"""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


_A   					=    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       )							->   Optional[Any]:
				return (abs(source - target       ) / target) < 0.01




@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				_TestCommandArgs(dataset=__UpperCAmelCase  ,					all_configs=__UpperCAmelCase  ,					save_infos=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				TestCommand(*__UpperCAmelCase       )
				test_command.run()
				SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					"README.md"       )
				assert os.path.exists(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				DatasetInfosDict.from_directory(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				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_351_563,
				                    "num_examples": 10_000,
				                },
				                {
				                    "name": "validation",
				                    "num_bytes": 238_418,
				                    "num_examples": 1_000,
				                },
				            ]  ,					download_size=3_940_680  ,					dataset_size=2_589_981  ,					)
				    }       )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				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





"""simple docstring"""






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_A   					=    HUGGINGFACE_HUB_CACHE


_A   					=    'config.json'
_A   					=    'diffusion_pytorch_model.bin'
_A   					=    'diffusion_flax_model.msgpack'
_A   					=    'model.onnx'
_A   					=    'diffusion_pytorch_model.safetensors'
_A   					=    'weights.pb'
_A   					=    'https://huggingface.co'
_A   					=    default_cache_path
_A   					=    'diffusers_modules'
_A   					=    os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A   					=    ['fp16', 'non-ema']
_A   					=    '.self_attn'




