
from typing import List, Optional, Union

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


lowercase__			  =   logging.get_logger(__name__)

lowercase__			  =   {
    "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     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Tuple	 ='''time_series_transformer'''
							lowercase_	:	List[str]	 ={
							    '''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__ = 3_2       ,A__ = 3_2       ,A__ = 2       ,A__ = 2       ,A__ = 2       ,A__ = 2       ,A__ = True       ,A__ = "gelu"       ,A__ = 6_4       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 1_0_0       ,A__ = 0.02       ,A__=True       ,**A__       ,):
												# time series specific configuration
												lowercase						     =						prediction_length
												lowercase						     =						context_length or prediction_length
												lowercase						     =						distribution_output
												lowercase						     =						loss
												lowercase						     =						input_size
												lowercase						     =						num_time_features
												lowercase						     =						lags_sequence
												lowercase						     =						scaling
												lowercase						     =						num_dynamic_real_features
												lowercase						     =						num_static_real_features
												lowercase						     =						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`''')
																	lowercase						     =						cardinality
												else:
																	lowercase						     =						[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`''')
																	lowercase						     =						embedding_dimension
												else:
																	lowercase						     =						[min(5_0       ,(cat + 1) // 2) for cat in self.cardinality]
												lowercase						     =						num_parallel_samples

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

												lowercase						     =						dropout
												lowercase						     =						attention_dropout
												lowercase						     =						activation_dropout
												lowercase						     =						encoder_layerdrop
												lowercase						     =						decoder_layerdrop

												lowercase						     =						activation_function
												lowercase						     =						init_std

												lowercase						     =						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
												)






from pathlib import Path

import fire
from tqdm import tqdm



def 	UpperCamelCase   (							lowerCAmelCase__="ro"			,      lowerCAmelCase__="en"			,      lowerCAmelCase__="wmt16"			,      lowerCAmelCase__=None		):
					'''simple docstring'''
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''		)
					lowercase						     =						f'{src_lang}-{tgt_lang}'
					print(f'Converting {dataset}-{pair}'		)
					lowercase						     =						datasets.load_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					if save_dir is None:
										lowercase						     =						f'{dataset}-{pair}'
					lowercase						     =						Path(lowerCAmelCase__		)
					save_dir.mkdir(exist_ok=lowerCAmelCase__		)

					for split in ds.keys():
										print(f'Splitting {split} with {ds[split].num_rows} records'		)

										# to save to val.source, val.target like summary datasets
										lowercase						     =						'''val''' if split == '''validation''' else split
										lowercase						     =						save_dir.joinpath(f'{fn}.source'		)
										lowercase						     =						save_dir.joinpath(f'{fn}.target'		)
										lowercase						     =						src_path.open('''w+'''		)
										lowercase						     =						tgt_path.open('''w+'''		)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]		):
															lowercase						     =						x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''		)
															tgt_fp.write(ex[tgt_lang] + '''\n'''		)

					print(f'Saved {dataset} dataset to {save_dir}'		)


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




