






'''simple docstring'''






import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          FocalNetBackbone,
          FocalNetForImageClassification,
          FocalNetForMaskedImageModeling,
          FocalNetModel,
      )
      from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor




class a_    :







   def __init__(self					,   __a					,   __a=1_3					,   __a=3_2					,   __a=2					,   __a=3					,   __a=1_6					,   __a=[3_2, 6_4, 1_2_8]					,   __a=[1, 2, 1]					,   __a=[2, 2, 4]					,   __a=2					,   __a=2.0					,   __a=True					,   __a=0.0					,   __a=0.0					,   __a=0.1					,   __a="gelu"					,   __a=False					,   __a=True					,   __a=0.02					,   __a=1E-5					,   __a=True					,   __a=None					,   __a=True					,   __a=1_0					,   __a=8					,   __a=["stage1", "stage2"]					,   __a=[1, 2]					,   )			->      Dict:
      """simple docstring"""






      __snake_case				:      List[str]     							= parent
      __snake_case				:      Dict     							= batch_size
      __snake_case				:      Dict     							= image_size
      __snake_case				:      List[str]     							= patch_size
      __snake_case				:      int     							= num_channels
      __snake_case				:      Dict     							= embed_dim
      __snake_case				:      Dict     							= hidden_sizes
      __snake_case				:      Union[str, Any]     							= depths
      __snake_case				:      str     							= num_heads
      __snake_case				:      Optional[int]     							= window_size
      __snake_case				:      Dict     							= mlp_ratio
      __snake_case				:      Dict     							= qkv_bias
      __snake_case				:      Tuple     							= hidden_dropout_prob
      __snake_case				:      int     							= attention_probs_dropout_prob
      __snake_case				:      Tuple     							= drop_path_rate
      __snake_case				:      Dict     							= hidden_act
      __snake_case				:      Optional[int]     							= use_absolute_embeddings
      __snake_case				:      Dict     							= patch_norm
      __snake_case				:      Tuple     							= layer_norm_eps
      __snake_case				:      List[Any]     							= initializer_range
      __snake_case				:      List[str]     							= is_training
      __snake_case				:      str     							= scope
      __snake_case				:      List[str]     							= use_labels
      __snake_case				:      Optional[Any]     							= type_sequence_label_size
      __snake_case				:      Optional[Any]     							= encoder_stride
      __snake_case				:      Union[str, Any]     							= out_features
      __snake_case				:      List[Any]     							= out_indices







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Tuple     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      __snake_case				:      Any     							= None
      if self.use_labels:
         __snake_case				:      Union[str, Any]     							= ids_tensor([self.batch_size]					,   self.type_sequence_label_size)

      __snake_case				:      List[Any]     							= self.get_config()

      return config, pixel_values, labels







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return FocalNetConfig(
          image_size=self.image_size					,   patch_size=self.patch_size					,   num_channels=self.num_channels					,   embed_dim=self.embed_dim					,   hidden_sizes=self.hidden_sizes					,   depths=self.depths					,   num_heads=self.num_heads					,   window_size=self.window_size					,   mlp_ratio=self.mlp_ratio					,   qkv_bias=self.qkv_bias					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   drop_path_rate=self.drop_path_rate					,   hidden_act=self.hidden_act					,   use_absolute_embeddings=self.use_absolute_embeddings					,   path_norm=self.patch_norm					,   layer_norm_eps=self.layer_norm_eps					,   initializer_range=self.initializer_range					,   encoder_stride=self.encoder_stride					,   out_features=self.out_features					,   out_indices=self.out_indices					,   )







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      str     							= FocalNetModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case				:      str     							= model(__a)

      __snake_case				:      List[str]     							= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
      __snake_case				:      Optional[Any]     							= int(config.embed_dim * 2 ** (len(config.depths) - 1))

      self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, expected_seq_len, expected_dim))







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      Dict     							= FocalNetBackbone(config=__a)
      model.to(__a)
      model.eval()
      __snake_case				:      int     							= model(__a)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps)					,   len(config.out_features))
      self.parent.assertListEqual(list(result.feature_maps[0].shape)					,   [self.batch_size, self.image_size, 8, 8])

      # verify channels
      self.parent.assertEqual(len(model.channels)					,   len(config.out_features))
      self.parent.assertListEqual(model.channels					,   config.hidden_sizes[:-1])

      # verify backbone works with out_features=None
      __snake_case				:      Optional[int]     							= None
      __snake_case				:      List[str]     							= FocalNetBackbone(config=__a)
      model.to(__a)
      model.eval()
      __snake_case				:      List[Any]     							= model(__a)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps)					,   1)
      self.parent.assertListEqual(list(result.feature_maps[0].shape)					,   [self.batch_size, self.image_size * 2, 4, 4])

