

import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


a		=[
    {"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
    {"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
    {"""dataset""": """snli""", """config_name""": """plain_text"""},
    {"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
    {"""dataset""": """wiki40b""", """config_name""": """en"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
    {"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
    {"""dataset""": """natural_questions""", """config_name""": """default"""},
]






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__=True			)  ->     Dict:
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]






@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__lowercase				)				)
class        A_		(       __lowercase				):
							_UpperCAmelCase    :  Union[str, Any]						      =      None
							_UpperCAmelCase    :  Dict						      =      None
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								with TemporaryDirectory() as tmp_dir:
									__lowerCamelCase				:				int					       = dataset_module_factory(UpperCAmelCase__   ,cache_dir=UpperCAmelCase__)

									__lowerCamelCase				:				Union[str, Any]					       = import_main_class(dataset_module.module_path   ,dataset=UpperCAmelCase__)

									__lowerCamelCase				:				Any					       = builder_cls(
									    cache_dir=UpperCAmelCase__   ,config_name=UpperCAmelCase__   ,hash=dataset_module.hash   ,)

									__lowerCamelCase				:				int					       = '/'.join(
									    [
									        HF_GCP_BASE_URL,
									        builder_instance._relative_data_dir(with_hash=UpperCAmelCase__).replace(os.sep   ,'/'),
									        config.DATASET_INFO_FILENAME,
									    ])
									__lowerCamelCase				:				Union[str, Any]					       = cached_path(UpperCAmelCase__   ,cache_dir=UpperCAmelCase__)
									self.assertTrue(os.path.exists(UpperCAmelCase__))






@pytest.mark.integration
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Dict:
	__lowerCamelCase				:				Dict					       = tmp_path_factory.mktemp('test_hf_gcp'			) / 'test_wikipedia_simple'
	__lowerCamelCase				:				Any					       = dataset_module_factory('wikipedia'	, cache_dir=lowerCamelCase__			)
	__lowerCamelCase				:				List[Any]					       = import_main_class(dataset_module.module_path			)
	__lowerCamelCase				:				List[str]					       = builder_cls(
	    cache_dir=lowerCamelCase__	, config_name='20220301.frr'	, hash=dataset_module.hash	, )
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	__lowerCamelCase				:				str					       = None
	builder_instance.download_and_prepare()
	__lowerCamelCase				:				Optional[Any]					       = builder_instance.as_dataset()
	assert ds






@pytest.mark.integration
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Any:
	__lowerCamelCase				:				Optional[Any]					       = dataset_module_factory('wikipedia'	, cache_dir=lowerCamelCase__			)
	__lowerCamelCase				:				Optional[int]					       = import_main_class(dataset_module.module_path	, dataset=lowerCamelCase__			)
	__lowerCamelCase				:				str					       = builder_cls(
	    cache_dir=lowerCamelCase__	, config_name='20220301.frr'	, hash=dataset_module.hash	, )
	__lowerCamelCase				:				Dict					       = builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(lowerCamelCase__	, lowerCamelCase__			)
	assert "train" in ds
	assert isinstance(ds['train']	, lowerCamelCase__			)
	assert next(iter(ds['train']			)			)








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





