






"""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        lowerCAmelCase__       (  __magic_name__					):
   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     Union[str, Any]				,       snake_case__	:     List[str]=False				):


        '''simple docstring'''
        UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  Dict				=				(
            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(snake_case__				)				,       "wb"				).close()
        UpperCAmelCase__		:  Optional[int]				=				pa.Table.from_pydict({"filename": [Path(snake_case__				).name] * 1_0_0}				)
        if take == -1:
             UpperCAmelCase__		:  int				=				len(snake_case__				) - skip
        if skip is not None and take is not None:
             UpperCAmelCase__		:  int				=				pa_table.slice(snake_case__				,       snake_case__				)
        return pa_table




class        lowerCAmelCase__       (  __magic_name__					):
   def 	__a				(						self	:     str				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				"my_name"
        UpperCAmelCase__		:  str				=				SplitInfo(name="train"				,       num_examples=1_0_0				)
        UpperCAmelCase__		:  Optional[Any]				=				SplitInfo(name="test"				,       num_examples=1_0_0				)
        UpperCAmelCase__		:  Optional[int]				=				[train_info, test_info]
        UpperCAmelCase__		:  Tuple				=				SplitDict()
        split_dict.add(snake_case__				)
        split_dict.add(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				DatasetInfo(splits=snake_case__				)

        with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCAmelCase__		:  Union[str, Any]				=				ReaderTest(snake_case__				,       snake_case__				)

             UpperCAmelCase__		:  List[str]				=				"test[:33%]"
             UpperCAmelCase__		:  Dict				=				Dataset(**reader.read(snake_case__				,       snake_case__				,       snake_case__				)				)
             self.assertEqual(dset["filename"][0]				,       f'{name}-test'				)
             self.assertEqual(dset.num_rows				,       3_3				)
             self.assertEqual(dset.num_columns				,       1				)

             UpperCAmelCase__		:  List[str]				=				["train", "test[:33%]"]
             UpperCAmelCase__		:  Optional[Any]				=				[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"				)]
             for instructions in [instructionsa, instructionsa]:
                  UpperCAmelCase__		:  Union[str, Any]				=				[reader.read(snake_case__				,       snake_case__				,       snake_case__				) for instr in instructions]
                  UpperCAmelCase__				,							UpperCAmelCase__		:  Any				=				(Dataset(**snake_case__				) for dataset_kwargs in datasets_kwargs)
                  self.assertEqual(train_dset["filename"][0]				,       f'{name}-train'				)
                  self.assertEqual(train_dset.num_rows				,       1_0_0				)
                  self.assertEqual(train_dset.num_columns				,       1				)
                  self.assertIsInstance(train_dset.split				,       snake_case__				)
                  self.assertEqual(str(train_dset.split				)				,       "train"				)
                  self.assertEqual(test_dset["filename"][0]				,       f'{name}-test'				)
                  self.assertEqual(test_dset.num_rows				,       3_3				)
                  self.assertEqual(test_dset.num_columns				,       1				)
                  self.assertIsInstance(test_dset.split				,       snake_case__				)
                  self.assertEqual(str(test_dset.split				)				,       "test[:33%]"				)
                  del train_dset, test_dset
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				"my_name"
        UpperCAmelCase__		:  Optional[int]				=				SplitInfo(name="train"				,       num_examples=1_0_0_0				,       shard_lengths=[1_0_0] * 1_0				)
        UpperCAmelCase__		:  int				=				[train_info]
        UpperCAmelCase__		:  Tuple				=				SplitDict()
        split_dict.add(snake_case__				)
        UpperCAmelCase__		:  Any				=				DatasetInfo(splits=snake_case__				)

        with tempfile.TemporaryDirectory() as tmp_dir:
             UpperCAmelCase__		:  Optional[Any]				=				ReaderTest(snake_case__				,       snake_case__				)

             UpperCAmelCase__		:  str				=				"train[:33%]"
             UpperCAmelCase__		:  Tuple				=				Dataset(**reader.read(snake_case__				,       snake_case__				,       snake_case__				)				)
             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				,       3_3_0				)
             self.assertEqual(dset.num_columns				,       1				)




