
'''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 					a_					(_a      ):




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_=False					):
         _lowerCAmelCase			,   _lowerCAmelCase			,   _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    :					Any							=   pa.Table.from_pydict({"""filename""": [Path(snake_case_					).name] * 1_0_0}					)
         if take == -1:
           _lowerCAmelCase    :					Union[str, Any]							=   len(snake_case_					) - skip
         if skip is not None and take is not None:
           _lowerCAmelCase    :					Any							=   pa_table.slice(snake_case_				,				snake_case_					)
         return pa_table



class 					a_					(_a      ):




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   """my_name"""
         _lowerCAmelCase    :					List[str]							=   SplitInfo(name="""train"""				,				num_examples=1_0_0					)
         _lowerCAmelCase    :					List[Any]							=   SplitInfo(name="""test"""				,				num_examples=1_0_0					)
         _lowerCAmelCase    :					int							=   [train_info, test_info]
         _lowerCAmelCase    :					Union[str, Any]							=   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    :					List[Any]							=   ReaderTest(snake_case_				,				snake_case_					)

           _lowerCAmelCase    :					int							=   """test[:33%]"""
           _lowerCAmelCase    :					Union[str, Any]							=   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					)

           _lowerCAmelCase    :					Any							=   ["""train""", """test[:33%]"""]
           _lowerCAmelCase    :					List[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			,   _lowerCAmelCase    :					Tuple							=   (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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   """my_name"""
         _lowerCAmelCase    :					List[str]							=   SplitInfo(name="""train"""				,				num_examples=1_0_0_0				,				shard_lengths=[1_0_0] * 1_0					)
         _lowerCAmelCase    :					Optional[int]							=   [train_info]
         _lowerCAmelCase    :					Any							=   SplitDict()
         split_dict.add(snake_case_					)
         _lowerCAmelCase    :					Any							=   DatasetInfo(splits=snake_case_					)

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

           _lowerCAmelCase    :					List[Any]							=   """train[:33%]"""
           _lowerCAmelCase    :					Dict							=   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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   SplitInfo(name="""train"""				,				num_examples=1_0_0					)
         _lowerCAmelCase    :					Union[str, Any]							=   SplitInfo(name="""test"""				,				num_examples=1_0_0					)
         _lowerCAmelCase    :					List[str]							=   SplitDict()
         split_dict.add(snake_case_					)
         split_dict.add(snake_case_					)
         _lowerCAmelCase    :					Union[str, Any]							=   DatasetInfo(splits=snake_case_					)

         with tempfile.TemporaryDirectory() as tmp_dir:
           _lowerCAmelCase    :					Dict							=   ReaderTest(snake_case_				,				snake_case_					)

           _lowerCAmelCase    :					Tuple							=   [
               {"""filename""": os.path.join(snake_case_				,				"""train"""					)},
               {"""filename""": os.path.join(snake_case_				,				"""test"""					), """skip""": 1_0, """take""": 1_0},
           ]
           _lowerCAmelCase    :					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 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    Union[str, Any]						)      ->  List[Any]:
  _lowerCAmelCase    :					Dict							=   arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowerCAmelCase    :					Optional[int]							=   ArrowReader.read_table(_lowerCamelCase					, in_memory=_lowerCamelCase						)
  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			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Tuple						)      ->  Dict:
  _lowerCAmelCase    :					Union[str, Any]							=   arrow_file
  _lowerCAmelCase    :					Any							=   ArrowReader(""""""					, _lowerCamelCase						)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowerCAmelCase    :					Optional[int]							=   reader.read_files([{"""filename""": filename}]					, in_memory=_lowerCamelCase						)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _lowerCAmelCase    :					Tuple							=   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			(					)      ->  int:
  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    :					Dict							=   """train+test"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == spec_train_test

  _lowerCAmelCase    :					List[str]							=   """train[2:10]"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == spec_train_abs

  _lowerCAmelCase    :					Union[str, Any]							=   """train[15%:-20%]"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == spec_train_pct

  _lowerCAmelCase    :					Union[str, Any]							=   """train[:10%](closest)"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == "train[:10%]"

  _lowerCAmelCase    :					List[str]							=   """train[:10%](pct1_dropremainder)"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == spec_train_pct_rounding

  _lowerCAmelCase    :					Any							=   """train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
  assert ReadInstruction.from_spec(_lowerCamelCase						).to_spec() == spec_train_test_pct_rounding


def 		_UpperCAmelCase			(					)      ->  List[Any]:
  _lowerCAmelCase    :					List[str]							=   """dummy"""
  _lowerCAmelCase    :					Optional[Any]							=   [SplitInfo(name="""train"""					, num_examples=1_00						)]
  _lowerCAmelCase    :					str							=   """train[:33%]"""
  _lowerCAmelCase    :					Dict							=   """arrow"""
  _lowerCAmelCase    :					int							=   """prefix"""

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

  _lowerCAmelCase    :					int							=   [SplitInfo(name="""train"""					, num_examples=1_00					, shard_lengths=[10] * 10						)]
  _lowerCAmelCase    :					Optional[Any]							=   make_file_instructions(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)
  assert isinstance(_lowerCamelCase					, _lowerCamelCase						)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(_lowerCamelCase					, f'{name}-train-00000-of-00010.arrow'						), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase					, f'{name}-train-00001-of-00010.arrow'						), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase					, f'{name}-train-00002-of-00010.arrow'						), "skip": 0, "take": -1},
      {"filename": os.path.join(_lowerCamelCase					, f'{name}-train-00003-of-00010.arrow'						), "skip": 0, "take": 3},
  ]



@pytest.mark.parametrize("""name, expected_exception"""					, [(None, TypeError), ("""""", ValueError)]						)
def 		_UpperCAmelCase			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    Dict						)      ->  int:
  _lowerCAmelCase    :					Optional[int]							=   [SplitInfo(name="""train"""					, num_examples=1_00						)]
  _lowerCAmelCase    :					List[str]							=   """train"""
  _lowerCAmelCase    :					Optional[Any]							=   """arrow"""
  _lowerCAmelCase    :					Dict							=   """prefix_path"""
  with pytest.raises(_lowerCamelCase						):
    _lowerCAmelCase    :					str							=   make_file_instructions(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


