



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       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   def __init__(    self:     Any  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Optional[int]=7  ,lowerCamelCase_:     List[Any]=3  ,lowerCamelCase_:     Dict=18  ,lowerCamelCase_:     Dict=30  ,lowerCamelCase_:     Optional[int]=400  ,lowerCamelCase_:     Tuple=True  ,lowerCamelCase_:     Tuple=None  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     int=[0.5, 0.5, 0.5]  ,lowerCamelCase_:     int=[0.5, 0.5, 0.5]  ,)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					size if size is not None else {"""height""": 18, """width""": 18}
       UpperCAmelCase_     :       Dict  		=					parent
       UpperCAmelCase_     :       List[str]  		=					batch_size
       UpperCAmelCase_     :       Union[str, Any]  		=					num_channels
       UpperCAmelCase_     :       str  		=					image_size
       UpperCAmelCase_     :       Union[str, Any]  		=					min_resolution
       UpperCAmelCase_     :       int  		=					max_resolution
       UpperCAmelCase_     :       Optional[int]  		=					do_resize
       UpperCAmelCase_     :       Any  		=					size
       UpperCAmelCase_     :       List[str]  		=					do_normalize
       UpperCAmelCase_     :       Optional[int]  		=					image_mean
       UpperCAmelCase_     :       Tuple  		=					image_std






   def       A__    (    self:     List[Any]							)	->      Any:
       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       _snake_case				(     UpperCAmelCase__ ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : Dict       =						ViTImageProcessor if is_vision_available() else None


   def       A__    (    self:     List[str]							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[int]  		=					ViTImageProcessingTester(self							)


   @property
   def       A__    (    self:     int							)	->      Any:
       return self.image_processor_tester.prepare_image_processor_dict()


   def       A__    (    self:     str							)	->      Tuple:
       UpperCAmelCase_     :       List[str]  		=					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       A__    (    self:     List[str]							)	->      Optional[int]:
       UpperCAmelCase_     :       Any  		=					self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size  ,{"""height""": 18, """width""": 18}							)

       UpperCAmelCase_     :       List[str]  		=					self.image_processing_class.from_dict(self.image_processor_dict  ,size=42							)
       self.assertEqual(image_processor.size  ,{"""height""": 42, """width""": 42}							)


   def       A__    (    self:     Tuple							)	->      Any:
       pass


   def       A__    (    self:     str							)	->      Tuple:
       UpperCAmelCase_     :       List[Any]  		=					self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCAmelCase_     :       Optional[int]  		=					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_     :       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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )  ,)

       # Test batched
       UpperCAmelCase_     :       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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )  ,)


   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Dict  		=					self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       UpperCAmelCase_     :       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
       UpperCAmelCase_     :       Union[str, Any]  		=					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_     :       Union[str, 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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )  ,)






   def       A__    (    self:     List[Any]							)	->      Dict:
       UpperCAmelCase_     :       str  		=					self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       UpperCAmelCase_     :       Any  		=					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_     :       int  		=					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_     :       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.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       ,       )




