






"""simple docstring"""







import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      assert isinstance(a__				,	a__							)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""				,	[False, True]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase          =	JsonDatasetReader(a__				,	cache_dir=a__				,	keep_in_memory=a__							).read()
      _check_json_dataset(a__				,	a__							)

@pytest.mark.parametrize(
    """features"""				,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]				,	)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCAmelCase          =	features.copy() if features else default_expected_features
      UpperCAmelCase          =	(
          Features({feature: Value(a__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      UpperCAmelCase          =	JsonDatasetReader(a__				,	features=a__				,	cache_dir=a__							).read()
      _check_json_dataset(a__				,	a__							)

@pytest.mark.parametrize(
    """features"""				,	[
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]				,	)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
      UpperCAmelCase          =	features.copy() if features else default_expected_features
      UpperCAmelCase          =	(
          Features({feature: Value(a__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      UpperCAmelCase          =	JsonDatasetReader(a__				,	features=a__				,	cache_dir=a__							).read()
      assert isinstance(a__				,	a__							)
      assert dataset.num_rows == 2
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_3", "col_1", "col_2"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
      UpperCAmelCase          =	features.copy()
      UpperCAmelCase          =	(
          Features({feature: Value(a__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	JsonDatasetReader(a__				,	features=a__				,	cache_dir=a__							).read()
      assert isinstance(a__				,	a__							)
      assert dataset.num_rows == 2
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_2", "col_3", "col_1"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""split"""				,	[None, NamedSplit("""train"""							), """train""", """test"""]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCAmelCase          =	JsonDatasetReader(a__				,	cache_dir=a__				,	split=a__							).read()
      _check_json_dataset(a__				,	a__							)
      assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""				,	[str, list]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if issubclass(a__				,	a__							):
            UpperCAmelCase          =	jsonl_path
      elif issubclass(a__				,	a__							):
            UpperCAmelCase          =	[jsonl_path]
      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCAmelCase          =	JsonDatasetReader(a__				,	cache_dir=a__							).read()
      _check_json_dataset(a__				,	a__							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case=("train",)							):







      """simple docstring"""






      assert isinstance(a__				,	a__							)
      for split in splits:
            UpperCAmelCase          =	dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 3
            assert dataset.column_names == ["col_1", "col_2", "col_3"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""				,	[False, True]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase          =	JsonDatasetReader({"""train""": jsonl_path}				,	cache_dir=a__				,	keep_in_memory=a__							).read()
      _check_json_datasetdict(a__				,	a__							)

@pytest.mark.parametrize(
    """features"""				,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]				,	)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCAmelCase          =	features.copy() if features else default_expected_features
      UpperCAmelCase          =	(
          Features({feature: Value(a__							) for feature, dtype in features.items()}							) if features is not None else None
      )
      UpperCAmelCase          =	JsonDatasetReader({"""train""": jsonl_path}				,	features=a__				,	cache_dir=a__							).read()
      _check_json_datasetdict(a__				,	a__							)

@pytest.mark.parametrize("""split"""				,	[None, NamedSplit("""train"""							), """train""", """test"""]							)
def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      if split:
            UpperCAmelCase          =	{split: jsonl_path}
      else:
            UpperCAmelCase          =	"""train"""
            UpperCAmelCase          =	{"""train""": jsonl_path, """test""": jsonl_path}
      UpperCAmelCase          =	tmp_path / """cache"""
      UpperCAmelCase          =	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCAmelCase          =	JsonDatasetReader(a__				,	cache_dir=a__							).read()
      _check_json_datasetdict(a__				,	a__				,	splits=list(path.keys()							)							)
      assert all(dataset[split].split == split for split in path.keys()							)

def _a			(		_snake_case							):







      """simple docstring"""






      return json.load(a__							)

def _a			(		_snake_case							):







      """simple docstring"""






      return [json.loads(a__							) for line in buffer]







class  lowerCamelCase__ :






