

'''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 PoolFormerImageProcessor




class      a      (					unittest.TestCase							):






						"""simple docstring"""




						def __init__(				self       :   Tuple      ,      snake_case_       :   List[Any]      ,      snake_case_       :   Optional[Any]=7      ,      snake_case_       :   Dict=3      ,      snake_case_       :   Any=3_0      ,      snake_case_       :   List[str]=4_0_0      ,      snake_case_       :   Any=True      ,      snake_case_       :   List[Any]=None      ,      snake_case_       :   int=0.9      ,      snake_case_       :   Tuple=None      ,      snake_case_       :   int=True      ,      snake_case_       :   Union[str, Any]=[0.5, 0.5, 0.5]      ,      snake_case_       :   Optional[Any]=[0.5, 0.5, 0.5]      ,      ):



								'''simple docstring'''



								snake_case__    :							Dict							    =	size if size is not None else {'''shortest_edge''': 3_0}
								snake_case__    :							Dict							    =	crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
								snake_case__    :							int							    =	parent
								snake_case__    :							Dict							    =	batch_size
								snake_case__    :							Tuple							    =	num_channels
								snake_case__    :							Dict							    =	min_resolution
								snake_case__    :							int							    =	max_resolution
								snake_case__    :							Any							    =	do_resize_and_center_crop
								snake_case__    :							Tuple							    =	size
								snake_case__    :							List[Any]							    =	crop_pct
								snake_case__    :							Optional[int]							    =	crop_size
								snake_case__    :							Any							    =	do_normalize
								snake_case__    :							Union[str, Any]							    =	image_mean
								snake_case__    :							Optional[int]							    =	image_std







						def    __magic_name__				(				self       :   str	):



								'''simple docstring'''



								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





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






						"""simple docstring"""




						__UpperCAmelCase        =     PoolFormerImageProcessor if is_vision_available() else None







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



								'''simple docstring'''



								snake_case__    :							List[str]							    =	PoolFormerImageProcessingTester(self	)







						@property
						def    __magic_name__				(				self       :   int	):



								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()







						def    __magic_name__				(				self       :   Dict	):



								'''simple docstring'''



								snake_case__    :							Tuple							    =	self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(snake_case_      ,      '''do_resize_and_center_crop'''	)	)
								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    __magic_name__				(				self       :   Union[str, Any]	):



								'''simple docstring'''



								snake_case__    :							List[str]							    =	self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size      ,      {'''shortest_edge''': 3_0}	)
								self.assertEqual(image_processor.crop_size      ,      {'''height''': 3_0, '''width''': 3_0}	)

								snake_case__    :							List[Any]							    =	self.image_processing_class.from_dict(self.image_processor_dict      ,      size=4_2      ,      crop_size=8_4	)
								self.assertEqual(image_processor.size      ,      {'''shortest_edge''': 4_2}	)
								self.assertEqual(image_processor.crop_size      ,      {'''height''': 8_4, '''width''': 8_4}	)







						def    __magic_name__				(				self       :   Optional[int]	):



								'''simple docstring'''



								pass







						def    __magic_name__				(				self       :   Optional[Any]	):



								'''simple docstring'''



								snake_case__    :							Union[str, Any]							    =	self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								snake_case__    :							List[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
								snake_case__    :							Tuple							    =	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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )

								# Test batched
								snake_case__    :							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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )







						def    __magic_name__				(				self       :   Optional[Any]	):



								'''simple docstring'''



								snake_case__    :							Union[str, Any]							    =	self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								snake_case__    :							Optional[int]							    =	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
								snake_case__    :							Tuple							    =	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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )

								# Test batched
								snake_case__    :							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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )







						def    __magic_name__				(				self       :   Optional[int]	):



								'''simple docstring'''



								snake_case__    :							Dict							    =	self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								snake_case__    :							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
								snake_case__    :							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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )

								# Test batched
								snake_case__    :							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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )      ,      )







import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


A				:       List[str]      =  pytest.mark.integration





@pytest.mark.parametrize("path"   ,						["paws", "csv"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						inspect_dataset(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"   ,						["accuracy"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						inspect_metric(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, config_name, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)
						assert info.config_name == config_name
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, expected"   ,						[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_names(_lowerCAmelCase			)
						assert expected in config_names





@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"   ,						[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert list(infos.keys()			) == expected_configs
						__snake_case				=		expected_configs[0]
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits_in_first_config





@pytest.mark.parametrize(
    "path, expected_config, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_split_names(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