   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				SplitInfo(name="train"				,       num_examples=1_0_0				)
        UpperCAmelCase__		:  Union[str, Any]				=				SplitInfo(name="test"				,       num_examples=1_0_0				)
        UpperCAmelCase__		:  Dict				=				SplitDict()
        split_dict.add(snake_case__				)
        split_dict.add(snake_case__				)
        UpperCAmelCase__		:  str				=				DatasetInfo(splits=snake_case__				)

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

             UpperCAmelCase__		:  str				=				[
                 {"filename": os.path.join(snake_case__				,       "train"				)},
                 {"filename": os.path.join(snake_case__				,       "test"				), "skip": 1_0, "take": 1_0},
             ]
             UpperCAmelCase__		:  Dict				=				Dataset(**reader.read_files(snake_case__				,       original_instructions="train+test[10:20]"				)				)
             self.assertEqual(dset.num_rows				,       1_1_0				)
             self.assertEqual(dset.num_columns				,       1				)
             del dset




@pytest.mark.parametrize("in_memory"       , [False, True]							)
def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Dict       , snake_case    :      Optional[Any]       , snake_case    :      Union[str, Any]							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCAmelCase__		:  Tuple				=				ArrowReader.read_table(snake_case       , in_memory=snake_case							)
     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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Optional[int]							)->						List[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  Any				=				arrow_file
     UpperCAmelCase__		:  Optional[Any]				=				ArrowReader(""       , snake_case							)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          UpperCAmelCase__		:  int				=				reader.read_files([{"filename": filename}]       , in_memory=snake_case							)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     UpperCAmelCase__		:  Union[str, Any]				=				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      SCREAMING_SNAKE_CASE__    (   )->						Optional[int]:






     '''simple docstring'''



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

     UpperCAmelCase__		:  Optional[int]				=				"train+test"
     assert ReadInstruction.from_spec(snake_case							).to_spec() == spec_train_test

     UpperCAmelCase__		:  Tuple				=				"train[2:10]"
     assert ReadInstruction.from_spec(snake_case							).to_spec() == spec_train_abs

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

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

     UpperCAmelCase__		:  str				=				"train[:10%](pct1_dropremainder)"
     assert ReadInstruction.from_spec(snake_case							).to_spec() == spec_train_pct_rounding

     UpperCAmelCase__		:  int				=				"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(snake_case							).to_spec() == spec_train_test_pct_rounding


def      SCREAMING_SNAKE_CASE__    (   )->						List[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				"dummy"
     UpperCAmelCase__		:  Dict				=				[SplitInfo(name="train"       , num_examples=100							)]
     UpperCAmelCase__		:  List[Any]				=				"train[:33%]"
     UpperCAmelCase__		:  str				=				"arrow"
     UpperCAmelCase__		:  int				=				"prefix"

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

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



@pytest.mark.parametrize("name, expected_exception"       , [(None, TypeError), ("", ValueError)]							)
def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[Any]       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				[SplitInfo(name="train"       , num_examples=100							)]
     UpperCAmelCase__		:  Dict				=				"train"
     UpperCAmelCase__		:  Any				=				"arrow"
     UpperCAmelCase__		:  Optional[int]				=				"prefix_path"
     with pytest.raises(snake_case							):
          UpperCAmelCase__		:  List[Any]				=				make_file_instructions(snake_case       , snake_case       , snake_case       , snake_case       , snake_case							)










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


_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     str				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				do_resize
        UpperCAmelCase__		:  Any				=				do_rescale
        UpperCAmelCase__		:  Optional[int]				=				do_normalize
        UpperCAmelCase__		:  Dict				=				size
        UpperCAmelCase__		:  Optional[Any]				=				resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor
        UpperCAmelCase__		:  str				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase__		:  Dict				=				image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				get_size_dict(snake_case__				)
        if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
        return resize(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     float				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     str				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[Any]				,       ):


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




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


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Optional[Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  str				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Optional[int]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  List[Any]				=				make_list_of_images(snake_case__				)

        if not valid_images(snake_case__				):
             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."				)

        # All transformations expect numpy arrays.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

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

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

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

        UpperCAmelCase__		:  Any				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

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


