

"""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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size


class       _snake_case  (    unittest.TestCase							):







						def __init__(self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=7  ,				UpperCAmelCase_=3  ,				UpperCAmelCase_=1_8  ,				UpperCAmelCase_=3_0  ,				UpperCAmelCase_=4_0_0  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=1 / 2_5_5  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=8  ,				)      ->    Any:
											__lowercase        =      parent
											__lowercase        =      batch_size
											__lowercase        =      num_channels
											__lowercase        =      image_size
											__lowercase        =      min_resolution
											__lowercase        =      max_resolution
											__lowercase        =      do_rescale
											__lowercase        =      rescale_factor
											__lowercase        =      do_pad
											__lowercase        =      pad_size







						def 							_UpperCamelCase     (self				)      ->    Dict:
											return {
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											    "pad_size": self.pad_size,
											}







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_=False  ,				UpperCAmelCase_=False  ,				UpperCAmelCase_=False				)      ->    Tuple:
											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																__lowercase        =      []
																for i in range(self.batch_size				):
																					image_inputs.append(
																					    np.random.randint(
																					        2_5_5  ,				size=(self.num_channels, self.max_resolution, self.max_resolution)  ,				dtype=np.uinta				)				)
											else:
																__lowercase        =      []
																for i in range(self.batch_size				):
																					__lowercase    ,			__lowercase        =      np.random.choice(np.arange(self.min_resolution  ,				self.max_resolution				)  ,				2				)
																					image_inputs.append(np.random.randint(2_5_5  ,				size=(self.num_channels, width, height)  ,				dtype=np.uinta				)				)

											if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																__lowercase        =      [Image.fromarray(np.moveaxis(_lowercase  ,				0  ,				-1				)				) for x in image_inputs]

											if torchify:
																__lowercase        =      [torch.from_numpy(_lowercase				) for x in image_inputs]

											return image_inputs



@require_torch
@require_vision
class       _snake_case  (    lowerCAmelCase__    ,      unittest.TestCase							):
						__snake_case			      =  SwinaSRImageProcessor if is_vision_available() else None







						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      SwinaSRImageProcessingTester(self				)







						@property
						def 							_UpperCamelCase     (self				)      ->    Dict:
											return self.image_processor_tester.prepare_image_processor_dict()







						def 							_UpperCamelCase     (self				)      ->    str:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(_lowercase  ,				'''do_rescale'''				)				)
											self.assertTrue(hasattr(_lowercase  ,				'''rescale_factor'''				)				)
											self.assertTrue(hasattr(_lowercase  ,				'''do_pad'''				)				)
											self.assertTrue(hasattr(_lowercase  ,				'''pad_size'''				)				)







						def 							_UpperCamelCase     (self				)      ->    List[str]:
											pass







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    Tuple:
											__lowercase    ,			__lowercase        =      get_image_size(_lowercase				)
											__lowercase        =      self.image_processor_tester.pad_size

											__lowercase        =      (old_height // size + 1) * size - old_height
											__lowercase        =      (old_width // size + 1) * size - old_width
											return old_height + pad_height, old_width + pad_width







						def 							_UpperCamelCase     (self				)      ->    Union[str, Any]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											__lowercase        =      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase				)
											for image in image_inputs:
																self.assertIsInstance(_lowercase  ,				Image.Image				)

											# Test not batched input
											__lowercase        =      image_processor(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											__lowercase    ,			__lowercase        =      self.calculate_expected_size(np.array(image_inputs[0]				)				)
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,				)







						def 							_UpperCamelCase     (self				)      ->    int:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											__lowercase        =      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase  ,				numpify=_lowercase				)
											for image in image_inputs:
																self.assertIsInstance(_lowercase  ,				np.ndarray				)

											# Test not batched input
											__lowercase        =      image_processor(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											__lowercase    ,			__lowercase        =      self.calculate_expected_size(image_inputs[0]				)
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,				)







						def 							_UpperCamelCase     (self				)      ->    Optional[Any]:
											__lowercase        =      self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											__lowercase        =      self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase  ,				torchify=_lowercase				)
											for image in image_inputs:
																self.assertIsInstance(_lowercase  ,				torch.Tensor				)

											# Test not batched input
											__lowercase        =      image_processor(image_inputs[0]  ,				return_tensors='''pt'''				).pixel_values
											__lowercase    ,			__lowercase        =      self.calculate_expected_size(image_inputs[0]				)
											self.assertEqual(
											    encoded_images.shape  ,				(
											        1,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )  ,				)








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""						,			[False, True]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize(
    """features"""						,			[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]						,			)
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			features=UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize("""split"""						,			[None, NamedSplit("""train"""   ), """train""", """test"""]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			split=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )
				assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""						,			[str, list]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				if issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  csv_path
				elif issubclass(UpperCamelCase__						,			UpperCamelCase__   ):
								__lowerCAmelCase					=  [csv_path]
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_dataset(UpperCamelCase__						,			UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=("train",)   )     ->     Optional[int]:







				'''simple docstring'''


				assert isinstance(UpperCamelCase__						,			UpperCamelCase__   )
				for split in splits:
								__lowerCAmelCase					=  dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""						,			[False, True]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__   ).read()
				_check_csv_datasetdict(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize(
    """features"""						,			[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]						,			)
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  features.copy() if features else default_expected_features
				__lowerCAmelCase					=  (
				    Features({feature: Value(UpperCamelCase__   ) for feature, dtype in features.items()}   ) if features is not None else None
				)
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			features=UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_datasetdict(UpperCamelCase__						,			UpperCamelCase__   )





@pytest.mark.parametrize("""split"""						,			[None, NamedSplit("""train"""   ), """train""", """test"""]   )
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


				if split:
								__lowerCAmelCase					=  {split: csv_path}
				else:
								__lowerCAmelCase					=  {"""train""": csv_path, """test""": csv_path}
				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  {"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				__lowerCAmelCase					=  CsvDatasetReader(UpperCamelCase__						,			cache_dir=UpperCamelCase__   ).read()
				_check_csv_datasetdict(UpperCamelCase__						,			UpperCamelCase__						,			splits=list(path.keys()   )   )
				assert all(dataset[split].split == split for split in path.keys()   )





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				with open(UpperCamelCase__						,			encoding="""utf-8"""   ) as csvfile:
								yield from csv.reader(UpperCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=1   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

				for rowa, rowa in zip(UpperCamelCase__						,			UpperCamelCase__   ):
								assert rowa == rowa





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=2   ).write()

				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )
				__lowerCAmelCase					=  iter_csv_file(UpperCamelCase__   )

				for rowa, rowa in zip(UpperCamelCase__						,			UpperCamelCase__   ):
								assert rowa == rowa





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


				__lowerCAmelCase					=  tmp_path / """cache"""
				__lowerCAmelCase					=  os.path.join(UpperCamelCase__						,			"""tmp.csv"""   )
				__lowerCAmelCase					=  CsvDatasetReader({"""train""": csv_path}						,			cache_dir=UpperCamelCase__   ).read()
				with pytest.raises(UpperCamelCase__   ):
								CsvDatasetWriter(dataset["""train"""]						,			UpperCamelCase__						,			num_proc=0   )





