'''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


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


if is_vision_available():
						import PIL





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







			def __init__(      self							:    Any							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    bool = True							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    bool = True							,     lowercase_							:    Union[int, float] = 1 / 255							,     lowercase_							:    bool = True							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = True							,     **lowercase_							:    Optional[Any]							,     ):
										super().__init__(**lowercase_       )
										lowercase_   :     List[Any]									=			size if size is not None else {"""shortest_edge""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_   :     int									=			get_size_dict(lowercase_       )

										lowercase_   :     Tuple									=			do_resize
										lowercase_   :     int									=			size
										lowercase_   :     List[Any]									=			resample
										lowercase_   :     str									=			do_center_crop
										lowercase_   :     Optional[int]									=			crop_size
										lowercase_   :     str									=			do_rescale
										lowercase_   :     List[Any]									=			rescale_factor
										lowercase_   :     Dict									=			do_normalize
										lowercase_   :     List[str]									=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowercase_   :     Any									=			image_std if image_std is not None else OPENAI_CLIP_STD
										lowercase_   :     List[str]									=			do_convert_rgb







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    PILImageResampling = PILImageResampling.BICUBIC							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Dict							,     ):
										lowercase_   :     Tuple									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     List[Any]									=			get_resize_output_image_size(
										    lowercase_							,     size=(size["""height"""], size["""width"""])							,     default_to_square=lowercase_       )
										return resize(lowercase_							,     size=lowercase_							,     resample=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    np.ndarray							,     lowercase_							:    Dict[str, int]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Any							,     ):
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										return center_crop(lowercase_							,     size=(size["""height"""], size["""width"""])							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[int, float]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    List[Any]							,     ):
										return rescale(lowercase_							,     scale=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    np.ndarray							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Union[float, List[float]]							,     lowercase_							:    Optional[Union[str, ChannelDimension]] = None							,     **lowercase_							:    Optional[Any]							,     ):
										return normalize(lowercase_							,     mean=lowercase_							,     std=lowercase_							,     data_format=lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    ImageInput							,     lowercase_							:    bool = None							,     lowercase_							:    Dict[str, int] = None							,     lowercase_							:    PILImageResampling = None							,     lowercase_							:    bool = None							,     lowercase_							:    int = None							,     lowercase_							:    bool = None							,     lowercase_							:    float = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    Optional[Union[float, List[float]]] = None							,     lowercase_							:    bool = None							,     lowercase_							:    Optional[Union[str, TensorType]] = None							,     lowercase_							:    Optional[ChannelDimension] = ChannelDimension.FIRST							,     **lowercase_							:    Optional[int]							,     ):
										lowercase_   :     Optional[Any]									=			do_resize if do_resize is not None else self.do_resize
										lowercase_   :     Union[str, Any]									=			size if size is not None else self.size
										lowercase_   :     Optional[int]									=			get_size_dict(lowercase_							,     default_to_square=lowercase_       )
										lowercase_   :     int									=			resample if resample is not None else self.resample
										lowercase_   :     str									=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_   :     int									=			crop_size if crop_size is not None else self.crop_size
										lowercase_   :     int									=			get_size_dict(lowercase_       )
										lowercase_   :     Dict									=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_   :     List[str]									=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_   :     Optional[Any]									=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_   :     List[Any]									=			image_mean if image_mean is not None else self.image_mean
										lowercase_   :     str									=			image_std if image_std is not None else self.image_std
										lowercase_   :     Any									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowercase_   :     int									=			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_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:
																	lowercase_   :     Dict									=			[convert_to_rgb(lowercase_       ) for image in images]

										# All transformations expect numpy arrays.
										lowercase_   :     Any									=			[to_numpy_array(lowercase_       ) for image in images]

										if do_resize:
																	lowercase_   :     Union[str, Any]									=			[self.resize(image=lowercase_							,     size=lowercase_							,     resample=lowercase_       ) for image in images]

										if do_center_crop:
																	lowercase_   :     List[str]									=			[self.center_crop(image=lowercase_							,     size=lowercase_       ) for image in images]

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

										if do_normalize:
																	lowercase_   :     List[Any]									=			[self.normalize(image=lowercase_							,     mean=lowercase_							,     std=lowercase_       ) for image in images]

										lowercase_   :     List[Any]									=			[to_channel_dimension_format(lowercase_							,     lowercase_       ) for image in images]

										lowercase_   :     Any									=			{"""pixel_values""": images}
										return BatchFeature(data=lowercase_							,     tensor_type=lowercase_       )






'''simple docstring'''




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict							,     lowercase_							:    List[str]=False       ):
										lowercase_		,		lowercase_		,		lowercase_   :     str									=			(
										    filename_skip_take["""filename"""],
										    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
										    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
										)
										open(os.path.join(lowercase_       )							,     """wb"""       ).close()
										lowercase_   :     Union[str, Any]									=			pa.Table.from_pydict({"""filename""": [Path(lowercase_       ).name] * 100}       )
										if take == -1:
																	lowercase_   :     Optional[int]									=			len(lowercase_       ) - skip
										if skip is not None and take is not None:
																	lowercase_   :     Any									=			pa_table.slice(lowercase_							,     lowercase_       )
										return pa_table





