"""simple docstring"""


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 MobileNetVaImageProcessor


class        a				(		unittest.TestCase   ):


    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[int]=7   ,				__lowerCAmelCase	:  List[str]=3   ,				__lowerCAmelCase	:  Optional[Any]=18   ,				__lowerCAmelCase	:  List[str]=30   ,				__lowerCAmelCase	:  Any=400   ,				__lowerCAmelCase	:  Optional[int]=True   ,				__lowerCAmelCase	:  Any=None   ,				__lowerCAmelCase	:  List[str]=True   ,				__lowerCAmelCase	:  str=None   ,				):
      _UpperCAmelCase						 =      size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase						 =      crop_size if crop_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_center_crop
      _UpperCAmelCase						 =      crop_size


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }



@require_torch
@require_vision
class        a				(		lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							MobileNetVaImageProcessor if is_vision_available() else None


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      MobileNetVaImageProcessingTester(self					)


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


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""do_resize"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""size"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""do_center_crop"""					)					)
      self.assertTrue(hasattr(__lowerCAmelCase   ,				"""crop_size"""					)					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 20}					)
      self.assertEqual(image_processor.crop_size   ,				{"""height""": 18, """width""": 18}					)

      _UpperCAmelCase						 =      self.image_processing_class.from_dict(self.image_processor_dict   ,				size=42   ,				crop_size=84					)
      self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 42}					)
      self.assertEqual(image_processor.crop_size   ,				{"""height""": 84, """width""": 84}					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      pass


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  Dict					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase   ,				numpify=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)


    def      lowerCAmelCase_  (					self	:  int					):
      # Initialize image_processing
      _UpperCAmelCase						 =      self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      _UpperCAmelCase						 =      prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=__lowerCAmelCase   ,				torchify=__lowerCAmelCase					)
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)

      # Test batched
      _UpperCAmelCase						 =      image_processing(__lowerCAmelCase   ,				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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )   ,				)

"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		lowerCAmelCase_   ):


    def __init__(					self	:  int   ,				*__lowerCAmelCase	:  List[str]   ,				**__lowerCAmelCase	:  List[str]					):
      warnings.warn(
          """The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ChineseCLIPImageProcessor instead."""   ,				__lowerCAmelCase   ,				)
      super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)
