


# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

UpperCAmelCase    =					"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
   raise ImportWarning(
       """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
   )

if version.parse(pyarrow.__version__).major < 8:
   raise ImportWarning(
       """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
       """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
   )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

UpperCAmelCase    =					concatenate_datasets
UpperCAmelCase    =					DownloadConfig
UpperCAmelCase    =					DownloadManager
UpperCAmelCase    =					DownloadMode
UpperCAmelCase    =					DownloadConfig
UpperCAmelCase    =					DownloadMode
UpperCAmelCase    =					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



from typing import List, Optional, Union

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """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 lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "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 = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       if cardinality 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`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       if embedding_dimension 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`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

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

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

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





 @property
 def 		UpperCamelCase__  (			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
       )