




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 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		Dict,     _lowercase:		Optional[int]=7,     _lowercase:		Union[str, Any]=3,     _lowercase:		List[Any]=18,     _lowercase:		Optional[int]=30,     _lowercase:		Dict=400,     _lowercase:		List[str]=True,     _lowercase:		Optional[int]=None,     _lowercase:		Tuple=0.875,     _lowercase:		List[str]=True,     _lowercase:		Union[str, Any]=[0.5, 0.5, 0.5],     _lowercase:		Optional[int]=[0.5, 0.5, 0.5],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  size if size is not None else {"""shortest_edge""": 20}
									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  min_resolution
									__lowerCAmelCase					=  max_resolution
									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  crop_pct
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean
									__lowerCAmelCase					=  image_std







					def     _lowercase						(   self:		Tuple):


									'''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,
									    "crop_pct": self.crop_pct,
									}





@require_torch
@require_vision
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= ConvNextImageProcessor if is_vision_available() else None




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  ConvNextImageProcessingTester(self)




					@property
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(_lowercase,     """do_resize"""))
									self.assertTrue(hasattr(_lowercase,     """size"""))
									self.assertTrue(hasattr(_lowercase,     """crop_pct"""))
									self.assertTrue(hasattr(_lowercase,     """do_normalize"""))
									self.assertTrue(hasattr(_lowercase,     """image_mean"""))
									self.assertTrue(hasattr(_lowercase,     """image_std"""))




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size,     {"""shortest_edge""": 20})

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




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		int):


									'''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=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     Image.Image)

									# Test not batched input
									__lowerCAmelCase					=  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					=  image_processing(_lowercase,     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     _lowercase						(   self:		Union[str, Any]):


									'''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=_lowercase,     numpify=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     np.ndarray)

									# Test not batched input
									__lowerCAmelCase					=  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					=  image_processing(_lowercase,     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     _lowercase						(   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=_lowercase,     torchify=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     torch.Tensor)

									# Test not batched input
									__lowerCAmelCase					=  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					=  image_processing(_lowercase,     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"""],
									    ),     )











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   )





