





import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
 assert isinstance(__a   , __a   )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'   , [False, True]   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			Optional[int]											=	tmp_path / 'cache'
 snake_case_		:			Any											=	{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  snake_case_		:			Union[str, Any]											=	TextDatasetReader(__a   , cache_dir=__a   , keep_in_memory=__a   ).read()
 _check_text_dataset(__a   , __a   )


@pytest.mark.parametrize(
    'features'   , [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			Any											=	tmp_path / 'cache'
 snake_case_		:			Any											=	{'text': 'string'}
 snake_case_		:			Tuple											=	features.copy() if features else default_expected_features
 snake_case_		:			Optional[int]											=	(
     Features({feature: Value(__a   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 snake_case_		:			Optional[int]											=	TextDatasetReader(__a   , features=__a   , cache_dir=__a   ).read()
 _check_text_dataset(__a   , __a   )


@pytest.mark.parametrize('split'   , [None, NamedSplit('train'   ), 'train', 'test']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			int											=	tmp_path / 'cache'
 snake_case_		:			Any											=	{'text': 'string'}
 snake_case_		:			Any											=	TextDatasetReader(__a   , cache_dir=__a   , split=__a   ).read()
 _check_text_dataset(__a   , __a   )
 assert dataset.split == split if split else "train"


@pytest.mark.parametrize('path_type'   , [str, list]   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):







 if issubclass(__a   , __a   ):
  snake_case_		:			Union[str, Any]											=	text_path
 elif issubclass(__a   , __a   ):
  snake_case_		:			List[Any]											=	[text_path]
 snake_case_		:			str											=	tmp_path / 'cache'
 snake_case_		:			List[Any]											=	{'text': 'string'}
 snake_case_		:			Dict											=	TextDatasetReader(__a   , cache_dir=__a   ).read()
 _check_text_dataset(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a=("train",)   ):
 assert isinstance(__a   , __a   )
 for split in splits:
  snake_case_		:			Any											=	dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize('keep_in_memory'   , [False, True]   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			List[Any]											=	tmp_path / 'cache'
 snake_case_		:			List[str]											=	{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  snake_case_		:			Any											=	TextDatasetReader({'train': text_path}   , cache_dir=__a   , keep_in_memory=__a   ).read()
 _check_text_datasetdict(__a   , __a   )


@pytest.mark.parametrize(
    'features'   , [
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]   , )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			Optional[Any]											=	tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 snake_case_		:			str											=	{'text': 'string'}
 snake_case_		:			List[str]											=	features.copy() if features else default_expected_features
 snake_case_		:			List[str]											=	(
     Features({feature: Value(__a   ) for feature, dtype in features.items()}   ) if features is not None else None
 )
 snake_case_		:			List[str]											=	TextDatasetReader({'train': text_path}   , features=__a   , cache_dir=__a   ).read()
 _check_text_datasetdict(__a   , __a   )


@pytest.mark.parametrize('split'   , [None, NamedSplit('train'   ), 'train', 'test']   )
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):







 if split:
  snake_case_		:			List[Any]											=	{split: text_path}
 else:
  snake_case_		:			int											=	'train'
  snake_case_		:			Any											=	{'train': text_path, 'test': text_path}
 snake_case_		:			Any											=	tmp_path / 'cache'
 snake_case_		:			List[Any]											=	{'text': 'string'}
 snake_case_		:			Dict											=	TextDatasetReader(__a   , cache_dir=__a   ).read()
 _check_text_datasetdict(__a   , __a   , splits=list(path.keys()   )   )
 assert all(dataset[split].split == split for split in path.keys()   )








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


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

