






"""simple docstring"""







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


lowerCAmelCase_		:			Union[str, Any]				   =					logging.get_logger(__name__)





class      UpperCamelCase_       (				a_     ):
  _A				:    Dict =		['pixel_values']



  def __init__(					self       ,						snake_case__ = True       ,						snake_case__ = None       ,						snake_case__ = PIL.Image.NEAREST       ,						snake_case__ = False       ,						snake_case__ = None       ,						snake_case__ = 1 / 2_55       ,						snake_case__ = False       ,						snake_case__ = True       ,						snake_case__ = True       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = True       ,						**snake_case__       ,						)	->	None:



         """simple docstring"""





         super().__init__(**snake_case__ )
         UpperCAmelCase					=	size if size is not None else {"""height""": 3_46, """width""": 3_46}
         UpperCAmelCase					=	get_size_dict(snake_case__ )
         UpperCAmelCase					=	crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         UpperCAmelCase					=	get_size_dict(snake_case__       ,						param_name="""crop_size""" )

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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = PIL.Image.NEAREST       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(
             snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						resample=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = True       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__=None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = ChannelDimension.FIRST       ,						**snake_case__       ,						)	->	PIL.Image.Image:



         """simple docstring"""





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

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

         UpperCAmelCase					=	make_list_of_images(snake_case__ )

         if not valid_images(snake_case__ ):
                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.
         UpperCAmelCase					=	[to_numpy_array(snake_case__ ) for image in images]

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

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

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

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

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

         UpperCAmelCase					=	[to_channel_dimension_format(snake_case__       ,						snake_case__ ) for image in images]

         UpperCAmelCase					=	{"""pixel_values""": images}
         return BatchFeature(data=snake_case__       ,						tensor_type=snake_case__ )








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        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       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        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			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
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(UpperCAmelCase_    ,       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  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(UpperCAmelCase_    ,       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  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       torchify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(UpperCAmelCase_    ,       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"],
           )    ,       )






