


'''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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=7  ,							snake_case=3  ,							snake_case=1_8  ,							snake_case=3_0  ,							snake_case=4_0_0  ,							snake_case=True  ,							snake_case=None  ,							snake_case=True  ,							snake_case=None  ,							snake_case=True  ,							snake_case=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,							snake_case=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,							snake_case=True  ,							)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase       :       Dict							   =crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
          _UpperCAmelCase       :       Optional[int]							   =parent
          _UpperCAmelCase       :       List[str]							   =batch_size
          _UpperCAmelCase       :       Union[str, Any]							   =num_channels
          _UpperCAmelCase       :       List[Any]							   =image_size
          _UpperCAmelCase       :       List[Any]							   =min_resolution
          _UpperCAmelCase       :       List[str]							   =max_resolution
          _UpperCAmelCase       :       List[str]							   =do_resize
          _UpperCAmelCase       :       Any							   =size
          _UpperCAmelCase       :       Any							   =do_center_crop
          _UpperCAmelCase       :       List[str]							   =crop_size
          _UpperCAmelCase       :       Optional[Any]							   =do_normalize
          _UpperCAmelCase       :       List[Any]							   =image_mean
          _UpperCAmelCase       :       Optional[Any]							   =image_std
          _UpperCAmelCase       :       Optional[int]							   =do_convert_rgb






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }






       def       lowerCAmelCase		(      self  ,							snake_case=False  ,							snake_case=False  ,							snake_case=False)						->  Optional[int]:




          '''simple docstring'''
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
             _UpperCAmelCase       :       int							   =[]
             for i in range(self.batch_size):
                image_inputs.append(
                    np.random.randint(
                        2_5_5  ,							size=(self.num_channels, self.max_resolution, self.max_resolution)  ,							dtype=np.uinta))
          else:
             _UpperCAmelCase       :       str							   =[]
             for i in range(self.batch_size):
                _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =np.random.choice(np.arange(self.min_resolution  ,							self.max_resolution)  ,							2)
                image_inputs.append(np.random.randint(2_5_5  ,							size=(self.num_channels, width, height)  ,							dtype=np.uinta))

          if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             _UpperCAmelCase       :       Dict							   =[Image.fromarray(np.moveaxis(snake_case  ,							0  ,							-1)) for x in image_inputs]

          if torchify:
             _UpperCAmelCase       :       Dict							   =[torch.from_numpy(snake_case) for x in image_inputs]

          return image_inputs






@require_torch
@require_vision
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=ChineseCLIPImageProcessor if is_vision_available() else None






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ChineseCLIPImageProcessingTester(self  ,							do_center_crop=snake_case)






       @property
       def       lowerCAmelCase		(      self)						->  Tuple:




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






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case  ,							'do_resize'))
          self.assertTrue(hasattr(snake_case  ,							'size'))
          self.assertTrue(hasattr(snake_case  ,							'do_center_crop'))
          self.assertTrue(hasattr(snake_case  ,							'center_crop'))
          self.assertTrue(hasattr(snake_case  ,							'do_normalize'))
          self.assertTrue(hasattr(snake_case  ,							'image_mean'))
          self.assertTrue(hasattr(snake_case  ,							'image_std'))
          self.assertTrue(hasattr(snake_case  ,							'do_convert_rgb'))






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size  ,							{'height': 2_2_4, 'width': 2_2_4})
          self.assertEqual(image_processor.crop_size  ,							{'height': 1_8, 'width': 1_8})

          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processing_class.from_dict(self.image_processor_dict  ,							size=4_2  ,							crop_size=8_4)
          self.assertEqual(image_processor.size  ,							{'shortest_edge': 4_2})
          self.assertEqual(image_processor.crop_size  ,							{'height': 8_4, 'width': 8_4})






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       Any							   =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase       :       Tuple							   =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							Image.Image)

          # Test not batched input
          _UpperCAmelCase       :       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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       Optional[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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       Optional[Any]							   =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _UpperCAmelCase       :       Tuple							   =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case  ,							numpify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							np.ndarray)

          # Test not batched input
          _UpperCAmelCase       :       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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       Optional[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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       str							   =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case  ,							torchify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							torch.Tensor)

          # Test not batched input
          _UpperCAmelCase       :       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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)






@require_torch
@require_vision
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=ChineseCLIPImageProcessor if is_vision_available() else None






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =ChineseCLIPImageProcessingTester(self  ,							num_channels=4  ,							do_center_crop=snake_case)
          _UpperCAmelCase       :       Any							   =3






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




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






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case  ,							'do_resize'))
          self.assertTrue(hasattr(snake_case  ,							'size'))
          self.assertTrue(hasattr(snake_case  ,							'do_center_crop'))
          self.assertTrue(hasattr(snake_case  ,							'center_crop'))
          self.assertTrue(hasattr(snake_case  ,							'do_normalize'))
          self.assertTrue(hasattr(snake_case  ,							'image_mean'))
          self.assertTrue(hasattr(snake_case  ,							'image_std'))
          self.assertTrue(hasattr(snake_case  ,							'do_convert_rgb'))






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       List[Any]							   =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase       :       Optional[int]							   =self.image_processor_tester.prepare_inputs(equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							Image.Image)

          # Test not batched input
          _UpperCAmelCase       :       Optional[int]							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)

          # Test batched
          _UpperCAmelCase       :       Tuple							   =image_processing(snake_case  ,							return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape  ,							(
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )  ,							)





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="glpn"






       def __init__(      self  ,							snake_case=3  ,							snake_case=4  ,							snake_case=[2, 2, 2, 2]  ,							snake_case=[8, 4, 2, 1]  ,							snake_case=[3_2, 6_4, 1_6_0, 2_5_6]  ,							snake_case=[7, 3, 3, 3]  ,							snake_case=[4, 2, 2, 2]  ,							snake_case=[1, 2, 5, 8]  ,							snake_case=[4, 4, 4, 4]  ,							snake_case="gelu"  ,							snake_case=0.0  ,							snake_case=0.0  ,							snake_case=0.02  ,							snake_case=0.1  ,							snake_case=1E-6  ,							snake_case=6_4  ,							snake_case=1_0  ,							snake_case=-1  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(**snake_case)

          _UpperCAmelCase       :       Any							   =num_channels
          _UpperCAmelCase       :       List[str]							   =num_encoder_blocks
          _UpperCAmelCase       :       Optional[Any]							   =depths
          _UpperCAmelCase       :       str							   =sr_ratios
          _UpperCAmelCase       :       Dict							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =patch_sizes
          _UpperCAmelCase       :       Any							   =strides
          _UpperCAmelCase       :       List[str]							   =mlp_ratios
          _UpperCAmelCase       :       Dict							   =num_attention_heads
          _UpperCAmelCase       :       List[str]							   =hidden_act
          _UpperCAmelCase       :       int							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[Any]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Union[str, Any]							   =initializer_range
          _UpperCAmelCase       :       Tuple							   =drop_path_rate
          _UpperCAmelCase       :       str							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[int]							   =decoder_hidden_size
          _UpperCAmelCase       :       List[str]							   =max_depth
          _UpperCAmelCase       :       Dict							   =head_in_index

