


"""simple docstring"""







import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict=1_0_2_4    ):


		'''simple docstring'''







		lowerCAmelCase					:					Any								=							[], []

		lowerCAmelCase					:					List[Any]								=							list(zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )    )
		lowerCAmelCase					:					Optional[int]								=							sorted_examples[0]

		def is_too_big(SCREAMING_SNAKE_CASE   :   int    ):
				return tok(SCREAMING_SNAKE_CASE    ,				return_tensors="pt"    ).input_ids.shape[1] > max_tokens

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

				# cleanup
		if new_src:
				assert new_tgt
				finished_src.append(SCREAMING_SNAKE_CASE    )
				finished_tgt.append(SCREAMING_SNAKE_CASE    )
		return finished_src, finished_tgt
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Path    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   int    ):


		'''simple docstring'''







		lowerCAmelCase					:					Union[str, Any]								=							Path(SCREAMING_SNAKE_CASE    )
		save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE    )
		for split in ["train"]:
				lowerCAmelCase					:					List[Any]								=							data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
				lowerCAmelCase					:					List[str]								=							[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE    ).open().readlines()]
				lowerCAmelCase					:					int								=							[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE    ).open().readlines()]
				lowerCAmelCase					:					List[str]								=							pack_examples(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
				print(f"""packed {split} split from {len(SCREAMING_SNAKE_CASE    )} examples -> {len(SCREAMING_SNAKE_CASE    )}."""    )
				Path(save_path / f"""{split}.source"""    ).open("w"    ).write("\n".join(SCREAMING_SNAKE_CASE    )    )
				Path(save_path / f"""{split}.target"""    ).open("w"    ).write("\n".join(SCREAMING_SNAKE_CASE    )    )
		for split in ["val", "test"]:
				lowerCAmelCase					:					Union[str, Any]								=							data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
				shutil.copyfile(SCREAMING_SNAKE_CASE    ,				save_path / f"""{split}.source"""    )
				shutil.copyfile(SCREAMING_SNAKE_CASE    ,				save_path / f"""{split}.target"""    )
def  a__      (    ):


		'''simple docstring'''







		lowerCAmelCase					:					List[str]								=							argparse.ArgumentParser()
		parser.add_argument("--tok_name"    ,				type=SCREAMING_SNAKE_CASE    ,				help="like facebook/bart-large-cnn,t5-base, etc."    )
		parser.add_argument("--max_seq_len"    ,				type=SCREAMING_SNAKE_CASE    ,				default=1_2_8    )
		parser.add_argument("--data_dir"    ,				type=SCREAMING_SNAKE_CASE    )
		parser.add_argument("--save_path"    ,				type=SCREAMING_SNAKE_CASE    )
		lowerCAmelCase					:					str								=							parser.parse_args()
		lowerCAmelCase					:					str								=							AutoTokenizer.from_pretrained(args.tok_name    )
		return pack_data_dir(SCREAMING_SNAKE_CASE    ,				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


lowerCAmelCase__				    =       logging.get_logger(__name__)

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


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Dict       ="autoformer"
       a  :    Dict       ={
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }

       def __init__(						self		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = "student_t"		,					snake_case__ = "nll"		,					snake_case__ = 1		,					snake_case__ = [1, 2, 3, 4, 5, 6, 7]		,					snake_case__ = True		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = 0		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = 64		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 2		,					snake_case__ = 32		,					snake_case__ = 32		,					snake_case__ = "gelu"		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 0.1		,					snake_case__ = 100		,					snake_case__ = 0.02		,					snake_case__ = True		,					snake_case__=True		,					snake_case__ = 10		,					snake_case__ = 25		,					snake_case__ = 3		,					**snake_case__		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							prediction_length
         lowerCAmelCase					:					Dict								=							context_length if context_length is not None else prediction_length
         lowerCAmelCase					:					Tuple								=							distribution_output
         lowerCAmelCase					:					List[Any]								=							loss
         lowerCAmelCase					:					int								=							input_size
         lowerCAmelCase					:					str								=							num_time_features
         lowerCAmelCase					:					str								=							lags_sequence
         lowerCAmelCase					:					List[str]								=							scaling
         lowerCAmelCase					:					List[Any]								=							num_dynamic_real_features
         lowerCAmelCase					:					Tuple								=							num_static_real_features
         lowerCAmelCase					:					Dict								=							num_static_categorical_features
         if cardinality is not None and num_static_categorical_features > 0:
           if len(snake_case__						) != num_static_categorical_features:
             raise ValueError(
                 "The cardinality should be a list of the same length as `num_static_categorical_features`"						)
           lowerCAmelCase					:					Any								=							cardinality
         else:
           lowerCAmelCase					:					Union[str, Any]								=							[0]
         if embedding_dimension is not None and num_static_categorical_features > 0:
           if len(snake_case__						) != num_static_categorical_features:
             raise ValueError(
                 "The embedding dimension should be a list of the same length as `num_static_categorical_features`"						)
           lowerCAmelCase					:					Tuple								=							embedding_dimension
         else:
           lowerCAmelCase					:					Any								=							[min(50		,					(cat + 1) // 2						) for cat in self.cardinality]
         lowerCAmelCase					:					Any								=							num_parallel_samples

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

         lowerCAmelCase					:					List[Any]								=							dropout
         lowerCAmelCase					:					Optional[int]								=							attention_dropout
         lowerCAmelCase					:					Union[str, Any]								=							activation_dropout
         lowerCAmelCase					:					Optional[int]								=							encoder_layerdrop
         lowerCAmelCase					:					Dict								=							decoder_layerdrop

         lowerCAmelCase					:					Tuple								=							activation_function
         lowerCAmelCase					:					Optional[Any]								=							init_std

         lowerCAmelCase					:					List[Any]								=							use_cache

         # Autoformer
         lowerCAmelCase					:					Any								=							label_length
         lowerCAmelCase					:					Any								=							moving_average
         lowerCAmelCase					:					Optional[Any]								=							autocorrelation_factor

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







       @property
       def 	lowercase__							(						self						):






         """simple docstring"""





         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
         )