    @pytest.mark.parametrize("""lines, load_json_function"""     ,[(True, load_json_lines), (False, load_json)] )
    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          with io.BytesIO() as buffer:
                JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,lines=__lowerCAmelCase ).write()
                buffer.seek(0 )
                UpperCAmelCase          =	load_json_function(__lowerCAmelCase )
          assert isinstance(__lowerCAmelCase     ,__lowerCAmelCase )
          assert isinstance(exported_content[0]     ,__lowerCAmelCase )
          assert len(__lowerCAmelCase ) == 10






    @pytest.mark.parametrize(
        """orient, container, keys, len_at"""     ,[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789""" ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ]     ,)
    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A ):
          with io.BytesIO() as buffer:
                JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,lines=__lowerCAmelCase     ,orient=__lowerCAmelCase ).write()
                buffer.seek(0 )
                UpperCAmelCase          =	load_json(__lowerCAmelCase )
          assert isinstance(__lowerCAmelCase     ,__lowerCAmelCase )
          if keys:
                if container is dict:
                      assert exported_content.keys() == keys
                else:
                      assert exported_content[0].keys() == keys
          else:
                assert not hasattr(__lowerCAmelCase     ,"""keys""" ) and not hasattr(exported_content[0]     ,"""keys""" )
          if len_at:
                assert len(exported_content[len_at] ) == 10
          else:
                assert len(__lowerCAmelCase ) == 10






    @pytest.mark.parametrize("""lines, load_json_function"""     ,[(True, load_json_lines), (False, load_json)] )
    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          with io.BytesIO() as buffer:
                JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,lines=__lowerCAmelCase     ,num_proc=2 ).write()
                buffer.seek(0 )
                UpperCAmelCase          =	load_json_function(__lowerCAmelCase )
          assert isinstance(__lowerCAmelCase     ,__lowerCAmelCase )
          assert isinstance(exported_content[0]     ,__lowerCAmelCase )
          assert len(__lowerCAmelCase ) == 10






    @pytest.mark.parametrize(
        """orient, container, keys, len_at"""     ,[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789""" ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ]     ,)
    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A ):
          with io.BytesIO() as buffer:
                JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,lines=__lowerCAmelCase     ,orient=__lowerCAmelCase     ,num_proc=2 ).write()
                buffer.seek(0 )
                UpperCAmelCase          =	load_json(__lowerCAmelCase )
          assert isinstance(__lowerCAmelCase     ,__lowerCAmelCase )
          if keys:
                if container is dict:
                      assert exported_content.keys() == keys
                else:
                      assert exported_content[0].keys() == keys
          else:
                assert not hasattr(__lowerCAmelCase     ,"""keys""" ) and not hasattr(exported_content[0]     ,"""keys""" )
          if len_at:
                assert len(exported_content[len_at] ) == 10
          else:
                assert len(__lowerCAmelCase ) == 10






    def     _UpperCamelCase							(			self     ,A ):
          with pytest.raises(__lowerCAmelCase ):
                with io.BytesIO() as buffer:
                      JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,num_proc=0 )






    @pytest.mark.parametrize("""compression, extension"""     ,[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")] )
    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	tmp_path_factory.mktemp("""data""" ) / F'''test.json.{extension}'''
          UpperCAmelCase          =	str(shared_datadir / F'''test_file.json.{extension}''' )
          JsonDatasetWriter(__lowerCAmelCase     ,__lowerCAmelCase     ,compression=__lowerCAmelCase ).write()

          with fsspec.open(__lowerCAmelCase     ,"""rb"""     ,compression="""infer""" ) as f:
                UpperCAmelCase          =	f.read()
          with fsspec.open(__lowerCAmelCase     ,"""rb"""     ,compression="""infer""" ) as f:
                UpperCAmelCase          =	f.read()
          assert exported_content == original_content











"""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


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







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          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     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

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