      # verify channels
      self.parent.assertEqual(len(model.channels)					,   1)
      self.parent.assertListEqual(model.channels					,   [config.hidden_sizes[-1]])







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Any:
      """simple docstring"""






      __snake_case				:      Dict     							= FocalNetForMaskedImageModeling(config=__a)
      model.to(__a)
      model.eval()
      __snake_case				:      List[str]     							= model(__a)
      self.parent.assertEqual(
          result.reconstruction.shape					,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

      # test greyscale images
      __snake_case				:      Tuple     							= 1
      __snake_case				:      Optional[Any]     							= FocalNetForMaskedImageModeling(__a)
      model.to(__a)
      model.eval()

      __snake_case				:      List[str]     							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      __snake_case				:      Optional[Any]     							= model(__a)
      self.parent.assertEqual(result.reconstruction.shape					,   (self.batch_size, 1, self.image_size, self.image_size))







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= self.type_sequence_label_size
      __snake_case				:      int     							= FocalNetForImageClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case				:      Any     							= model(__a					,   labels=__a)
      self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      __snake_case				:      Optional[Any]     							= 1
      __snake_case				:      Union[str, Any]     							= FocalNetForImageClassification(__a)
      model.to(__a)
      model.eval()

      __snake_case				:      str     							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      __snake_case				:      str     							= model(__a)
      self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= self.prepare_config_and_inputs()

      __snake_case							,__snake_case							,__snake_case				:      Optional[int]     							= config_and_inputs
      __snake_case				:      int     							= {'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							(
       (
           FocalNetModel,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   _snake_case        =							(
       {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
       if is_torch_available()
       else {}
   )
   _snake_case        =							False

   _snake_case        =							False
   _snake_case        =							False
   _snake_case        =							False
   _snake_case        =							False







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      List[Any]     							= FocalNetModelTester(self)
      __snake_case				:      List[Any]     							= ConfigTester(self					,   config_class=__a					,   embed_dim=3_7					,   has_text_modality=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      return







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case				:      Any     							= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case				:      Tuple     							= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__a)







   @unittest.skip(reason='FocalNet does not use inputs_embeds')
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   @unittest.skip(reason='FocalNet does not use feedforward chunking')
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      __snake_case							,__snake_case				:      Any     							= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
         __snake_case				:      Optional[Any]     							= model_class(__a)
         self.assertIsInstance(model.get_input_embeddings()					,   (nn.Module))
         __snake_case				:      int     							= model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__a					,   nn.Linear))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case							,__snake_case				:      str     							= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
         __snake_case				:      Any     							= model_class(__a)
         __snake_case				:      Any     							= inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __snake_case				:      Dict     							= [*signature.parameters.keys()]

         __snake_case				:      Any     							= ['pixel_values']
         self.assertListEqual(arg_names[:1]					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      str     							= model_class(__a)
      model.to(__a)
      model.eval()

      with torch.no_grad():
         __snake_case				:      int     							= model(**self._prepare_for_class(__a					,   __a))

      __snake_case				:      int     							= outputs.hidden_states

      __snake_case				:      List[Any]     							= getattr(
          self.model_tester					,   'expected_num_hidden_layers'					,   len(self.model_tester.depths) + 1)
      self.assertEqual(len(__a)					,   __a)

      # FocalNet has a different seq_length
      __snake_case				:      Union[str, Any]     							= (
          config.patch_size
          if isinstance(config.patch_size					,   collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      __snake_case				:      Optional[int]     							= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:])					,   [num_patches, self.model_tester.embed_dim]					,   )

      __snake_case				:      Tuple     							= outputs.reshaped_hidden_states
      self.assertEqual(len(__a)					,   __a)

      __snake_case							,__snake_case							,__snake_case							,__snake_case				:      int     							= reshaped_hidden_states[0].shape
      __snake_case				:      Dict     							= (
          reshaped_hidden_states[0].view(__a					,   __a					,   height * width).permute(0					,   2					,   1)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:])					,   [num_patches, self.model_tester.embed_dim]					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case							,__snake_case				:      int     							= self.model_tester.prepare_config_and_inputs_for_common()

      __snake_case				:      Tuple     							= (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					,   collections.abc.Iterable)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes[:-1]:
         __snake_case				:      Union[str, Any]     							= True
         self.check_hidden_states_output(__a					,   __a					,   __a					,   __a)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __snake_case				:      str     							= True

         self.check_hidden_states_output(__a					,   __a					,   __a					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case							,__snake_case				:      Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()
      __snake_case				:      Any     							= 3

      __snake_case				:      str     							= (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					,   collections.abc.Iterable)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      __snake_case				:      Any     							= (
          config.patch_size
          if isinstance(config.patch_size					,   collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      __snake_case				:      Union[str, Any]     							= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      __snake_case				:      List[Any]     							= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes[:-1]:
         __snake_case				:      Any     							= True
         self.check_hidden_states_output(__a					,   __a					,   __a					,   (padded_height, padded_width))

