






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 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 assert isinstance(_lowerCamelCase				,  _lowerCamelCase					)
 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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  tmp_path / """cache"""
 __lowercase             =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase             =  ParquetDatasetReader(_lowerCamelCase				,  cache_dir=_lowerCamelCase				,  keep_in_memory=_lowerCamelCase					).read()
 _check_parquet_dataset(_lowerCamelCase				,  _lowerCamelCase					)





@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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







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





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




 '''simple docstring'''







 __lowercase             =  tmp_path / """cache"""
 __lowercase             =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
 __lowercase             =  ParquetDatasetReader(_lowerCamelCase				,  cache_dir=_lowerCamelCase				,  split=_lowerCamelCase					).read()
 _check_parquet_dataset(_lowerCamelCase				,  _lowerCamelCase					)
 assert dataset.split == split if split else "train"





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




 '''simple docstring'''







 if issubclass(_lowerCamelCase				,  _lowerCamelCase					):
  __lowercase             =  parquet_path
 elif issubclass(_lowerCamelCase				,  _lowerCamelCase					):
  __lowercase             =  [parquet_path]
 __lowercase             =  tmp_path / """cache"""
 __lowercase             =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
 __lowercase             =  ParquetDatasetReader(_lowerCamelCase				,  cache_dir=_lowerCamelCase					).read()
 _check_parquet_dataset(_lowerCamelCase				,  _lowerCamelCase					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase=("train",)					):




 '''simple docstring'''







 assert isinstance(_lowerCamelCase				,  _lowerCamelCase					)
 for split in splits:
  __lowercase             =  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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  tmp_path / """cache"""
 __lowercase             =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  __lowercase             =  ParquetDatasetReader(
      {"""train""": parquet_path}				,  cache_dir=_lowerCamelCase				,  keep_in_memory=_lowerCamelCase					).read()
 _check_parquet_datasetdict(_lowerCamelCase				,  _lowerCamelCase					)





@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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







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





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




 '''simple docstring'''







 if split:
  __lowercase             =  {split: parquet_path}
 else:
  __lowercase             =  """train"""
  __lowercase             =  {"""train""": parquet_path, """test""": parquet_path}
 __lowercase             =  tmp_path / """cache"""
 __lowercase             =  {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
 __lowercase             =  ParquetDatasetReader(_lowerCamelCase				,  cache_dir=_lowerCamelCase					).read()
 _check_parquet_datasetdict(_lowerCamelCase				,  _lowerCamelCase				,  splits=list(path.keys()					)					)
 assert all(dataset[split].split == split for split in path.keys()					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  ParquetDatasetWriter(_lowerCamelCase				,  tmp_path / """foo.parquet"""					)
 assert writer.write() > 0
 __lowercase             =  pq.ParquetFile(tmp_path / """foo.parquet"""					)
 __lowercase             =  pf.read()
 assert dataset.data.table == output_table





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







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

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

 __lowercase             =  ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)				,  streaming=_lowerCamelCase					).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 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 assert get_writer_batch_size(_lowerCamelCase					) == expected









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

