

'''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 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     Optional[Any]					,lowercase__   :     int=3					,lowercase__   :     Any=3_2					,lowercase__   :     List[str]=3					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Any=[1_0, 2_0, 3_0, 4_0]					,lowercase__   :     Union[str, Any]=[1, 1, 2, 1]					,lowercase__   :     str=True					,lowercase__   :     Optional[Any]=True					,lowercase__   :     List[Any]="relu"					,lowercase__   :     Any=3					,lowercase__   :     Any=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = num_channels
   __lowercase							  = embeddings_size
   __lowercase							  = hidden_sizes
   __lowercase							  = depths
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __lowercase							  = hidden_act
   __lowercase							  = num_labels
   __lowercase							  = scope
   __lowercase							  = len(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   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   :     Any					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     List[Any]			):
   __lowercase							  = RegNetModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   # 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   :     List[str]					,lowercase__   :     Union[str, Any]					,lowercase__   :     int					,lowercase__   :     List[str]			):
   __lowercase							  = self.num_labels
   __lowercase							  = RegNetForImageClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class 			lowercase_	(_A				,       _A				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[Any]							=			(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :  List[Any]							=			(
      {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Tuple							=			False
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = RegNetModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   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   :     Optional[int]			):
   return






  @unittest.skip(reason='''RegNet does not use inputs_embeds'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   pass






  @unittest.skip(reason='''RegNet does not support input and output embeddings'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

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

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(config=lowercase__			)
    for name, module in model.named_modules():
     if isinstance(lowercase__					,(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   :     str			):
   def check_hidden_states_output(lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]			):
    __lowercase							  = model_class(lowercase__			)
    model.to(lowercase__			)
    model.eval()

    with torch.no_grad():
     __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

    __lowercase							  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

    __lowercase							  = self.model_tester.num_stages
    self.assertEqual(len(lowercase__			)					,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]					,)

   __lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase							  = ['''basic''', '''bottleneck''']
   for model_class in self.all_model_classes:
    for layer_type in layers_type:
     __lowercase							  = layer_type
     __lowercase							  = True
     check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)

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

     check_hidden_states_output(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = RegNetModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
 return image






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




  """simple docstring"""




  @cached_property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return (
       AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
       if is_vision_available()
       else None
   )






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_img()
   __lowercase							  = image_processor(images=lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__			)

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

   __lowercase							  = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]			).to(lowercase__			)

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









"""simple docstring"""



def 						lowercase    (				__snake_case				: int = 1_0_0	):
		lowercase_ : str							       =   0
		lowercase_ : List[Any]							       =   0
		for i in range(1		,					n + 1	):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
						print(F"""{solution() = }""")



