






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


SCREAMING_SNAKE_CASE__		:							str 				=				[
    {'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						(						__lowerCamelCase=True       ) ->			Union[str, Any]:

     '''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		):
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  None




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):



          with TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__      :  Dict				      =			dataset_module_factory(__SCREAMING_SNAKE_CASE	,						cache_dir=__SCREAMING_SNAKE_CASE		)

               UpperCAmelCase__      :  Tuple				      =			import_main_class(dataset_module.module_path	,						dataset=__SCREAMING_SNAKE_CASE		)

               UpperCAmelCase__      :  Tuple				      =			builder_cls(
                   cache_dir=__SCREAMING_SNAKE_CASE	,						config_name=__SCREAMING_SNAKE_CASE	,						hash=dataset_module.hash	,						)

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





@pytest.mark.integration
def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[Any]				      =			tmp_path_factory.mktemp("""test_hf_gcp"""       ) / """test_wikipedia_simple"""
     UpperCAmelCase__      :  List[str]				      =			dataset_module_factory("""wikipedia""" ,							cache_dir=__lowerCamelCase       )
     UpperCAmelCase__      :  List[str]				      =			import_main_class(dataset_module.module_path       )
     UpperCAmelCase__      :  int				      =			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
     UpperCAmelCase__      :  Dict				      =			None
     builder_instance.download_and_prepare()
     UpperCAmelCase__      :  Optional[int]				      =			builder_instance.as_dataset()
     assert ds







@pytest.mark.integration
def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[Any]				      =			dataset_module_factory("""wikipedia""" ,							cache_dir=__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[Any]				      =			import_main_class(dataset_module.module_path ,							dataset=__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[Any]				      =			builder_cls(
         cache_dir=__lowerCamelCase ,							config_name="""20220301.frr""" ,							hash=dataset_module.hash ,							)
     UpperCAmelCase__      :  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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


