"""simple docstring"""
from typing import List, Optional, Union

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


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""informer"""
      a__        =		{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(						self    ,			lowercase = None    ,			lowercase = None    ,			lowercase = "student_t"    ,			lowercase = "nll"    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = "mean"    ,			lowercase = 0    ,			lowercase = 0    ,			lowercase = 0    ,			lowercase = 0    ,			lowercase = None    ,			lowercase = None    ,			lowercase = 64    ,			lowercase = 32    ,			lowercase = 32    ,			lowercase = 2    ,			lowercase = 2    ,			lowercase = 2    ,			lowercase = 2    ,			lowercase = True    ,			lowercase = "gelu"    ,			lowercase = 0.05    ,			lowercase = 0.1    ,			lowercase = 0.1    ,			lowercase = 0.1    ,			lowercase = 0.1    ,			lowercase = 1_00    ,			lowercase = 0.02    ,			lowercase=True    ,			lowercase = "prob"    ,			lowercase = 5    ,			lowercase = True    ,			**lowercase    ,			)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	prediction_length
        a__:     int										=	context_length or prediction_length
        a__:     Optional[int]										=	distribution_output
        a__:     int										=	loss
        a__:     Dict										=	input_size
        a__:     Any										=	num_time_features
        a__:     Dict										=	lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        a__:     Union[str, Any]										=	scaling
        a__:     List[Any]										=	num_dynamic_real_features
        a__:     Any										=	num_static_real_features
        a__:     Tuple										=	num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(lowerCamelCase__) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`')
          a__:     int										=	cardinality
        else:
          a__:     List[Any]										=	[0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(lowerCamelCase__) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
          a__:     Dict										=	embedding_dimension
        else:
          a__:     Union[str, Any]										=	[min(50    ,			(cat + 1) // 2) for cat in self.cardinality]

        a__:     List[Any]										=	num_parallel_samples

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

        a__:     Optional[Any]										=	dropout
        a__:     Union[str, Any]										=	attention_dropout
        a__:     Union[str, Any]										=	activation_dropout
        a__:     Union[str, Any]										=	encoder_layerdrop
        a__:     int										=	decoder_layerdrop

        a__:     Optional[int]										=	activation_function
        a__:     List[Any]										=	init_std

        a__:     Any										=	use_cache

        # Informer
        a__:     Any										=	attention_type
        a__:     Any										=	sampling_factor
        a__:     Optional[int]										=	distil

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



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''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
        )








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

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

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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


