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


__UpperCamelCase		:       List[Any]									=     [
    {'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       A	(    _lowercase=True						):
						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=UpperCamelCase_))
class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		None
						UpperCamelCase_     =		None


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												with TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							dataset_module_factory(UpperCamelCase__    ,		cache_dir=UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							import_main_class(dataset_module.module_path    ,		dataset=UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    DatasetBuilder							=							builder_cls(
																		    cache_dir=UpperCamelCase__    ,		config_name=UpperCamelCase__    ,		hash=dataset_module.hash    ,		)

																		SCREAMING_SNAKE_CASE							:    List[str]							=							'''/'''.join(
																		    [
																		        HF_GCP_BASE_URL,
																		        builder_instance._relative_data_dir(with_hash=UpperCamelCase__						).replace(os.sep    ,		'''/'''						),
																		        config.DATASET_INFO_FILENAME,
																		    ]						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							cached_path(UpperCamelCase__    ,		cache_dir=UpperCamelCase__						)
																		self.assertTrue(os.path.exists(UpperCamelCase__						)						)






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



@pytest.mark.integration
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							dataset_module_factory('''wikipedia'''		,  cache_dir=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Dict							=							import_main_class(dataset_module.module_path		,  dataset=_lowercase						)
						SCREAMING_SNAKE_CASE							:    DatasetBuilder							=							builder_cls(
						    cache_dir=_lowercase		,  config_name='''20220301.frr'''		,  hash=dataset_module.hash		,  )
						SCREAMING_SNAKE_CASE							:    List[Any]							=							builder_instance.as_streaming_dataset()
						assert ds
						assert isinstance(_lowercase		,  _lowercase						)
						assert "train" in ds
						assert isinstance(ds['''train''']		,  _lowercase						)
						assert next(iter(ds['''train''']						)						)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