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __snake_case				:      str     							= True
         self.check_hidden_states_output(__a					,   __a					,   __a					,   (padded_height, padded_width))







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case				:      Optional[Any]     							= FocalNetModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      __snake_case							,__snake_case				:      Optional[int]     							= self.model_tester.prepare_config_and_inputs_for_common()

      __snake_case				:      List[str]     							= _config_zero_init(__a)
      for model_class in self.all_model_classes:
         __snake_case				:      str     							= model_class(config=__a)
         for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item()					,   [0.0, 1.0]					,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,   )




@require_vision
@require_torch
class a_    (    unittest.TestCase							):







   @cached_property
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny') if is_vision_available() else None







   @slow
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      __snake_case				:      str     							= FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny').to(__a)
      __snake_case				:      Optional[Any]     							= self.default_image_processor

      __snake_case				:      List[str]     							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
      __snake_case				:      Union[str, Any]     							= image_processor(images=__a					,   return_tensors='pt').to(__a)

      # forward pass
      with torch.no_grad():
         __snake_case				:      int     							= model(**__a)

      # verify the logits
      __snake_case				:      Union[str, Any]     							= torch.Size((1, 1_0_0_0))
      self.assertEqual(outputs.logits.shape					,   __a)
      __snake_case				:      Union[str, Any]     							= torch.tensor([0.2_166, -0.4_368, 0.2_191]).to(__a)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   __a					,   atol=1E-4))
      self.assertTrue(outputs.logits.argmax(dim=-1).item()					,   2_8_1)




@require_torch
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							(FocalNetBackbone,) if is_torch_available() else ()
   _snake_case        =							FocalNetConfig

   _snake_case        =							False







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= FocalNetModelTester(self)







'''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 a_    (    unittest.TestCase							):







   def __init__(self					,   __a					,   __a=7					,   __a=3					,   __a=1_8					,   __a=3_0					,   __a=4_0_0					,   __a=True					,   __a=None					,   __a=True					,   __a=None					,   __a=True					,   __a=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,   __a=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,   __a=True					,   )			->      List[Any]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """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     SCREAMING_SNAKE_CASE__					(self					,   __a=False					,   __a=False					,   __a=False)			->      List[str]:
      """simple docstring"""






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

      if equal_resolution:
         __snake_case				:      Optional[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:
         __snake_case				:      Dict     							= []
         for i in range(self.batch_size):
            __snake_case							,__snake_case				:      Optional[Any]     							= 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
         __snake_case				:      int     							= [Image.fromarray(np.moveaxis(__a					,   0					,   -1)) for x in image_inputs]

      if torchify:
         __snake_case				:      List[Any]     							= [torch.from_numpy(__a) for x in image_inputs]

      return image_inputs




@require_torch
@require_vision
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= ChineseCLIPImageProcessingTester(self					,   do_center_crop=__a)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      int     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= 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})

      __snake_case				:      List[str]     							= 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     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Optional[Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

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

      # Test batched
      __snake_case				:      List[Any]     							= image_processing(__a					,   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     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      __snake_case				:      Optional[int]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   numpify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   np.ndarray)

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

      # Test batched
      __snake_case				:      int     							= image_processing(__a					,   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     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      __snake_case				:      Tuple     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a					,   torchify=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   torch.Tensor)

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

      # Test batched
      __snake_case				:      Union[str, Any]     							= image_processing(__a					,   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 a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							ChineseCLIPImageProcessor if is_vision_available() else None







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= ChineseCLIPImageProcessingTester(self					,   num_channels=4					,   do_center_crop=__a)
      __snake_case				:      List[Any]     							= 3







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.image_processor_tester.prepare_image_processor_dict()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      Any     							= self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__a					,   'do_resize'))
      self.assertTrue(hasattr(__a					,   'size'))
      self.assertTrue(hasattr(__a					,   'do_center_crop'))
      self.assertTrue(hasattr(__a					,   'center_crop'))
      self.assertTrue(hasattr(__a					,   'do_normalize'))
      self.assertTrue(hasattr(__a					,   'image_mean'))
      self.assertTrue(hasattr(__a					,   'image_std'))
      self.assertTrue(hasattr(__a					,   'do_convert_rgb'))







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      __snake_case				:      Union[str, Any]     							= self.image_processor_tester.prepare_inputs(equal_resolution=__a)
      for image in image_inputs:
         self.assertIsInstance(__a					,   Image.Image)

      # Test not batched input
      __snake_case				:      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
      __snake_case				:      Optional[int]     							= image_processing(__a					,   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'],
          )					,   )