import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(__UpperCamelCase     ,      cache_dir=__UpperCamelCase     ,      keep_in_memory=__UpperCamelCase				).read()
      _check_parquet_dataset(__UpperCamelCase     ,      __UpperCamelCase				)


@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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_          =  (
          Features({feature: Value(__UpperCamelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(__UpperCamelCase     ,      features=__UpperCamelCase     ,      cache_dir=__UpperCamelCase				).read()
      _check_parquet_dataset(__UpperCamelCase     ,      __UpperCamelCase				)


@pytest.mark.parametrize("split"     ,      [None, NamedSplit("train"				), "train", "test"]				)
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(__UpperCamelCase     ,      cache_dir=__UpperCamelCase     ,      split=__UpperCamelCase				).read()
      _check_parquet_dataset(__UpperCamelCase     ,      __UpperCamelCase				)
      assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"     ,      [str, list]				)
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      if issubclass(__UpperCamelCase     ,      __UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  parquet_path
      elif issubclass(__UpperCamelCase     ,      __UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  [parquet_path]
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(__UpperCamelCase     ,      cache_dir=__UpperCamelCase				).read()
      _check_parquet_dataset(__UpperCamelCase     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=("train",)				):
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      for split in splits:
            SCREAMING_SNAKE_CASE_          =  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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(
                {"train": parquet_path}     ,      cache_dir=__UpperCamelCase     ,      keep_in_memory=__UpperCamelCase				).read()
      _check_parquet_datasetdict(__UpperCamelCase     ,      __UpperCamelCase				)


@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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_          =  (
          Features({feature: Value(__UpperCamelCase				) for feature, dtype in features.items()}				) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader({"train": parquet_path}     ,      features=__UpperCamelCase     ,      cache_dir=__UpperCamelCase				).read()
      _check_parquet_datasetdict(__UpperCamelCase     ,      __UpperCamelCase				)


@pytest.mark.parametrize("split"     ,      [None, NamedSplit("train"				), "train", "test"]				)
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      if split:
            SCREAMING_SNAKE_CASE_          =  {split: parquet_path}
      else:
            SCREAMING_SNAKE_CASE_          =  "train"
            SCREAMING_SNAKE_CASE_          =  {"train": parquet_path, "test": parquet_path}
      SCREAMING_SNAKE_CASE_          =  tmp_path / "cache"
      SCREAMING_SNAKE_CASE_          =  {"col_1": "string", "col_2": "int64", "col_3": "float64"}
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(__UpperCamelCase     ,      cache_dir=__UpperCamelCase				).read()
      _check_parquet_datasetdict(__UpperCamelCase     ,      __UpperCamelCase     ,      splits=list(path.keys()				)				)
      assert all(dataset[split].split == split for split in path.keys()				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetWriter(__UpperCamelCase     ,      tmp_path / "foo.parquet"				)
      assert writer.write() > 0
      SCREAMING_SNAKE_CASE_          =  pq.ParquetFile(tmp_path / "foo.parquet"				)
      SCREAMING_SNAKE_CASE_          =  pf.read()
      assert dataset.data.table == output_table


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  str(shared_datadir / "test_image_rgb.jpg"				)
      SCREAMING_SNAKE_CASE_          =  {"image": [image_path]}
      SCREAMING_SNAKE_CASE_          =  Features({"image": Image()}				)
      SCREAMING_SNAKE_CASE_          =  Dataset.from_dict(__UpperCamelCase     ,      features=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  ParquetDatasetWriter(__UpperCamelCase     ,      tmp_path / "foo.parquet"				)
      assert writer.write() > 0

      SCREAMING_SNAKE_CASE_          =  Dataset.from_parquet(str(tmp_path / "foo.parquet"				)				)
      assert dataset.features == reloaded_dataset.features

      SCREAMING_SNAKE_CASE_          =  ParquetDatasetReader(str(tmp_path / "foo.parquet"				)     ,      streaming=__UpperCamelCase				).read()
      assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"     ,      [
        (Features({"foo": Value("int32"				)}				), None),
        (Features({"image": Image(), "foo": Value("int32"				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()				)}				), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,      )
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      assert get_writer_batch_size(__UpperCamelCase				) == expected



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


