



'''simple docstring'''



import inspect
import unittest

from transformers import ResNetConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class        UpperCAmelCase__  :



 def __init__(  self		,	lowercase		,	lowercase=3		,	lowercase=3_2		,	lowercase=3		,	lowercase=1_0		,	lowercase=[1_0, 2_0, 3_0, 4_0]		,	lowercase=[1, 1, 2, 1]		,	lowercase=True		,	lowercase=True		,	lowercase="relu"		,	lowercase=3		,	lowercase=None		,	lowercase=["stage2", "stage3", "stage4"]		,	lowercase=[2, 3, 4]		,	)  ->							Optional[int]:
  __UpperCamelCase				      =  parent
  __UpperCamelCase				      =  batch_size
  __UpperCamelCase				      =  image_size
  __UpperCamelCase				      =  num_channels
  __UpperCamelCase				      =  embeddings_size
  __UpperCamelCase				      =  hidden_sizes
  __UpperCamelCase				      =  depths
  __UpperCamelCase				      =  is_training
  __UpperCamelCase				      =  use_labels
  __UpperCamelCase				      =  hidden_act
  __UpperCamelCase				      =  num_labels
  __UpperCamelCase				      =  scope
  __UpperCamelCase				      =  len(lowercase			)
  __UpperCamelCase				      =  out_features
  __UpperCamelCase				      =  out_indices



 def    __lowerCamelCase				(  self			)  ->							Dict:
  __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    __lowerCamelCase				(  self			)  ->							Tuple:
  return ResNetConfig(
      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		,	out_features=self.out_features		,	out_indices=self.out_indices		,	)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[int]:
  __UpperCamelCase				      =  ResNetModel(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  ResNetForImageClassification(lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase		,	labels=lowercase			)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Tuple:
  __UpperCamelCase				      =  ResNetBackbone(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase			)

  # 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.hidden_sizes[1], 4, 4]			)

  # 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
  __UpperCamelCase				      =  None
  __UpperCamelCase				      =  ResNetBackbone(config=lowercase			)
  model.to(lowercase			)
  model.eval()
  __UpperCamelCase				      =  model(lowercase			)

  # verify feature maps
  self.parent.assertEqual(len(result.feature_maps			)		,	1			)
  self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,	[self.batch_size, self.hidden_sizes[-1], 1, 1]			)

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




 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase				      =  self.prepare_config_and_inputs()
  __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  config_and_inputs
  __UpperCamelCase				      =  {"""pixel_values""": pixel_values}
  return config, inputs_dict


@require_torch
class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				(
     (
         ResNetModel,
         ResNetForImageClassification,
         ResNetBackbone,
     )
     if is_torch_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE					      =				(
     {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
     if is_torch_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE					      =				True
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False



 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  __UpperCamelCase				      =  ResNetModelTester(self			)
  __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase		,	has_text_modality=lowercase			)



 def    __lowerCamelCase				(  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    __lowerCamelCase				(  self			)  ->							List[str]:
  return



 @unittest.skip(reason="""ResNet does not use inputs_embeds"""			)
 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  pass



 @unittest.skip(reason="""ResNet does not support input and output embeddings"""			)
 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  pass



 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __UpperCamelCase				      =  model_class(lowercase			)
   __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]		,	lowercase			)



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_backbone(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __UpperCamelCase				      =  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    __lowerCamelCase				(  self			)  ->							Any:
  def check_hidden_states_output(lowercase		,	lowercase		,	lowercase			):
   __UpperCamelCase				      =  model_class(lowercase			)
   model.to(lowercase			)
   model.eval()

   with torch.no_grad():
    __UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)

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

   __UpperCamelCase				      =  self.model_tester.num_stages
   self.assertEqual(len(lowercase			)		,	expected_num_stages + 1			)

   # ResNet'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 // 4, self.model_tester.image_size // 4]		,	)

  __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  __UpperCamelCase				      =  ["""basic""", """bottleneck"""]
  for model_class in self.all_model_classes:
   for layer_type in layers_type:
    __UpperCamelCase				      =  layer_type
    __UpperCamelCase				      =  True
    check_hidden_states_output(lowercase		,	lowercase		,	lowercase			)

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

    check_hidden_states_output(lowercase		,	lowercase		,	lowercase			)



 @unittest.skip(reason="""ResNet does not use feedforward chunking"""			)
 def    __lowerCamelCase				(  self			)  ->							str:
  pass



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*lowercase			)




 @slow
 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __UpperCamelCase				      =  ResNetModel.from_pretrained(lowercase			)
   self.assertIsNotNone(lowercase			)







def _lowercase   (  ):



 '''simple docstring'''



 __UpperCamelCase				      =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
 return image
@require_torch
@require_vision
class        UpperCAmelCase__  (    unittest.TestCase):



 @cached_property
 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  return (
      AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
      if is_vision_available()
      else None
  )




 @slow
 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase				      =  ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(lowercase			)

  __UpperCamelCase				      =  self.default_image_processor
  __UpperCamelCase				      =  prepare_img()
  __UpperCamelCase				      =  image_processor(images=lowercase		,	return_tensors="""pt"""			).to(lowercase			)

  # forward pass
  with torch.no_grad():
   __UpperCamelCase				      =  model(**lowercase			)

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

  __UpperCamelCase				      =  torch.tensor([-11.1_069, -9.7_877, -8.3_777]			).to(lowercase			)

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


@require_torch
class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				(ResNetBackbone,) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				ResNetConfig



 def    __lowerCamelCase				(  self			)  ->							int:
  __UpperCamelCase				      =  ResNetModelTester(self			)










'''simple docstring'''



from .stack import Stack







def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  Stack()
 __UpperCamelCase				      =  {"""(""": """)""", """[""": """]""", """{""": """}"""}
 for bracket in parentheses:
  if bracket in bracket_pairs:
   stack.push(__A       )
  elif bracket in (")", "]", "}"):
   if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
    return False
 return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 a__						:			List[Any]										=			['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  a__						:			int										=			'' if balanced_parentheses(example) else 'not '
  print(f'''{example} is {not_str}balanced''')





