






"""simple docstring"""



import inspect
import unittest

from transformers import ViTMSNConfig
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_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 ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor




class 			lowerCAmelCase__			:
   def __init__(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any]=1_3							,			snake_case__      :	Any=3_0							,			snake_case__      :	Dict=2							,			snake_case__      :	Union[str, Any]=3							,			snake_case__      :	List[Any]=True							,			snake_case__      :	Optional[int]=True							,			snake_case__      :	Dict=3_2							,			snake_case__      :	int=5							,			snake_case__      :	Optional[Any]=4							,			snake_case__      :	Tuple=3_7							,			snake_case__      :	List[str]="gelu"							,			snake_case__      :	Optional[int]=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Dict=1_0							,			snake_case__      :	Any=0.02							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							parent
        UpperCAmelCase__						:    Union[str, Any]							=							batch_size
        UpperCAmelCase__						:    List[Any]							=							image_size
        UpperCAmelCase__						:    Tuple							=							patch_size
        UpperCAmelCase__						:    Optional[int]							=							num_channels
        UpperCAmelCase__						:    Dict							=							is_training
        UpperCAmelCase__						:    Optional[Any]							=							use_labels
        UpperCAmelCase__						:    List[Any]							=							hidden_size
        UpperCAmelCase__						:    str							=							num_hidden_layers
        UpperCAmelCase__						:    Optional[Any]							=							num_attention_heads
        UpperCAmelCase__						:    Tuple							=							intermediate_size
        UpperCAmelCase__						:    Optional[Any]							=							hidden_act
        UpperCAmelCase__						:    int							=							hidden_dropout_prob
        UpperCAmelCase__						:    Union[str, Any]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    List[str]							=							type_sequence_label_size
        UpperCAmelCase__						:    Tuple							=							initializer_range
        UpperCAmelCase__						:    Optional[Any]							=							scope

        # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        UpperCAmelCase__						:    Optional[int]							=							(image_size // patch_size) ** 2
        UpperCAmelCase__						:    Union[str, Any]							=							num_patches + 1
   def       __a							(		self      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        UpperCAmelCase__						:    List[str]							=							None
        if self.use_labels:
             UpperCAmelCase__						:    Optional[int]							=							ids_tensor([self.batch_size]							,			self.type_sequence_label_size							)

        UpperCAmelCase__						:    Optional[int]							=							self.get_config()

        return config, pixel_values, labels
   def       __a							(		self      :	str							):


        '''simple docstring'''
        return ViTMSNConfig(
            image_size=self.image_size							,			patch_size=self.patch_size							,			num_channels=self.num_channels							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			intermediate_size=self.intermediate_size							,			hidden_act=self.hidden_act							,			hidden_dropout_prob=self.hidden_dropout_prob							,			attention_probs_dropout_prob=self.attention_probs_dropout_prob							,			initializer_range=self.initializer_range							,			)
   def       __a							(		self      :	Dict							,			snake_case__      :	Tuple							,			snake_case__      :	List[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							ViTMSNModel(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[Any]							=							model(snake_case__							)
        self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	List[str]							,			snake_case__      :	str							,			snake_case__      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.type_sequence_label_size
        UpperCAmelCase__						:    Optional[int]							=							ViTMSNForImageClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[Any]							=							model(snake_case__							,			labels=snake_case__							)
        print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"							)
        print("Labels: {labels}"							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)							)

        # test greyscale images
        UpperCAmelCase__						:    List[str]							=							1
        UpperCAmelCase__						:    Union[str, Any]							=							ViTMSNForImageClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()

        UpperCAmelCase__						:    str							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        UpperCAmelCase__						:    Tuple							=							model(snake_case__							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)							)




   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.prepare_config_and_inputs()
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							config_and_inputs
        UpperCAmelCase__						:    Optional[int]							=							{"pixel_values": pixel_values}
        return config, inputs_dict




@require_torch
class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   SCREAMING_SNAKE_CASE_    =(
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							ViTMSNModelTester(self							)
        UpperCAmelCase__						:    str							=							ConfigTester(self							,			config_class=snake_case__							,			has_text_modality=snake_case__							,			hidden_size=3_7							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   @unittest.skip(reason="ViTMSN does not use inputs_embeds"							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        pass
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Union[str, Any]							=							model_class(snake_case__							)
             self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)							)
             UpperCAmelCase__						:    List[str]							=							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__							,			nn.Linear							)							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    Optional[Any]							=							model_class(snake_case__							)
             UpperCAmelCase__						:    Union[str, Any]							=							inspect.signature(model.forward							)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase__						:    Optional[Any]							=							[*signature.parameters.keys()]

             UpperCAmelCase__						:    List[Any]							=							["pixel_values"]
             self.assertListEqual(arg_names[:1]							,			snake_case__							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case__							)




   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase__						:    Dict							=							ViTMSNModel.from_pretrained(snake_case__							)
             self.assertIsNotNone(snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  )->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image




@require_torch
@require_vision
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @cached_property
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"							) if is_vision_available() else None




   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        torch.manual_seed(2							)
        UpperCAmelCase__						:    Optional[int]							=							ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"							).to(snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							self.default_image_processor
        UpperCAmelCase__						:    Dict							=							prepare_img()
        UpperCAmelCase__						:    int							=							image_processor(images=snake_case__							,			return_tensors="pt"							).to(snake_case__							)

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

        # verify the logits
        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_0_0_0)							)
        self.assertEqual(outputs.logits.shape							,			snake_case__							)

        UpperCAmelCase__						:    str							=							torch.tensor([-0.0803, -0.4454, -0.2375]							).to(snake_case__							)

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










"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


