"						,__UpperCAmelCase = 1						,__UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]						,__UpperCAmelCase = "mean"						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 32						,__UpperCAmelCase = 32						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = True						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 64						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> List[str]:
													# time series specific configuration
													A__		   =							prediction_length
													A__		   =							context_length or prediction_length
													A__		   =							distribution_output
													A__		   =							loss
													A__		   =							input_size
													A__		   =							num_time_features
													A__		   =							lags_sequence
													A__		   =							scaling
													A__		   =							num_dynamic_real_features
													A__		   =							num_static_real_features
													A__		   =							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`'   )
																				A__		   =							cardinality
													else:
																				A__		   =							[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`'   )
																				A__		   =							embedding_dimension
													else:
																				A__		   =							[min(50						,(cat + 1) // 2   ) for cat in self.cardinality]
													A__		   =							num_parallel_samples

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

													A__		   =							dropout
													A__		   =							attention_dropout
													A__		   =							activation_dropout
													A__		   =							encoder_layerdrop
													A__		   =							decoder_layerdrop

													A__		   =							activation_function
													A__		   =							init_std

													A__		   =							use_cache

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





						@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
													)





