"""simple docstring"""







import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json


@pytest.fixture
def 	a__   (		UpperCAmelCase__							:      Tuple      )->						Union[str, Any]:
						UpperCAmelCase__       						=		tmp_path / """file.jsonl"""
						UpperCAmelCase__       						=		textwrap.dedent(
						    """\
        {\"col_1\": -1}
        {\"col_1\": 1, \"col_2\": 2}
        {\"col_1\": 10, \"col_2\": 20}
        """      )
						with open(UpperCAmelCase__		,      """w"""      ) as f:
												f.write(UpperCAmelCase__      )
						return str(UpperCAmelCase__      )


@pytest.fixture
def 	a__   (		UpperCAmelCase__							:      int      )->						Optional[int]:
						UpperCAmelCase__       						=		tmp_path / """file_utf16_encoded.jsonl"""
						UpperCAmelCase__       						=		textwrap.dedent(
						    """\
        {\"col_1\": -1}
        {\"col_1\": 1, \"col_2\": 2}
        {\"col_1\": 10, \"col_2\": 20}
        """      )
						with open(UpperCAmelCase__		,      """w"""		,      encoding="""utf-16"""      ) as f:
												f.write(UpperCAmelCase__      )
						return str(UpperCAmelCase__      )


@pytest.fixture
def 	a__   (		UpperCAmelCase__							:      Any      )->						Dict:
						UpperCAmelCase__       						=		tmp_path / """file_with_list_of_dicts.json"""
						UpperCAmelCase__       						=		textwrap.dedent(
						    """\
        [
            {\"col_1\": -1},
            {\"col_1\": 1, \"col_2\": 2},
            {\"col_1\": 10, \"col_2\": 20}
        ]
        """      )
						with open(UpperCAmelCase__		,      """w"""      ) as f:
												f.write(UpperCAmelCase__      )
						return str(UpperCAmelCase__      )


@pytest.fixture
def 	a__   (		UpperCAmelCase__							:      Dict      )->						Tuple:
						UpperCAmelCase__       						=		tmp_path / """file_with_list_of_dicts_field.json"""
						UpperCAmelCase__       						=		textwrap.dedent(
						    """\
        {
            \"field1\": 1,
            \"field2\": \"aabb\",
            \"field3\": [
                {\"col_1\": -1},
                {\"col_1\": 1, \"col_2\": 2},
                {\"col_1\": 10, \"col_2\": 20}
            ]
        }
        """      )
						with open(UpperCAmelCase__		,      """w"""      ) as f:
												f.write(UpperCAmelCase__      )
						return str(UpperCAmelCase__      )


@pytest.mark.parametrize(
    """file_fixture, config_kwargs"""		,      [
        ("""jsonl_file""", {}),
        ("""jsonl_file_utf16_encoded""", {"""encoding""": """utf-16"""}),
        ("""json_file_with_list_of_dicts""", {}),
        ("""json_file_with_list_of_dicts_field""", {"""field""": """field3"""}),
    ]		,      )
def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      List[Any]      )->						List[str]:
						UpperCAmelCase__       						=		Json(**UpperCAmelCase__      )
						UpperCAmelCase__       						=		json._generate_tables([[request.getfixturevalue(UpperCAmelCase__      )]]      )
						UpperCAmelCase__       						=		pa.concat_tables([table for _, table in generator]      )
						assert pa_table.to_pydict() == {"col_1": [-1, 1, 1_0], "col_2": [None, 2, 2_0]}







@pytest.mark.parametrize(
    """file_fixture, config_kwargs"""		,      [
        (
            """jsonl_file""",
            {"""features""": Features({"""col_1""": Value("""int64"""      ), """col_2""": Value("""int64"""      ), """missing_col""": Value("""string"""      )}      )},
        ),
        (
            """json_file_with_list_of_dicts""",
            {"""features""": Features({"""col_1""": Value("""int64"""      ), """col_2""": Value("""int64"""      ), """missing_col""": Value("""string"""      )}      )},
        ),
        (
            """json_file_with_list_of_dicts_field""",
            {
                """field""": """field3""",
                """features""": Features(
                    {"""col_1""": Value("""int64"""      ), """col_2""": Value("""int64"""      ), """missing_col""": Value("""string"""      )}      ),
            },
        ),
    ]		,      )
def 	a__   (		UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      Any      )->						Optional[Any]:
						UpperCAmelCase__       						=		Json(**UpperCAmelCase__      )
						UpperCAmelCase__       						=		json._generate_tables([[request.getfixturevalue(UpperCAmelCase__      )]]      )
						UpperCAmelCase__       						=		pa.concat_tables([table for _, table in generator]      )
						assert pa_table.to_pydict() == {"col_1": [-1, 1, 1_0], "col_2": [None, 2, 2_0], "missing_col": [None, None, None]}


