






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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Dict					)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	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}}"""					)
	UpperCamelCase     :List[str]          =	DatasetInfosDict.from_directory(__magic_name__					)
	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  DatasetInfo					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	str(__magic_name__					)
	dataset_info.write_to_directory(__magic_name__					)
	UpperCamelCase     :List[str]          =	DatasetInfo.from_directory(__magic_name__					)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(__magic_name__	,			"""dataset_info.json"""					)					)

def    SCREAMING_SNAKE_CASE_							(							)		->			int:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	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	,			)
	UpperCamelCase     :str          =	dataset_info._to_yaml_dict()
	assert sorted(__magic_name__					) == 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)					)
	UpperCamelCase     :int          =	yaml.safe_dump(__magic_name__					)
	UpperCamelCase     :Dict          =	yaml.safe_load(__magic_name__					)
	assert dataset_info_yaml_dict == reloaded

def    SCREAMING_SNAKE_CASE_							(							)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	DatasetInfo()
	UpperCamelCase     :Tuple          =	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  DatasetInfosDict					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	str(__magic_name__					)
	dataset_infos_dict.write_to_directory(__magic_name__					)
	UpperCamelCase     :int          =	DatasetInfosDict.from_directory(__magic_name__					)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		UpperCamelCase     :int          =	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
		UpperCamelCase     :int          =	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(__magic_name__	,			"""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 ViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		}

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
