





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   __snake_case				(			lowercase_   ):






    '''simple docstring'''





    def 	A		(   self						:		Any			,						snake_case						:		List[str]			,						snake_case						:		Dict=False							):

      """simple docstring"""





      __lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase			=				(
          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			=				pa.Table.from_pydict({'''filename''': [Path(snake_case							).name] * 100}							)
      if take == -1:
        __lowerCAmelCase			=				len(snake_case							) - skip
      if skip is not None and take is not None:
        __lowerCAmelCase			=				pa_table.slice(snake_case			,						snake_case							)
      return pa_table


class   __snake_case				(			lowercase_   ):






    '''simple docstring'''





    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				'''my_name'''
      __lowerCAmelCase			=				SplitInfo(name='''train'''			,						num_examples=100							)
      __lowerCAmelCase			=				SplitInfo(name='''test'''			,						num_examples=100							)
      __lowerCAmelCase			=				[train_info, test_info]
      __lowerCAmelCase			=				SplitDict()
      split_dict.add(snake_case							)
      split_dict.add(snake_case							)
      __lowerCAmelCase			=				DatasetInfo(splits=snake_case							)

      with tempfile.TemporaryDirectory() as tmp_dir:
        __lowerCAmelCase			=				ReaderTest(snake_case			,						snake_case							)

        __lowerCAmelCase			=				'''test[:33%]'''
        __lowerCAmelCase			=				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			=				['''train''', '''test[:33%]''']
        __lowerCAmelCase			=				[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''							)]
        for instructions in [instructionsa, instructionsa]:
          __lowerCAmelCase			=				[reader.read(snake_case			,						snake_case			,						snake_case							) for instr in instructions]
          __lowerCAmelCase	,						__lowerCAmelCase			=				(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 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				'''my_name'''
      __lowerCAmelCase			=				SplitInfo(name='''train'''			,						num_examples=1_000			,						shard_lengths=[100] * 10							)
      __lowerCAmelCase			=				[train_info]
      __lowerCAmelCase			=				SplitDict()
      split_dict.add(snake_case							)
      __lowerCAmelCase			=				DatasetInfo(splits=snake_case							)

      with tempfile.TemporaryDirectory() as tmp_dir:
        __lowerCAmelCase			=				ReaderTest(snake_case			,						snake_case							)

        __lowerCAmelCase			=				'''train[:33%]'''
        __lowerCAmelCase			=				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 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      __lowerCAmelCase			=				SplitInfo(name='''train'''			,						num_examples=100							)
      __lowerCAmelCase			=				SplitInfo(name='''test'''			,						num_examples=100							)
      __lowerCAmelCase			=				SplitDict()
      split_dict.add(snake_case							)
      split_dict.add(snake_case							)
      __lowerCAmelCase			=				DatasetInfo(splits=snake_case							)

      with tempfile.TemporaryDirectory() as tmp_dir:
        __lowerCAmelCase			=				ReaderTest(snake_case			,						snake_case							)

        __lowerCAmelCase			=				[
            {'''filename''': os.path.join(snake_case			,						'''train'''							)},
            {'''filename''': os.path.join(snake_case			,						'''test'''							), '''skip''': 10, '''take''': 10},
        ]
        __lowerCAmelCase			=				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 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[Any]     ,							__lowercase:		int     ,							__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase			=				arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    __lowerCAmelCase			=				ArrowReader.read_table(__lowercase     ,							in_memory=__lowercase)
  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  (							__lowercase:		str     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase			=				arrow_file
  __lowerCAmelCase			=				ArrowReader(''''''     ,							__lowercase)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    __lowerCAmelCase			=				reader.read_files([{'''filename''': filename}]     ,							in_memory=__lowercase)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  __lowerCAmelCase			=				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  (							):



  '''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%]"

  __lowerCAmelCase			=				'''train+test'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == spec_train_test

  __lowerCAmelCase			=				'''train[2:10]'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == spec_train_abs

  __lowerCAmelCase			=				'''train[15%:-20%]'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == spec_train_pct

  __lowerCAmelCase			=				'''train[:10%](closest)'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == "train[:10%]"

  __lowerCAmelCase			=				'''train[:10%](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == spec_train_pct_rounding

  __lowerCAmelCase			=				'''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
  assert ReadInstruction.from_spec(__lowercase).to_spec() == spec_train_test_pct_rounding


def 					__SCREAMING_SNAKE_CASE  (							):



  '''simple docstring'''






  __lowerCAmelCase			=				'''dummy'''
  __lowerCAmelCase			=				[SplitInfo(name='''train'''     ,							num_examples=1_00)]
  __lowerCAmelCase			=				'''train[:33%]'''
  __lowerCAmelCase			=				'''arrow'''
  __lowerCAmelCase			=				'''prefix'''

