




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        __lowerCAmelCase					(			unittest.TestCase					):







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




									"""simple docstring"""


									_UpperCAmelCase						=	size if size is not None else {"height": 18, "width": 18}
									_UpperCAmelCase						=	parent
									_UpperCAmelCase						=	batch_size
									_UpperCAmelCase						=	num_channels
									_UpperCAmelCase						=	image_size
									_UpperCAmelCase						=	min_resolution
									_UpperCAmelCase						=	max_resolution
									_UpperCAmelCase						=	do_resize
									_UpperCAmelCase						=	size
									_UpperCAmelCase						=	do_normalize
									_UpperCAmelCase						=	image_mean
									_UpperCAmelCase						=	image_std







			def UpperCamelCase     (			self       :     List[Any] ):




									"""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,
									}






@require_torch
@require_vision
class        __lowerCAmelCase					(			UpperCAmelCase__   ,						unittest.TestCase					):
			snake_case_ :					Dict						=	ViTImageProcessor if is_vision_available() else None







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	ViTImageProcessingTester(self )







			@property
			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									return self.image_processor_tester.prepare_image_processor_dict()







			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(snake_case__       ,       "image_mean" ) )
									self.assertTrue(hasattr(snake_case__       ,       "image_std" ) )
									self.assertTrue(hasattr(snake_case__       ,       "do_normalize" ) )
									self.assertTrue(hasattr(snake_case__       ,       "do_resize" ) )
									self.assertTrue(hasattr(snake_case__       ,       "size" ) )







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size       ,       {"height": 18, "width": 18} )

									_UpperCAmelCase						=	self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42 )
									self.assertEqual(image_processor.size       ,       {"height": 42, "width": 42} )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass







			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									_UpperCAmelCase						=	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
									_UpperCAmelCase						=	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["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )

									# Test batched
									_UpperCAmelCase						=	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.size["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									_UpperCAmelCase						=	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
									_UpperCAmelCase						=	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["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )

									# Test batched
									_UpperCAmelCase						=	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.size["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )







			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									_UpperCAmelCase						=	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
									_UpperCAmelCase						=	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["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )

									# Test batched
									_UpperCAmelCase						=	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.size["height"],
									        self.image_processor_tester.size["width"],
									    )       ,       )










import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowercase_      :							Optional[int]    							=					set()
lowercase_      :							List[Any]    							=					get_logger(__name__)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_ = None		):

						'''simple docstring'''


						def decorator(snake_case_		):
												global _emitted_deprecation_warnings

												if inspect.isclass(snake_case_		):
																		_UpperCAmelCase						=	deprecated_class_or_function.__init__
																		_UpperCAmelCase						=	deprecated_class_or_function.__name__
												else:
																		_UpperCAmelCase						=	deprecated_class_or_function
																		_UpperCAmelCase						=	deprecated_function.__name__
																		# Support deprecating __init__ class method: class name instead
																		_UpperCAmelCase						=	name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

												_UpperCAmelCase						=	(
												    f"""{name} is deprecated and will be removed in the next major version of datasets.""" + f""" {help_message}"""
												    if help_message
												    else ""
												)

												@wraps(snake_case_		)
												def wrapper(*snake_case_      ,       **snake_case_		):
																		_UpperCAmelCase						=	hash(snake_case_		)
																		if func_hash not in _emitted_deprecation_warnings:
																								warnings.warn(snake_case_      ,       category=snake_case_      ,       stacklevel=2		)
																								_emitted_deprecation_warnings.add(snake_case_		)
																		return deprecated_function(*snake_case_      ,       **snake_case_		)

												_UpperCAmelCase						=	"deprecated"

												if inspect.isclass(snake_case_		):
																		_UpperCAmelCase						=	wrapper
																		return deprecated_class_or_function
												else:
																		return wrapper

						return decorator






class        __lowerCAmelCase					(			enum.EnumMeta					):







			def __getattribute__(			cls       :     str       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__getattribute__(snake_case__ )
									if isinstance(snake_case__       ,       enum.Enum ) and obj._on_access:
															obj._on_access()
									return obj







			def __getitem__(			cls       :     str       ,       snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__getitem__(snake_case__ )
									if member._on_access:
															member._on_access()
									return member







			def __call__(			cls       :     Dict       ,       snake_case__       :     Any       ,       snake_case__       :     List[str]=None       ,       *, snake_case__       :     Optional[int]=None       ,       snake_case__       :     List[Any]=None       ,       snake_case__       :     Tuple=None       ,       snake_case__       :     List[Any]=1 ):




									"""simple docstring"""


									_UpperCAmelCase						=	super().__call__(snake_case__       ,       snake_case__       ,       module=snake_case__       ,       qualname=snake_case__       ,       type=snake_case__       ,       start=snake_case__ )
									if isinstance(snake_case__       ,       enum.Enum ) and obj._on_access:
															obj._on_access()
									return obj






class        __lowerCAmelCase					(			enum.Enum   ,						metaclass=UpperCAmelCase__					):







			def __new__(			cls       :     Any       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	object.__new__(cls )
									_UpperCAmelCase						=	value
									_UpperCAmelCase						=	member.deprecate
									return member







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									return ""







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	F""" {self.help_message}""" if self.help_message else ""
									warnings.warn(
									    F"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
									    + help_message       ,       snake_case__       ,       stacklevel=3       ,       )




