



'''simple docstring'''


import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
   import torch

   from transformers import CvtForImageClassification, CvtModel
   from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(UpperCamelCase__  ,						'embed_dim' ) )
       self.parent.assertTrue(hasattr(UpperCamelCase__  ,						'num_heads' ) )

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Optional[int]=1_3  ,						UpperCamelCase__      :					Union[str, Any]=6_4  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Dict=[1_6, 4_8, 9_6]  ,						UpperCamelCase__      :					Any=[1, 3, 6]  ,						UpperCamelCase__      :					List[str]=[1, 2, 1_0]  ,						UpperCamelCase__      :					str=[7, 3, 3]  ,						UpperCamelCase__      :					int=[4, 2, 2]  ,						UpperCamelCase__      :					Optional[Any]=[2, 1, 1]  ,						UpperCamelCase__      :					Union[str, Any]=[2, 2, 2]  ,						UpperCamelCase__      :					List[str]=[False, False, True]  ,						UpperCamelCase__      :					Dict=[0.0, 0.0, 0.0]  ,						UpperCamelCase__      :					int=0.0_2  ,						UpperCamelCase__      :					Dict=1E-1_2  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					int=2  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  patch_sizes
       UpperCamelCase					=  patch_stride
       UpperCamelCase					=  patch_padding
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  embed_dim
       UpperCamelCase					=  num_heads
       UpperCamelCase					=  stride_kv
       UpperCamelCase					=  depth
       UpperCamelCase					=  cls_token
       UpperCamelCase					=  attention_drop_rate
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  layer_norm_eps






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_labels )

       UpperCamelCase					=  self.get_config()
       return config, pixel_values, labels






    def  A       (  self      :					str ):


       """simple docstring"""







       return CvtConfig(
           image_size=self.image_size  ,						num_labels=self.num_labels  ,						num_channels=self.num_channels  ,						embed_dim=self.embed_dim  ,						num_heads=self.num_heads  ,						patch_sizes=self.patch_sizes  ,						patch_padding=self.patch_padding  ,						patch_stride=self.patch_stride  ,						stride_kv=self.stride_kv  ,						depth=self.depth  ,						cls_token=self.cls_token  ,						attention_drop_rate=self.attention_drop_rate  ,						initializer_range=self.initializer_range  ,						)






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  CvtModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )
       UpperCamelCase					=  (self.image_size, self.image_size)
       UpperCamelCase				,      UpperCamelCase					=  image_size[0], image_size[1]
       for i in range(len(self.depth ) ):
          UpperCamelCase					=  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
          UpperCamelCase					=  floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.embed_dim[-1], height, width) )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  CvtForImageClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  config_and_inputs
       UpperCamelCase					=  {'pixel_values': pixel_values}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (CvtModel, CvtForImageClassification) if is_torch_available() else ()
    _SCREAMING_SNAKE_CASE	  =  (
        {"""feature-extraction""": CvtModel, """image-classification""": CvtForImageClassification}
        if is_torch_available()
        else {}
    )

    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  CvtModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					Union[str, 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  A       (  self      :					Any ):


       """simple docstring"""







       return






    @unittest.skip(reason='Cvt does not output attentions' )
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='Cvt does not use inputs_embeds' )
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='Cvt does not support input and output embeddings' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCamelCase					=  [*signature.parameters.keys()]

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, Any] ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.eval()

          with torch.no_grad():
             UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.hidden_states

          UpperCamelCase					=  len(self.model_tester.depth )
          self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )

          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-3:] )  ,						[
                  self.model_tester.embed_dim[0],
                  self.model_tester.image_size // 4,
                  self.model_tester.image_size // 4,
              ]  ,						)

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCamelCase					=  True

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def  A       (  self      :					int ):


       """simple docstring"""







       pass






    @slow
    def  A       (  self      :					Tuple ):


       """simple docstring"""







       for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  CvtModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )







def 				__lowerCamelCase				(   )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

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






    """simple docstring"""





    @cached_property
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






    @slow
    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(UpperCamelCase__ )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='pt' ).to(UpperCamelCase__ )

       # forward pass
       with torch.no_grad():
          UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] ).to(UpperCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

