





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 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case=3       ,      snake_case=32       ,      snake_case=3       ,      snake_case=10       ,      snake_case=[10, 20, 30, 40]       ,      snake_case=[1, 1, 2, 1]       ,      snake_case=True       ,      snake_case=True       ,      snake_case="relu"       ,      snake_case=3       ,      snake_case=None       ,      )					->	Any:
      """simple docstring"""





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





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





      a__						:    Dict   		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

      a__						:    Tuple   		=	self.get_config()

      return config, pixel_values, labels





   def 						_snake_case			(			self   )					->	List[str]:
      """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 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	RegNetModel(config=_SCREAMING_SNAKE_CASE   )
      model.to(_SCREAMING_SNAKE_CASE   )
      model.eval()
      a__						:    str   		=	model(_SCREAMING_SNAKE_CASE   )
      # 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 // 32, self.image_size // 32)       ,      )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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





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





      a__						:    int   		=	self.prepare_config_and_inputs()
      a__		,  a__		,  a__						:    Union[str, Any]   		=	config_and_inputs
      a__						:    Any   		=	{"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class 	__lowerCAmelCase      (       __lowerCAmelCase		,__lowerCAmelCase		,unittest.TestCase	):
   _UpperCamelCase			:    Dict  		=					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
   _UpperCamelCase			:    Tuple  		=					(
       {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCamelCase			:    Tuple  		=					False
   _UpperCamelCase			:    Tuple  		=					False
   _UpperCamelCase			:    Optional[Any]  		=					False
   _UpperCamelCase			:    Optional[Any]  		=					False





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





      a__						:    Optional[Any]   		=	RegNetModelTester(self   )
      a__						:    Tuple   		=	ConfigTester(self       ,      config_class=_SCREAMING_SNAKE_CASE       ,      has_text_modality=_SCREAMING_SNAKE_CASE   )





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





      return





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





      pass





   @unittest.skip(reason="RegNet does not support input and output embeddings"   )
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      pass





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





      a__		,  a__						:    List[str]   		=	self.model_tester.prepare_config_and_inputs_for_common()

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

         a__						:    Any   		=	["pixel_values"]
         self.assertListEqual(arg_names[:1]       ,      _SCREAMING_SNAKE_CASE   )





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





      a__						:    List[Any]   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )





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





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

      for model_class in self.all_model_classes:
         a__						:    Optional[Any]   		=	model_class(config=_SCREAMING_SNAKE_CASE   )
         for name, module in model.named_modules():
            if isinstance(_SCREAMING_SNAKE_CASE       ,      (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 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      def check_hidden_states_output(snake_case       ,      snake_case       ,      snake_case   ):
         a__						:    Optional[Any]   		=	model_class(_SCREAMING_SNAKE_CASE   )
         model.to(_SCREAMING_SNAKE_CASE   )
         model.eval()

         with torch.no_grad():
            a__						:    Dict   		=	model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE   )   )

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

         a__						:    int   		=	self.model_tester.num_stages
         self.assertEqual(len(_SCREAMING_SNAKE_CASE   )       ,      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]       ,      )

      a__		,  a__						:    Dict   		=	self.model_tester.prepare_config_and_inputs_for_common()
      a__						:    Tuple   		=	["basic", "bottleneck"]
      for model_class in self.all_model_classes:
         for layer_type in layers_type:
            a__						:    Optional[Any]   		=	layer_type
            a__						:    str   		=	True
            check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE   )

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

            check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE   )





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





      a__						:    Tuple   		=	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )





   @slow
   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a__						:    Optional[int]   		=	RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def    _A	(   ):
   a__						:    Tuple   		=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
   return image







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





   @cached_property
   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





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





   @slow
   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Optional[int]   		=	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(_SCREAMING_SNAKE_CASE   )

      a__						:    Tuple   		=	self.default_image_processor
      a__						:    Optional[int]   		=	prepare_img()
      a__						:    str   		=	image_processor(images=_SCREAMING_SNAKE_CASE       ,      return_tensors="pt"   ).to(_SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
         a__						:    Optional[Any]   		=	model(**_SCREAMING_SNAKE_CASE   )

      # verify the logits
      a__						:    Optional[int]   		=	torch.Size((1, 1_000)   )
      self.assertEqual(outputs.logits.shape       ,      _SCREAMING_SNAKE_CASE   )

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

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







'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






     def __init__(					self       )				->     List[Any]:
          _snake_case				= None






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('''After swapping''')
   ll.print_list()