"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['pixel_values']

				def __init__(  self							:     str							,     _lowercase							:     bool = True							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = PILImageResampling.BILINEAR							,     _lowercase							:     bool = True							,     _lowercase							:     bool = False							,     _lowercase							:     bool = True							,     _lowercase							:     bool = True							,     _lowercase							:     Union[int, float] = 1 / 2_5_5							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     **_lowercase							:     List[str]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										UpperCAmelCase__       						=		size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
										if isinstance(_lowercase							,     (tuple, list)  ):
																# The previous feature extractor size parameter was in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )

										UpperCAmelCase__       						=		do_resize
										UpperCAmelCase__       						=		size
										UpperCAmelCase__       						=		resample
										UpperCAmelCase__       						=		do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis
										UpperCAmelCase__       						=		do_pad
										UpperCAmelCase__       						=		do_rescale
										UpperCAmelCase__       						=		rescale_factor
										UpperCAmelCase__       						=		do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase__       						=		image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							A			(  self							:     str							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																UpperCAmelCase__       						=		np.rotaa(_lowercase							,     3  )

										if data_format is not None:
																UpperCAmelCase__       						=		to_channel_dimension_format(_lowercase							,     _lowercase  )

										return image

				def 							A			(  self							:     Tuple							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )

										UpperCAmelCase__       						=		output_width - input_width
										UpperCAmelCase__       						=		output_height - input_height

										if random_padding:
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_height + 1  )
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_width + 1  )
										else:
																UpperCAmelCase__       						=		delta_height // 2
																UpperCAmelCase__       						=		delta_width // 2

										UpperCAmelCase__       						=		delta_height - pad_top
										UpperCAmelCase__       						=		delta_width - pad_left

										UpperCAmelCase__       						=		((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_lowercase							,     _lowercase							,     data_format=_lowercase  )

				def 							A			(  self							:     Union[str, Any]							,     *_lowercase							:     List[str]							,     **_lowercase							:     int  ):


										'''simple docstring'''
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
										return self.pad_image(*_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										# We always resize to the smallest of either the input or output size.
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																UpperCAmelCase__       						=		int(input_width * height / input_height  )
										elif input_width > input_height:
																UpperCAmelCase__       						=		int(input_height * width / input_width  )

										return resize(
										    _lowercase							,     size=(height, width)							,     resample=_lowercase							,     reducing_gap=2.0							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		min(size["""height"""]							,     size["""width"""]  )
										UpperCAmelCase__       						=		get_resize_output_image_size(_lowercase							,     size=_lowercase							,     default_to_square=_lowercase  )
										UpperCAmelCase__       						=		resize(_lowercase							,     size=_lowercase							,     resample=_lowercase							,     data_format=_lowercase							,     **_lowercase  )
										return resized_image

				def 							A			(  self							:     int							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[int, float]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Union[str, Any]							,     ):


										'''simple docstring'''
										return rescale(_lowercase							,     scale=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Tuple							,     ):


										'''simple docstring'''
										return normalize(_lowercase							,     mean=_lowercase							,     std=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     ImageInput							,     _lowercase							:     bool = None							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = False							,     _lowercase							:     bool = None							,     _lowercase							:     float = None							,     _lowercase							:     bool = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[str, TensorType]] = None							,     _lowercase							:     Optional[ChannelDimension] = ChannelDimension.FIRST							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__       						=		size if size is not None else self.size
										if isinstance(_lowercase							,     (tuple, list)  ):
																# Previous feature extractor had size in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		resample if resample is not None else self.resample
										UpperCAmelCase__       						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										UpperCAmelCase__       						=		do_pad if do_pad is not None else self.do_pad
										UpperCAmelCase__       						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__       						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__       						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__       						=		image_std if image_std is not None else self.image_std

										UpperCAmelCase__       						=		make_list_of_images(_lowercase  )

										if not valid_images(_lowercase  ):
																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_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

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

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

										if do_align_long_axis:
																UpperCAmelCase__       						=		[self.align_long_axis(_lowercase							,     size=_lowercase  ) for image in images]

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

										if do_thumbnail:
																UpperCAmelCase__       						=		[self.thumbnail(image=_lowercase							,     size=_lowercase  ) for image in images]

										if do_pad:
																UpperCAmelCase__       						=		[self.pad_image(image=_lowercase							,     size=_lowercase							,     random_padding=_lowercase  ) for image in images]

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

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

										UpperCAmelCase__       						=		[to_channel_dimension_format(_lowercase							,     _lowercase  ) for image in images]

										UpperCAmelCase__       						=		{"""pixel_values""": images}
										return BatchFeature(data=_lowercase							,     tensor_type=_lowercase  )

