






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ =			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''InformerForPrediction''',
     '''InformerModel''',
     '''InformerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_informer import (
      INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      InformerForPrediction,
      InformerModel,
      InformerPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