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

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




@pytest.mark.parametrize('''name, expected_exception'''     ,							[(None, TypeError), ('''''', ValueError)])
def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict     ,							__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase			=				[SplitInfo(name='''train'''     ,							num_examples=1_00)]
  __lowerCAmelCase			=				'''train'''
  __lowerCAmelCase			=				'''arrow'''
  __lowerCAmelCase			=				'''prefix_path'''
  with pytest.raises(__lowercase):
    __lowerCAmelCase			=				make_file_instructions(__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase)







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 ViltImageProcessor

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def __init__(   self						:		Tuple			,						snake_case						:		List[str]			,						snake_case						:		Dict=7			,						snake_case						:		str=3			,						snake_case						:		Union[str, Any]=18			,						snake_case						:		str=30			,						snake_case						:		str=400			,						snake_case						:		Optional[Any]=True			,						snake_case						:		List[str]=None			,						snake_case						:		Tuple=2			,						snake_case						:		Dict=True			,						snake_case						:		Dict=[0.5, 0.5, 0.5]			,						snake_case						:		Tuple=[0.5, 0.5, 0.5]			,						):

      """simple docstring"""





      __lowerCAmelCase			=				size if size is not None else {'''shortest_edge''': 30}
      __lowerCAmelCase			=				parent
      __lowerCAmelCase			=				batch_size
      __lowerCAmelCase			=				num_channels
      __lowerCAmelCase			=				image_size
      __lowerCAmelCase			=				min_resolution
      __lowerCAmelCase			=				max_resolution
      __lowerCAmelCase			=				do_resize
      __lowerCAmelCase			=				size
      __lowerCAmelCase			=				size_divisor
      __lowerCAmelCase			=				do_normalize
      __lowerCAmelCase			=				image_mean
      __lowerCAmelCase			=				image_std





    def 	A		(   self						:		int							):

      """simple docstring"""





      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }





    def 	A		(   self						:		Dict			,						snake_case						:		List[Any]			,						snake_case						:		Any=False							):

      """simple docstring"""





      if not batched:
        __lowerCAmelCase			=				self.size['''shortest_edge''']
        __lowerCAmelCase			=				image_inputs[0]
        if isinstance(snake_case			,						Image.Image							):
          __lowerCAmelCase	,						__lowerCAmelCase			=				image.size
        else:
          __lowerCAmelCase	,						__lowerCAmelCase			=				image.shape[1], image.shape[2]
        __lowerCAmelCase			=				size / min(snake_case			,						snake_case							)
        if h < w:
          __lowerCAmelCase	,						__lowerCAmelCase			=				size, scale * w
        else:
          __lowerCAmelCase	,						__lowerCAmelCase			=				scale * h, size

        __lowerCAmelCase			=				int((1_333 / 800) * size							)
        if max(snake_case			,						snake_case							) > max_size:
          __lowerCAmelCase			=				max_size / max(snake_case			,						snake_case							)
          __lowerCAmelCase			=				newh * scale
          __lowerCAmelCase			=				neww * scale

        __lowerCAmelCase	,						__lowerCAmelCase			=				int(newh + 0.5							), int(neww + 0.5							)
        __lowerCAmelCase	,						__lowerCAmelCase			=				(
            newh // self.size_divisor * self.size_divisor,
            neww // self.size_divisor * self.size_divisor,
        )

      else:
        __lowerCAmelCase			=				[]
        for image in image_inputs:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.get_expected_values([image]							)
          expected_values.append((expected_height, expected_width)							)
        __lowerCAmelCase			=				max(snake_case			,						key=lambda snake_case							: item[0]							)[0]
        __lowerCAmelCase			=				max(snake_case			,						key=lambda snake_case							: item[1]							)[1]

      return expected_height, expected_width


@require_torch
@require_vision
class   __snake_case				(			_a       ,				unittest.TestCase   ):






    '''simple docstring'''
    _lowerCAmelCase     =	ViltImageProcessor if is_vision_available() else None





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				ViltImageProcessingTester(self							)





    @property
    def 	A		(   self						:		Any							):

      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(snake_case			,						'''image_mean'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''image_std'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_normalize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''do_resize'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''size'''							)							)
      self.assertTrue(hasattr(snake_case			,						'''size_divisor'''							)							)





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size			,						{'''shortest_edge''': 30}							)

      __lowerCAmelCase			=				self.image_processing_class.from_dict(self.image_processor_dict			,						size=42							)
      self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42}							)





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      pass





    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      __lowerCAmelCase			=				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			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)





    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      __lowerCAmelCase			=				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			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)





    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      __lowerCAmelCase			=				self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      __lowerCAmelCase			=				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			=				image_processing(image_inputs[0]			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

      # Test batched
      __lowerCAmelCase			=				image_processing(snake_case			,						return_tensors='''pt'''							).pixel_values

      __lowerCAmelCase	,						__lowerCAmelCase			=				self.image_processor_tester.get_expected_values(snake_case			,						batched=snake_case							)
      self.assertEqual(
          encoded_images.shape			,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,						)
