






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    '''files'''	,     [
        ['''full:README.md''', '''dataset_infos.json'''],
        ['''empty:README.md''', '''dataset_infos.json'''],
        ['''dataset_infos.json'''],
        ['''full:README.md'''],
    ]	,     )
def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    tmp_path_factory.mktemp('''dset_infos_dir'''       )
				if "full:README.md" in files:
								with open(dataset_infos_dir / '''README.md'''	,     '''w'''       ) as f:
												f.write('''---\ndataset_info:\n  dataset_size: 42\n---'''       )
				if "empty:README.md" in files:
								with open(dataset_infos_dir / '''README.md'''	,     '''w'''       ) as f:
												f.write(''''''       )
    # we want to support dataset_infos.json for backward compatibility
				if "dataset_infos.json" in files:
								with open(dataset_infos_dir / '''dataset_infos.json'''	,     '''w'''       ) as f:
												f.write('''{\"default\": {\"dataset_size\": 42}}'''       )
				lowercase__     				=    DatasetInfosDict.from_directory(lowerCamelCase__       )
				assert dataset_infos
				assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    '''dataset_info'''	,     [
        DatasetInfo(),
        DatasetInfo(
            description='''foo'''	,     features=Features({'''a''': Value('''int32'''       )}       )	,     builder_name='''builder'''	,     config_name='''config'''	,     version='''1.0.0'''	,     splits=[{'''name''': '''train'''}]	,     download_size=42	,     ),
    ]	,     )
def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    str(lowerCamelCase__       )
				dataset_info.write_to_directory(lowerCamelCase__       )
				lowercase__     				=    DatasetInfo.from_directory(lowerCamelCase__       )
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(lowerCamelCase__	,     '''dataset_info.json'''       )       )


def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    DatasetInfo(
				    description='''foo'''	,     citation='''bar'''	,     homepage='''https://foo.bar'''	,     license='''CC0'''	,     features=Features({'''a''': Value('''int32'''       )}       )	,     post_processed={}	,     supervised_keys=()	,     task_templates=[]	,     builder_name='''builder'''	,     config_name='''config'''	,     version='''1.0.0'''	,     splits=[{'''name''': '''train''', '''num_examples''': 42}]	,     download_checksums={}	,     download_size=1337	,     post_processing_size=442	,     dataset_size=1234	,     size_in_bytes=1337 + 442 + 1234	,     )
				lowercase__     				=    dataset_info._to_yaml_dict()
				assert sorted(lowerCamelCase__       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								assert key in dataset_info_yaml_dict
								assert isinstance(dataset_info_yaml_dict[key]	,     (list, dict, int, str)       )
				lowercase__     				=    yaml.safe_dump(lowerCamelCase__       )
				lowercase__     				=    yaml.safe_load(lowerCamelCase__       )
				assert dataset_info_yaml_dict == reloaded


def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    DatasetInfo()
				lowercase__     				=    dataset_info._to_yaml_dict()
				assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    '''dataset_infos_dict'''	,     [
        DatasetInfosDict(),
        DatasetInfosDict({'''default''': DatasetInfo()}       ),
        DatasetInfosDict({'''my_config_name''': DatasetInfo()}       ),
        DatasetInfosDict(
            {
                '''default''': DatasetInfo(
                    description='''foo'''	,     features=Features({'''a''': Value('''int32'''       )}       )	,     builder_name='''builder'''	,     config_name='''config'''	,     version='''1.0.0'''	,     splits=[{'''name''': '''train'''}]	,     download_size=42	,     )
            }       ),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42       ),
                '''v2''': DatasetInfo(dataset_size=1337       ),
            }       ),
    ]	,     )
def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    str(lowerCamelCase__       )
				dataset_infos_dict.write_to_directory(lowerCamelCase__       )
				lowercase__     				=    DatasetInfosDict.from_directory(lowerCamelCase__       )

				# the config_name of the dataset_infos_dict take over the attribute
				for config_name, dataset_info in dataset_infos_dict.items():
								lowercase__     				=    config_name
								# the yaml representation doesn't include fields like description or citation
								# so we just test that we can recover what we can from the yaml
								lowercase__     				=    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
				assert dataset_infos_dict == reloaded

				if dataset_infos_dict:
								assert os.path.exists(os.path.join(lowerCamelCase__	,     '''README.md'''       )       )












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 MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 20}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 18, '''width''': 18}						)

								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 84, '''width''': 84}						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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'''],
								    ),						)




