




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 ConvNextImageProcessor







class 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     def __init__(   self			:							Dict    ,					A			:							Dict    ,					A			:							Optional[int]=7    ,					A			:							Union[str, Any]=3    ,					A			:							List[Any]=18    ,					A			:							Optional[int]=30    ,					A			:							Dict=4_00    ,					A			:							List[str]=True    ,					A			:							Optional[int]=None    ,					A			:							Tuple=0.8_7_5    ,					A			:							List[str]=True    ,					A			:							Union[str, Any]=[0.5, 0.5, 0.5]    ,					A			:							Optional[int]=[0.5, 0.5, 0.5]    ,					)      ->      Optional[int]:






         """simple docstring"""
         a__  			=							size if size is not None else {"""shortest_edge""": 20}
         a__  			=							parent
         a__  			=							batch_size
         a__  			=							num_channels
         a__  			=							image_size
         a__  			=							min_resolution
         a__  			=							max_resolution
         a__  			=							do_resize
         a__  			=							size
         a__  			=							crop_pct
         a__  			=							do_normalize
         a__  			=							image_mean
         a__  			=							image_std






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      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,
             "crop_pct": self.crop_pct,
         }







@require_torch
@require_vision
class 		__UpperCAmelCase(		lowerCAmelCase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Union[str, Any]							=     ConvNextImageProcessor if is_vision_available() else None

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Tuple:






         """simple docstring"""
         a__  			=							ConvNextImageProcessingTester(self     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Optional[int]:






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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      List[Any]:






         """simple docstring"""
         a__  			=							self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_lowercase    ,					"""do_resize"""     )     )
         self.assertTrue(hasattr(_lowercase    ,					"""size"""     )     )
         self.assertTrue(hasattr(_lowercase    ,					"""crop_pct"""     )     )
         self.assertTrue(hasattr(_lowercase    ,					"""do_normalize"""     )     )
         self.assertTrue(hasattr(_lowercase    ,					"""image_mean"""     )     )
         self.assertTrue(hasattr(_lowercase    ,					"""image_std"""     )     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}     )

         a__  			=							self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42     )
         self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[Any]:






         """simple docstring"""
         pass

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         a__  			=							prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase    ,					Image.Image     )

         # Test not batched input
         a__  			=							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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)

         # Test batched
         a__  			=							image_processing(_lowercase    ,					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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      str:






         """simple docstring"""
         a__  			=							self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         a__  			=							prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_lowercase    ,					numpify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase    ,					np.ndarray     )

         # Test not batched input
         a__  			=							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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)

         # Test batched
         a__  			=							image_processing(_lowercase    ,					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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)






     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      int:






         """simple docstring"""
         a__  			=							self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         a__  			=							prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_lowercase    ,					torchify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase    ,					torch.Tensor     )

         # Test not batched input
         a__  			=							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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)

         # Test batched
         a__  			=							image_processing(_lowercase    ,					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["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )    ,					)












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


__A						:		Dict          =						logging.get_logger(__name__)

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= ["pixel_values"]




					def __init__(   self:		str,     _lowercase:		bool = True,     _lowercase:		Dict[str, int] = None,     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		bool = False,     _lowercase:		Dict[str, int] = None,     _lowercase:		Union[int, float] = 1 / 255,     _lowercase:		bool = False,     _lowercase:		bool = True,     _lowercase:		bool = True,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = True,     **_lowercase:		Union[str, Any],     ):


									'''simple docstring'''


									super().__init__(**_lowercase)
									__lowerCAmelCase					=  size if size is not None else {"""height""": 346, """width""": 346}
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  resample
									__lowerCAmelCase					=  do_center_crop
									__lowerCAmelCase					=  crop_size
									__lowerCAmelCase					=  do_rescale
									__lowerCAmelCase					=  rescale_factor
									__lowerCAmelCase					=  rescale_offset
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__lowerCAmelCase					=  image_std if image_std is not None else IMAGENET_STANDARD_STD
									__lowerCAmelCase					=  include_top




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		PILImageResampling = PIL.Image.NEAREST,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return resize(
									    _lowercase,     size=(size["""height"""], size["""width"""]),     resample=_lowercase,     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		List[str],     _lowercase:		np.ndarray,     _lowercase:		Dict[str, int],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  get_size_dict(_lowercase)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''')
									return center_crop(_lowercase,     size=(size["""height"""], size["""width"""]),     data_format=_lowercase,     **_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		np.ndarray,     _lowercase:		Union[int, float],     _lowercase:		bool = True,     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Dict,     ):


