

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   __UpperCAmelCase					(      __a :     List[Any]=True      )     ->	str:

					"""simple docstring"""

					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 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       None
							UpperCAmelCase__			:		List[str]             =       None


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Optional[Any]:
												with TemporaryDirectory() as tmp_dir:
																	_a      :       Dict   =						dataset_module_factory(_a			,				cache_dir=_a			)

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

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

																	_a      :       int   =						'''/'''.join(
																	    [
																	        HF_GCP_BASE_URL,
																	        builder_instance._relative_data_dir(with_hash=_a			).replace(os.sep			,				'''/'''			),
																	        config.DATASET_INFO_FILENAME,
																	    ]			)
																	_a      :       Optional[int]   =						cached_path(_a			,				cache_dir=_a			)
																	self.assertTrue(os.path.exists(_a			)			)



@pytest.mark.integration
def   __UpperCAmelCase					(      __a :     Optional[int]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						tmp_path_factory.mktemp('''test_hf_gcp'''      ) / '''test_wikipedia_simple'''
					_a      :       List[str]   =						dataset_module_factory('''wikipedia'''						,cache_dir=__a      )
					_a      :       Tuple   =						import_main_class(dataset_module.module_path      )
					_a      :       DatasetBuilder   =						builder_cls(
					    cache_dir=__a						,config_name='''20220301.frr'''						,hash=dataset_module.hash						,)
					# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
					_a      :       int   =						None
					builder_instance.download_and_prepare()
					_a      :       Tuple   =						builder_instance.as_dataset()
					assert ds







@pytest.mark.integration
def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						dataset_module_factory('''wikipedia'''						,cache_dir=__a      )
					_a      :       Optional[int]   =						import_main_class(dataset_module.module_path						,dataset=__a      )
					_a      :       DatasetBuilder   =						builder_cls(
					    cache_dir=__a						,config_name='''20220301.frr'''						,hash=dataset_module.hash						,)
					_a      :       Dict   =						builder_instance.as_streaming_dataset()
					assert ds
					assert isinstance(__a						,__a      )
					assert "train" in ds
					assert isinstance(ds['''train''']						,__a      )
					assert next(iter(ds['''train''']      )      )








import os
import sys
import unittest


a_								=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a_								=       os.path.join(git_repo_path, """src""", """transformers""")

a_								=       """
{0} = None
"""

a_								=       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


a_								=       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''					)
    self.assertIsNone(__UpperCAmelCase					)

    __lowerCamelCase    				=     find_backend('''    if not is_tokenizers_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tokenizers'''					)

    __lowerCamelCase    				=     find_backend('''    if not is_tensorflow_text_available():'''					)
    self.assertEqual(__UpperCAmelCase						,					'''tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tensorflow_text'''					)

    __lowerCamelCase    				=     find_backend(
        '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''					)
    self.assertEqual(__UpperCAmelCase						,					'''sentencepiece_and_tokenizers_and_vision'''					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     read_init()
    # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
    self.assertIn('''torch'''						,					__UpperCAmelCase					)
    self.assertIn('''tensorflow_text'''						,					__UpperCAmelCase					)
    self.assertIn('''sentencepiece_and_tokenizers'''						,					__UpperCAmelCase					)

    # Likewise, we can't assert on the exact content of a key
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertModel'''						,					objects['''tf''']					)
    self.assertIn('''FlaxBertModel'''						,					objects['''flax''']					)
    self.assertIn('''BertModel'''						,					objects['''torch''']					)
    self.assertIn('''TFBertTokenizer'''						,					objects['''tensorflow_text''']					)
    self.assertIn('''convert_slow_tokenizer'''						,					objects['''sentencepiece_and_tokenizers''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     create_dummy_object('''CONSTANT'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					'''\nCONSTANT = None\n'''					)

    __lowerCamelCase    				=     create_dummy_object('''function'''						,					'''\'torch\''''					)
    self.assertEqual(
        __UpperCAmelCase						,					'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''					)

    __lowerCamelCase    				=     '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
    __lowerCamelCase    				=     create_dummy_object('''FakeClass'''						,					'''\'torch\''''					)
    self.assertEqual(__UpperCAmelCase						,					__UpperCAmelCase					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
    __lowerCamelCase    				=     create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}					)
    self.assertEqual(dummy_files['''torch''']						,					__UpperCAmelCase					)






