





"""simple docstring"""






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def     __A      (			a_						:Optional[Any]   ,       a_						:List[str]   ,       a_						:Union[str, Any]   ,       a_						:Union[str, Any]=10_24)		->		Any:
				__a       ,						__a :						Optional[int]			       =  [], []

				__a :						Union[str, Any]			       =  list(zip(a_   ,       a_))
				__a       ,						__a :						Any			       =  sorted_examples[0]

				def is_too_big(a_						:List[Any]):
								return tok(a_   ,       return_tensors='''pt''').input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]):
								__a :						List[str]			       =  new_src + ''' ''' + src
								__a :						List[str]			       =  new_tgt + ''' ''' + tgt
								if is_too_big(a_) or is_too_big(a_):  # cant fit, finalize example
												finished_src.append(a_)
												finished_tgt.append(a_)
												__a       ,						__a :						Tuple			       =  src, tgt
								else:  # can fit, keep adding
												__a       ,						__a :						str			       =  cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(a_)
								finished_tgt.append(a_)
				return finished_src, finished_tgt

def     __A      (			a_						:List[Any]   ,       a_						:Path   ,       a_						:Tuple   ,       a_						:Optional[Any])		->		List[str]:
				__a :						str			       =  Path(a_)
				save_path.mkdir(exist_ok=a_)
				for split in ["train"]:
								__a       ,						__a :						Optional[Any]			       =  data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__a :						Optional[Any]			       =  [x.rstrip() for x in Path(a_).open().readlines()]
								__a :						Dict			       =  [x.rstrip() for x in Path(a_).open().readlines()]
								__a       ,						__a :						Dict			       =  pack_examples(a_   ,       a_   ,       a_   ,       a_)
								print(F"""packed {split} split from {len(a_)} examples -> {len(a_)}.""")
								Path(save_path / F"""{split}.source""").open('''w''').write('''\n'''.join(a_))
								Path(save_path / F"""{split}.target""").open('''w''').write('''\n'''.join(a_))
				for split in ["val", "test"]:
								__a       ,						__a :						Optional[int]			       =  data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(a_   ,       save_path / F"""{split}.source""")
								shutil.copyfile(a_   ,       save_path / F"""{split}.target""")

def     __A      (			)		->		int:
				__a :						Union[str, Any]			       =  argparse.ArgumentParser()
				parser.add_argument('''--tok_name'''   ,       type=a_   ,       help='''like facebook/bart-large-cnn,t5-base, etc.''')
				parser.add_argument('''--max_seq_len'''   ,       type=a_   ,       default=1_28)
				parser.add_argument('''--data_dir'''   ,       type=a_)
				parser.add_argument('''--save_path'''   ,       type=a_)
				__a :						str			       =  parser.parse_args()
				__a :						List[Any]			       =  AutoTokenizer.from_pretrained(args.tok_name)
				return pack_data_dir(a_   ,       Path(args.data_dir)   ,       args.max_seq_len   ,       args.save_path)


if __name__ == "__main__":
		packer_cli()






"""simple docstring"""






from typing import List, Optional

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


A 			=  logging.get_logger(__name__)

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




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''autoformer'''
					__lowerCAmelCase  =  {
					    '''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 = True	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 64	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = "gelu"	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 100	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = True	,			_UpperCAmelCase=True	,			_UpperCAmelCase = 10	,			_UpperCAmelCase = 25	,			_UpperCAmelCase = 3	,			**_UpperCAmelCase	,			):
									# time series specific configuration
									__a :						Optional[int]			       =  prediction_length
									__a :						Optional[int]			       =  context_length if context_length is not None else prediction_length
									__a :						Union[str, Any]			       =  distribution_output
									__a :						str			       =  loss
									__a :						Optional[Any]			       =  input_size
									__a :						str			       =  num_time_features
									__a :						Optional[Any]			       =  lags_sequence
									__a :						Any			       =  scaling
									__a :						Optional[Any]			       =  num_dynamic_real_features
									__a :						str			       =  num_static_real_features
									__a :						Any			       =  num_static_categorical_features
									if cardinality is not None 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 :						List[str]			       =  cardinality
									else:
													__a :						Any			       =  [0]
									if embedding_dimension is not None 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 :						Any			       =  embedding_dimension
									else:
													__a :						Tuple			       =  [min(50	,			(cat + 1) // 2     ) for cat in self.cardinality]
									__a :						Union[str, Any]			       =  num_parallel_samples

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

									__a :						Union[str, Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						Optional[Any]			       =  activation_dropout
									__a :						List[str]			       =  encoder_layerdrop
									__a :						Any			       =  decoder_layerdrop

									__a :						Union[str, Any]			       =  activation_function
									__a :						Union[str, Any]			       =  init_std

									__a :						List[str]			       =  use_cache

									# Autoformer
									__a :						int			       =  label_length
									__a :						List[Any]			       =  moving_average
									__a :						str			       =  autocorrelation_factor

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







					@property
					def  _lowerCamelCase		(						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
									)