									'''simple docstring'''


									if offset:
													__lowerCAmelCase					=  (image - 127.5) * scale
													if data_format is not None:
																	__lowerCAmelCase					=  to_channel_dimension_format(_lowercase,     _lowercase)
													__lowerCAmelCase					=  rescaled_image.astype(np.floataa)
									else:
													__lowerCAmelCase					=  rescale(_lowercase,     scale=_lowercase,     data_format=_lowercase,     **_lowercase)
									return rescaled_image




					def     _lowercase						(   self:		Dict,     _lowercase:		np.ndarray,     _lowercase:		Union[float, List[float]],     _lowercase:		Union[float, List[float]],     _lowercase:		Optional[Union[str, ChannelDimension]] = None,     **_lowercase:		Any,     ):


									'''simple docstring'''


									return normalize(_lowercase,     mean=_lowercase,     std=_lowercase,     data_format=_lowercase,     **_lowercase)







					def     _lowercase						(   self:		Dict,     _lowercase:		ImageInput,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		str=None,     _lowercase:		bool = None,     _lowercase:		Dict[str, int] = None,     _lowercase:		bool = None,     _lowercase:		float = None,     _lowercase:		bool = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		Optional[Union[float, List[float]]] = None,     _lowercase:		bool = None,     _lowercase:		Optional[Union[str, TensorType]] = None,     _lowercase:		ChannelDimension = ChannelDimension.FIRST,     **_lowercase:		Tuple,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  do_resize if do_resize is not None else self.do_resize
									__lowerCAmelCase					=  resample if resample is not None else self.resample
									__lowerCAmelCase					=  do_center_crop if do_center_crop is not None else self.do_center_crop
									__lowerCAmelCase					=  do_rescale if do_rescale is not None else self.do_rescale
									__lowerCAmelCase					=  rescale_factor if rescale_factor is not None else self.rescale_factor
									__lowerCAmelCase					=  rescale_offset if rescale_offset is not None else self.rescale_offset
									__lowerCAmelCase					=  do_normalize if do_normalize is not None else self.do_normalize
									__lowerCAmelCase					=  image_mean if image_mean is not None else self.image_mean
									__lowerCAmelCase					=  image_std if image_std is not None else self.image_std
									__lowerCAmelCase					=  include_top if include_top is not None else self.include_top

									__lowerCAmelCase					=  size if size is not None else self.size
									__lowerCAmelCase					=  get_size_dict(_lowercase)
									__lowerCAmelCase					=  crop_size if crop_size is not None else self.crop_size
									__lowerCAmelCase					=  get_size_dict(_lowercase,     param_name="""crop_size""")

									__lowerCAmelCase					=  make_list_of_images(_lowercase)

									if not valid_images(_lowercase):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray.""")

									if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True.""")

									if do_center_crop and crop_size is None:
													raise ValueError("""Crop size must be specified if do_center_crop is True.""")

									if do_rescale and rescale_factor is None:
													raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

									# All transformations expect numpy arrays.
									__lowerCAmelCase					=  [to_numpy_array(_lowercase) for image in images]

									if do_resize:
													__lowerCAmelCase					=  [self.resize(image=_lowercase,     size=_lowercase,     resample=_lowercase) for image in images]

									if do_center_crop:
													__lowerCAmelCase					=  [self.center_crop(image=_lowercase,     size=_lowercase) for image in images]

									if do_rescale:
													__lowerCAmelCase					=  [self.rescale(image=_lowercase,     scale=_lowercase,     offset=_lowercase) for image in images]

									if do_normalize:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=_lowercase,     std=_lowercase) for image in images]

									if include_top:
													__lowerCAmelCase					=  [self.normalize(image=_lowercase,     mean=[0, 0, 0],     std=_lowercase) for image in images]

									__lowerCAmelCase					=  [to_channel_dimension_format(_lowercase,     _lowercase) for image in images]

									__lowerCAmelCase					=  {"""pixel_values""": images}
									return BatchFeature(data=_lowercase,     tensor_type=_lowercase)





