


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        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def      _snake_case	(				self:       Union[str, Any]						,      a:       Union[str, Any]						,      a:       List[str]=False				):
       __lowerCamelCase		:							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()
       __lowerCamelCase		:							Optional[int]							=						pa.Table.from_pydict({'filename': [Path(snake_case__				).name] * 100}				)
       if take == -1:
        __lowerCamelCase		:							int							=						len(snake_case__				) - skip
       if skip is not None and take is not None:
        __lowerCamelCase		:							int							=						pa_table.slice(snake_case__						,      snake_case__				)
       return pa_table



class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							List[str]							=						"my_name"
       __lowerCamelCase		:							str							=						SplitInfo(name='train'						,      num_examples=100				)
       __lowerCamelCase		:							Optional[Any]							=						SplitInfo(name='test'						,      num_examples=100				)
       __lowerCamelCase		:							Optional[int]							=						[train_info, test_info]
       __lowerCamelCase		:							Tuple							=						SplitDict()
       split_dict.add(snake_case__				)
       split_dict.add(snake_case__				)
       __lowerCamelCase		:							Optional[Any]							=						DatasetInfo(splits=snake_case__				)

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

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

        __lowerCamelCase		:							List[str]							=						["train", "test[:33%]"]
        __lowerCamelCase		:							Optional[Any]							=						[Split.TRAIN, ReadInstruction.from_spec('test[:33%]'				)]
        for instructions in [instructionsa, instructionsa]:
         __lowerCamelCase		:							Union[str, Any]							=						[reader.read(snake_case__						,      snake_case__						,      snake_case__				) for instr in instructions]
         __lowerCamelCase		:							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						,      100				)
         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						,      33				)
         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      _snake_case	(				self:       Dict				):
       __lowerCamelCase		:							int							=						"my_name"
       __lowerCamelCase		:							Optional[int]							=						SplitInfo(name='train'						,      num_examples=1000						,      shard_lengths=[100] * 10				)
       __lowerCamelCase		:							int							=						[train_info]
       __lowerCamelCase		:							Tuple							=						SplitDict()
       split_dict.add(snake_case__				)
       __lowerCamelCase		:							Any							=						DatasetInfo(splits=snake_case__				)

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

        __lowerCamelCase		:							str							=						"train[:33%]"
        __lowerCamelCase		:							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						,      330				)
        self.assertEqual(dset.num_columns						,      1				)




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						SplitInfo(name='train'						,      num_examples=100				)
       __lowerCamelCase		:							Union[str, Any]							=						SplitInfo(name='test'						,      num_examples=100				)
       __lowerCamelCase		:							Dict							=						SplitDict()
       split_dict.add(snake_case__				)
       split_dict.add(snake_case__				)
       __lowerCamelCase		:							str							=						DatasetInfo(splits=snake_case__				)

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

        __lowerCamelCase		:							str							=						[
            {"filename": os.path.join(snake_case__						,      'train'				)},
            {"filename": os.path.join(snake_case__						,      'test'				), "skip": 10, "take": 10},
        ]
        __lowerCamelCase		:							Dict							=						Dataset(**reader.read_files(snake_case__						,      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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						arrow_file
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  __lowerCamelCase		:							Tuple							=						ArrowReader.read_table(SCREAMING_SNAKE_CASE__			,       in_memory=SCREAMING_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       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Any							=						arrow_file
 __lowerCamelCase		:							Optional[Any]							=						ArrowReader(''			,       SCREAMING_SNAKE_CASE__						)
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  __lowerCamelCase		:							int							=						reader.read_files([{'filename': filename}]			,       in_memory=SCREAMING_SNAKE_CASE__						)
 assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
 __lowerCamelCase		:							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       UpperCamelCase__     (  ):
 assert ReadInstruction('train'			,       to=10			,       unit='abs'						).to_spec() == "train[:10]"
 assert ReadInstruction('train'			,       from_=-80			,       to=10			,       unit='%'						).to_spec() == "train[-80%:10%]"

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

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

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

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

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

 __lowerCamelCase		:							int							=						"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
 assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE__						).to_spec() == spec_train_test_pct_rounding
def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							int							=						"dummy"
 __lowerCamelCase		:							Dict							=						[SplitInfo(name='train'			,       num_examples=100						)]
 __lowerCamelCase		:							List[Any]							=						"train[:33%]"
 __lowerCamelCase		:							str							=						"arrow"
 __lowerCamelCase		:							int							=						"prefix"

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

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



@pytest.mark.parametrize('name, expected_exception'			,       [(None, TypeError), ('', ValueError)]						)
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int							=						[SplitInfo(name='train'			,       num_examples=100						)]
 __lowerCamelCase		:							Dict							=						"train"
 __lowerCamelCase		:							Any							=						"arrow"
 __lowerCamelCase		:							Optional[int]							=						"prefix_path"
 with pytest.raises(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							List[Any]							=						make_file_instructions(SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__			,       SCREAMING_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__				)


