


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


_SCREAMING_SNAKE_CASE     :			int        =							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 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 return (abs(source - target      ) / target) < 0.01





@pytest.mark.integration
def 							__lowerCAmelCase      (			__magic_name__      ):
 _lowercase:       Union[str, Any]						=				_TestCommandArgs(dataset=lowerCAmelCase__ ,   all_configs=lowerCAmelCase__ ,   save_infos=lowerCAmelCase__      )
 _lowercase:       Tuple						=				TestCommand(*lowerCAmelCase__      )
 test_command.run()
 _lowercase:       Optional[int]						=				os.path.join(lowerCAmelCase__ ,   "README.md"      )
 assert os.path.exists(lowerCAmelCase__      )
 _lowercase:       List[Any]						=				DatasetInfosDict.from_directory(lowerCAmelCase__      )
 _lowercase:       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": 2_3_5_1_5_6_3,
                     "num_examples": 1_0_0_0_0,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 2_3_8_4_1_8,
                     "num_examples": 1_0_0_0,
                 },
             ] ,   download_size=3_9_4_0_6_8_0 ,   dataset_size=2_5_8_9_9_8_1 ,   )
     }      )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  _lowercase					,	_lowercase:       List[str]						=				getattr(dataset_infos["default"] ,   lowerCAmelCase__      ), getattr(expected_dataset_infos["default"] ,   lowerCAmelCase__      )
  if key == "num_bytes":
   assert is_apercent_close(lowerCAmelCase__ ,   lowerCAmelCase__      )
  elif key == "splits":
   assert list(lowerCAmelCase__      ) == list(lowerCAmelCase__      )
   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



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



