





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_   =   logging.get_logger(__name__)

class 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							["pixel_values"]



      def __init__(					self     ,  lowercase_ = True     ,  lowercase_ = None     ,  lowercase_ = PIL.Image.NEAREST     ,  lowercase_ = False     ,  lowercase_ = None     ,  lowercase_ = 1 / 255     ,  lowercase_ = False     ,  lowercase_ = True     ,  lowercase_ = True     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = True     ,  **lowercase_     ,  )      ->   Union[str, Any]:



             '''simple docstring'''

             super().__init__(**UpperCamelCase_					)
             _snake_case      :							int     				=						size if size is not None else {'''height''': 346, '''width''': 346}
             _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_					)
             _snake_case      :							List[Any]     				=						crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
             _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_     ,  param_name='''crop_size'''					)

             _snake_case      :							str     				=						do_resize
             _snake_case      :							Optional[Any]     				=						size
             _snake_case      :							Optional[int]     				=						resample
             _snake_case      :							str     				=						do_center_crop
             _snake_case      :							Optional[Any]     				=						crop_size
             _snake_case      :							str     				=						do_rescale
             _snake_case      :							str     				=						rescale_factor
             _snake_case      :							Dict     				=						rescale_offset
             _snake_case      :							List[str]     				=						do_normalize
             _snake_case      :							int     				=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _snake_case      :							Optional[int]     				=						image_std if image_std is not None else IMAGENET_STANDARD_STD
             _snake_case      :							List[str]     				=						include_top



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = PIL.Image.NEAREST     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_					)
             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(
                 UpperCamelCase_     ,  size=(size['''height'''], size['''width'''])     ,  resample=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							int     				=						get_size_dict(UpperCamelCase_					)
             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(UpperCamelCase_     ,  size=(size['''height'''], size['''width'''])     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = True     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   Union[str, Any]:



             '''simple docstring'''

             if offset:
                    _snake_case      :							str     				=						(image - 127.5) * scale
                    if data_format is not None:
                           _snake_case      :							Any     				=						to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					)
                    _snake_case      :							Dict     				=						rescaled_image.astype(np.floataa					)
             else:
                    _snake_case      :							Any     				=						rescale(UpperCamelCase_     ,  scale=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
             return rescaled_image



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   str:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_=None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = ChannelDimension.FIRST     ,  **lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						do_resize if do_resize is not None else self.do_resize
             _snake_case      :							int     				=						resample if resample is not None else self.resample
             _snake_case      :							Any     				=						do_center_crop if do_center_crop is not None else self.do_center_crop
             _snake_case      :							List[str]     				=						do_rescale if do_rescale is not None else self.do_rescale
             _snake_case      :							Optional[Any]     				=						rescale_factor if rescale_factor is not None else self.rescale_factor
             _snake_case      :							List[str]     				=						rescale_offset if rescale_offset is not None else self.rescale_offset
             _snake_case      :							Optional[int]     				=						do_normalize if do_normalize is not None else self.do_normalize
             _snake_case      :							List[str]     				=						image_mean if image_mean is not None else self.image_mean
             _snake_case      :							int     				=						image_std if image_std is not None else self.image_std
             _snake_case      :							List[Any]     				=						include_top if include_top is not None else self.include_top

             _snake_case      :							Any     				=						size if size is not None else self.size
             _snake_case      :							List[Any]     				=						get_size_dict(UpperCamelCase_					)
             _snake_case      :							Optional[Any]     				=						crop_size if crop_size is not None else self.crop_size
             _snake_case      :							Optional[Any]     				=						get_size_dict(UpperCamelCase_     ,  param_name='''crop_size'''					)

             _snake_case      :							Any     				=						make_list_of_images(UpperCamelCase_					)

             if not valid_images(UpperCamelCase_					):
                    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.
             _snake_case      :							Optional[Any]     				=						[to_numpy_array(UpperCamelCase_					) for image in images]

             if do_resize:
                    _snake_case      :							Union[str, Any]     				=						[self.resize(image=UpperCamelCase_     ,  size=UpperCamelCase_     ,  resample=UpperCamelCase_					) for image in images]

             if do_center_crop:
                    _snake_case      :							Dict     				=						[self.center_crop(image=UpperCamelCase_     ,  size=UpperCamelCase_					) for image in images]

             if do_rescale:
                    _snake_case      :							Union[str, Any]     				=						[self.rescale(image=UpperCamelCase_     ,  scale=UpperCamelCase_     ,  offset=UpperCamelCase_					) for image in images]

             if do_normalize:
                    _snake_case      :							Dict     				=						[self.normalize(image=UpperCamelCase_     ,  mean=UpperCamelCase_     ,  std=UpperCamelCase_					) for image in images]

             if include_top:
                    _snake_case      :							int     				=						[self.normalize(image=UpperCamelCase_     ,  mean=[0, 0, 0]     ,  std=UpperCamelCase_					) for image in images]

             _snake_case      :							Dict     				=						[to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					) for image in images]

             _snake_case      :							List[str]     				=						{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase_     ,  tensor_type=UpperCamelCase_					)













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 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
             _snake_case      :							List[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							str     				=						num_channels
             _snake_case      :							Union[str, Any]     				=						image_size
             _snake_case      :							Optional[Any]     				=						min_resolution
             _snake_case      :							Union[str, Any]     				=						max_resolution
             _snake_case      :							Tuple     				=						do_resize
             _snake_case      :							List[Any]     				=						size
             _snake_case      :							Optional[int]     				=						crop_pct
             _snake_case      :							Any     				=						do_normalize
             _snake_case      :							Optional[int]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std



      def    __a (					self					)      ->   str:



             '''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 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						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    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)

             _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _snake_case      :							List[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
             _snake_case      :							int     				=						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    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						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
             _snake_case      :							List[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
             _snake_case      :							Union[str, Any]     				=						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    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							Dict     				=						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
             _snake_case      :							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['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							List[Any]     				=						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'''],
                 )     ,  )