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Union[str, Any]									=			"""my_name"""
										lowercase_   :     Optional[Any]									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Dict									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     str									=			[train_info, test_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[Any]									=			"""test[:33%]"""
																	lowercase_   :     List[Any]									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-test'''       )
																	self.assertEqual(dset.num_rows							,     33       )
																	self.assertEqual(dset.num_columns							,     1       )

																	lowercase_   :     str									=			["""train""", """test[:33%]"""]
																	lowercase_   :     Any									=			[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
																	for instructions in [instructionsa, instructionsa]:
																								lowercase_   :     Dict									=			[reader.read(lowercase_							,     lowercase_							,     lowercase_       ) for instr in instructions]
																								lowercase_		,		lowercase_   :     List[Any]									=			(Dataset(**lowercase_       ) for dataset_kwargs in datasets_kwargs)
																								self.assertEqual(train_dset["""filename"""][0]							,     f'''{name}-train'''       )
																								self.assertEqual(train_dset.num_rows							,     100       )
																								self.assertEqual(train_dset.num_columns							,     1       )
																								self.assertIsInstance(train_dset.split							,     lowercase_       )
																								self.assertEqual(str(train_dset.split       )							,     """train"""       )
																								self.assertEqual(test_dset["""filename"""][0]							,     f'''{name}-test'''       )
																								self.assertEqual(test_dset.num_rows							,     33       )
																								self.assertEqual(test_dset.num_columns							,     1       )
																								self.assertIsInstance(test_dset.split							,     lowercase_       )
																								self.assertEqual(str(test_dset.split       )							,     """test[:33%]"""       )
																								del train_dset, test_dset







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     List[Any]									=			"""my_name"""
										lowercase_   :     int									=			SplitInfo(name="""train"""							,     num_examples=1000							,     shard_lengths=[100] * 10       )
										lowercase_   :     Tuple									=			[train_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Optional[int]									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[int]									=			"""train[:33%]"""
																	lowercase_   :     int									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-train-00000-of-00010'''       )
																	self.assertEqual(dset["""filename"""][-1]							,     f'''{name}-train-00003-of-00010'''       )
																	self.assertEqual(dset.num_rows							,     330       )
																	self.assertEqual(dset.num_columns							,     1       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Any									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Optional[int]									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     Union[str, Any]									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Tuple									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     List[str]									=			[
																	    {"""filename""": os.path.join(lowercase_							,     """train"""       )},
																	    {"""filename""": os.path.join(lowercase_							,     """test"""       ), """skip""": 10, """take""": 10},
																	]
																	lowercase_   :     int									=			Dataset(**reader.read_files(lowercase_							,     original_instructions="""train+test[10:20]"""       )       )
																	self.assertEqual(dset.num_rows							,     110       )
																	self.assertEqual(dset.num_columns							,     1       )
																	del dset




@pytest.mark.parametrize("""in_memory""" , [False, True]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Union[str, Any]					)     -> Union[str, Any]:
							lowercase_   :     str									=			arrow_file
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Optional[Any]									=			ArrowReader.read_table(UpperCAmelCase__ , in_memory=UpperCAmelCase__					)
							assert table.shape == dataset.data.shape
							assert set(table.column_names					) == set(dataset.data.column_names					)
							assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




@pytest.mark.parametrize("""in_memory""" , [False, True]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Optional[int]					)     -> Any:
							lowercase_   :     Tuple									=			arrow_file
							lowercase_   :     Optional[Any]									=			ArrowReader("""""" , UpperCAmelCase__					)
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Dict									=			reader.read_files([{"""filename""": filename}] , in_memory=UpperCAmelCase__					)
							assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
							lowercase_   :     Optional[int]									=			dataset_kwargs["""arrow_table"""]
							assert table.shape == dataset.data.shape
							assert set(table.column_names					) == set(dataset.data.column_names					)
							assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




def 				lowerCamelCase      ( )     -> Optional[Any]:
							assert ReadInstruction("""train""" , to=10 , unit="""abs"""					).to_spec() == "train[:10]"
							assert ReadInstruction("""train""" , from_=-80 , to=10 , unit="""%"""					).to_spec() == "train[-80%:10%]"

							lowercase_   :     Tuple									=			"""train+test"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test

							lowercase_   :     List[Any]									=			"""train[2:10]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_abs

							lowercase_   :     Any									=			"""train[15%:-20%]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct

							lowercase_   :     Optional[int]									=			"""train[:10%](closest)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == "train[:10%]"

							lowercase_   :     List[Any]									=			"""train[:10%](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct_rounding

							lowercase_   :     Union[str, Any]									=			"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test_pct_rounding




def 				lowerCamelCase      ( )     -> int:
							lowercase_   :     Optional[int]									=			"""dummy"""
							lowercase_   :     Union[str, Any]									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Optional[int]									=			"""train[:33%]"""
							lowercase_   :     Dict									=			"""arrow"""
							lowercase_   :     int									=			"""prefix"""

							lowercase_   :     int									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)
							assert isinstance(UpperCAmelCase__ , UpperCAmelCase__					)
							assert file_instructions.num_examples == 33
							assert file_instructions.file_instructions == [
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train.arrow'''					), "skip": 0, "take": 33}
							]

							lowercase_   :     Dict									=			[SplitInfo(name="""train""" , num_examples=100 , shard_lengths=[10] * 10					)]
							lowercase_   :     Dict									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)
							assert isinstance(UpperCAmelCase__ , UpperCAmelCase__					)
							assert file_instructions.num_examples == 33
							assert file_instructions.file_instructions == [
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
							]




@pytest.mark.parametrize("""name, expected_exception""" , [(None, TypeError), ("""""", ValueError)]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Any					)     -> List[str]:
							lowercase_   :     str									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Any									=			"""train"""
							lowercase_   :     Optional[int]									=			"""arrow"""
							lowercase_   :     str									=			"""prefix_path"""
							with pytest.raises(UpperCAmelCase__					):
														lowercase_   :     List[str]									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)





