





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      lowerCAmelCase_ (       __a=True		)     ->		int:







  """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=__SCREAMING_SNAKE_CASE    )    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			None
 lowercase_		=			None

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   with TemporaryDirectory() as tmp_dir:
     lowerCamelCase__:      List[str]			  =dataset_module_factory(UpperCAmelCase_       , cache_dir=UpperCAmelCase_)

     lowerCamelCase__:      List[Any]			  =import_main_class(dataset_module.module_path       , dataset=UpperCAmelCase_)

     lowerCamelCase__:      DatasetBuilder			  =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__:      str			  =cached_path(UpperCAmelCase_       , cache_dir=UpperCAmelCase_)
     self.assertTrue(os.path.exists(UpperCAmelCase_))





@pytest.mark.integration
def      lowerCAmelCase_ (       __a		)     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      int			  =tmp_path_factory.mktemp("test_hf_gcp"		) / "test_wikipedia_simple"
  lowerCamelCase__:      List[str]			  =dataset_module_factory("wikipedia"	,    cache_dir=__a		)
  lowerCamelCase__:      Optional[Any]			  =import_main_class(dataset_module.module_path		)
  lowerCamelCase__:      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
  lowerCamelCase__:      Union[str, Any]			  =None
  builder_instance.download_and_prepare()
  lowerCamelCase__:      int			  =builder_instance.as_dataset()
  assert ds






@pytest.mark.integration
def      lowerCAmelCase_ (       __a		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      int			  =dataset_module_factory("wikipedia"	,    cache_dir=__a		)
  lowerCamelCase__:      Dict			  =import_main_class(dataset_module.module_path	,    dataset=__a		)
  lowerCamelCase__:      DatasetBuilder			  =builder_cls(
      cache_dir=__a	,    config_name="20220301.frr"	,    hash=dataset_module.hash	,    )
  lowerCamelCase__:      Tuple			  =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"]		)		)







"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



