






'''simple docstring'''






import inspect
import unittest

from transformers import RegNetConfig
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 torch import nn

      from transformers import RegNetForImageClassification, RegNetModel
      from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor




class a_    :







   def __init__(self					,   __a					,   __a=3					,   __a=3_2					,   __a=3					,   __a=1_0					,   __a=[1_0, 2_0, 3_0, 4_0]					,   __a=[1, 1, 2, 1]					,   __a=True					,   __a=True					,   __a="relu"					,   __a=3					,   __a=None					,   )			->      Tuple:
      """simple docstring"""






      __snake_case				:      List[str]     							= parent
      __snake_case				:      List[Any]     							= batch_size
      __snake_case				:      Dict     							= image_size
      __snake_case				:      Union[str, Any]     							= num_channels
      __snake_case				:      Dict     							= embeddings_size
      __snake_case				:      Optional[Any]     							= hidden_sizes
      __snake_case				:      int     							= depths
      __snake_case				:      str     							= is_training
      __snake_case				:      List[str]     							= use_labels
      __snake_case				:      List[Any]     							= hidden_act
      __snake_case				:      List[str]     							= num_labels
      __snake_case				:      Dict     							= scope
      __snake_case				:      Dict     							= len(__a)







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






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

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

      __snake_case				:      int     							= self.get_config()

      return config, pixel_values, labels







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






      return RegNetConfig(
          num_channels=self.num_channels					,   embeddings_size=self.embeddings_size					,   hidden_sizes=self.hidden_sizes					,   depths=self.depths					,   hidden_act=self.hidden_act					,   num_labels=self.num_labels					,   )







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






      __snake_case				:      Tuple     							= RegNetModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case				:      Optional[int]     							= model(__a)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape					,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,   )







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






      __snake_case				:      str     							= self.num_labels
      __snake_case				:      List[Any]     							= RegNetForImageClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case				:      int     							= model(__a					,   labels=__a)
      self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels))







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






      __snake_case				:      Union[str, Any]     							= self.prepare_config_and_inputs()
      __snake_case							,__snake_case							,__snake_case				:      List[Any]     							= config_and_inputs
      __snake_case				:      str     							= {'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   _snake_case        =							(
       {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

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







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






      __snake_case				:      Optional[Any]     							= RegNetModelTester(self)
      __snake_case				:      Optional[Any]     							= ConfigTester(self					,   config_class=__a					,   has_text_modality=__a)







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






      return







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






      pass







   @unittest.skip(reason='RegNet does not support input and output embeddings')
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      pass







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






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

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

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







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






      __snake_case				:      List[str]     							= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







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






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

      for model_class in self.all_model_classes:
         __snake_case				:      Tuple     							= model_class(config=__a)
         for name, module in model.named_modules():
            if isinstance(__a					,   (nn.BatchNormad, nn.GroupNorm)):
               self.assertTrue(
                   torch.all(module.weight == 1)					,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,   )
               self.assertTrue(
                   torch.all(module.bias == 0)					,   msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,   )







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






      def check_hidden_states_output(__a					,   __a					,   __a):
         __snake_case				:      Optional[int]     							= model_class(__a)
         model.to(__a)
         model.eval()

         with torch.no_grad():
            __snake_case				:      Optional[Any]     							= model(**self._prepare_for_class(__a					,   __a))

         __snake_case				:      Any     							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         __snake_case				:      List[str]     							= self.model_tester.num_stages
         self.assertEqual(len(__a)					,   expected_num_stages + 1)

         # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:])					,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,   )

      __snake_case							,__snake_case				:      List[str]     							= self.model_tester.prepare_config_and_inputs_for_common()
      __snake_case				:      str     							= ['basic', 'bottleneck']
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            __snake_case				:      List[Any]     							= layer_type
            __snake_case				:      Tuple     							= True
            check_hidden_states_output(__a					,   __a					,   __a)

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

            check_hidden_states_output(__a					,   __a					,   __a)







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






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







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






      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case				:      Tuple     							= RegNetModel.from_pretrained(__a)
         self.assertIsNotNone(__a)




def        _SCREAMING_SNAKE_CASE  (				)    -> str:







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





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







   @cached_property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
          if is_vision_available()
          else None
      )







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






      __snake_case				:      Dict     							= RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__a)

      __snake_case				:      List[str]     							= self.default_image_processor
      __snake_case				:      List[str]     							= prepare_img()
      __snake_case				:      int     							= image_processor(images=__a					,   return_tensors='pt').to(__a)

      # forward pass
      with torch.no_grad():
         __snake_case				:      List[str]     							= model(**__a)

      # verify the logits
      __snake_case				:      str     							= torch.Size((1, 1_0_0_0))
      self.assertEqual(outputs.logits.shape					,   __a)

      __snake_case				:      Optional[Any]     							= torch.tensor([-0.4_180, -1.5_051, -3.4_836]).to(__a)

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







'''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'],
          )					,   )