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

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


lowercase_       :						Union[str, Any]     					=	logging.get_logger(__name__)

lowercase_       :						List[Any]     					=	{
    '''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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""informer"""
     A__      =			{
         """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__ = None  ,     snake_case__ = "mean"  ,     snake_case__ = 0  ,     snake_case__ = 0  ,     snake_case__ = 0  ,     snake_case__ = 0  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = 64  ,     snake_case__ = 32  ,     snake_case__ = 32  ,     snake_case__ = 2  ,     snake_case__ = 2  ,     snake_case__ = 2  ,     snake_case__ = 2  ,     snake_case__ = True  ,     snake_case__ = "gelu"  ,     snake_case__ = 0.05  ,     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__ = "prob"  ,     snake_case__ = 5  ,     snake_case__ = True  ,     **snake_case__  ,     ):




       """simple docstring"""


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

       # set cardinality
       if cardinality 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`"				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = cardinality
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [0]

       # set embedding_dimension
       if embedding_dimension 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`"				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = embedding_dimension
       else:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = [min(50  ,     (cat + 1) // 2				) for cat in self.cardinality]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = num_parallel_samples

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

       _SCREAMING_SNAKE_CASE   :				int							    = dropout
       _SCREAMING_SNAKE_CASE   :				str							    = attention_dropout
       _SCREAMING_SNAKE_CASE   :				List[str]							    = activation_dropout
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = encoder_layerdrop
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = decoder_layerdrop

       _SCREAMING_SNAKE_CASE   :				Dict							    = activation_function
       _SCREAMING_SNAKE_CASE   :				int							    = init_std

       _SCREAMING_SNAKE_CASE   :				Any							    = use_cache

       # Informer
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = attention_type
       _SCREAMING_SNAKE_CASE   :				Dict							    = sampling_factor
       _SCREAMING_SNAKE_CASE   :				Tuple							    = distil

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




     @property
     def 				__SCREAMING_SNAKE_CASE							(     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
       )





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

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

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

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





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





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



