





'''simple docstring'''



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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  assert isinstance(lowercase__					,	lowercase__       )
  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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _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():
    _snake_case     		=				ParquetDatasetReader(lowercase__					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__       ).read()
  _check_parquet_dataset(lowercase__					,	lowercase__       )




@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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




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







  '''simple docstring'''



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




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







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=("train",)       ):







  '''simple docstring'''



  assert isinstance(lowercase__					,	lowercase__       )
  for split in splits:
    _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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				tmp_path / "cache"
  _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():
    _snake_case     		=				ParquetDatasetReader(
        {"train": parquet_path}					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__       ).read()
  _check_parquet_datasetdict(lowercase__					,	lowercase__       )




@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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




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







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

  _snake_case     		=				ParquetDatasetReader(str(tmp_path / "foo.parquet"       )					,	streaming=lowercase__       ).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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  assert get_writer_batch_size(lowercase__       ) == expected









'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = True						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."	)

       if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."	)

       if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."	)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."	)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

       # All transformations expect numpy arrays.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

       if do_rescale:
         _snake_case     		=				[self.rescale(image=lowerCamelCase						,   scale=lowerCamelCase	) for image in images]

       if do_normalize:
         _snake_case     		=				[self.normalize(image=lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase	) for image in images]

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


