




'''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        lowercase_      :





     def __init__(     self     :					Optional[Any]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Dict=13     ,							__lowerCamelCase     :					List[Any]=30     ,							__lowerCamelCase     :					int=2     ,							__lowerCamelCase     :					List[Any]=3     ,							__lowerCamelCase     :					Optional[int]=True     ,							__lowerCamelCase     :					List[str]=True     ,							__lowerCamelCase     :					Optional[Any]=32     ,							__lowerCamelCase     :					Union[str, Any]=5     ,							__lowerCamelCase     :					Tuple=4     ,							__lowerCamelCase     :					Optional[int]=37     ,							__lowerCamelCase     :					List[Any]="gelu"     ,							__lowerCamelCase     :					Optional[Any]=0.1     ,							__lowerCamelCase     :					Optional[int]=0.1     ,							__lowerCamelCase     :					Any=10     ,							__lowerCamelCase     :					List[Any]=0.0_2     ,							__lowerCamelCase     :					List[Any]=None     ,							):
           snake_case__       :   Tuple	      =    parent
           snake_case__       :   Dict	      =    batch_size
           snake_case__       :   Any	      =    image_size
           snake_case__       :   Optional[Any]	      =    patch_size
           snake_case__       :   List[str]	      =    num_channels
           snake_case__       :   Tuple	      =    is_training
           snake_case__       :   Dict	      =    use_labels
           snake_case__       :   int	      =    hidden_size
           snake_case__       :   int	      =    num_hidden_layers
           snake_case__       :   Tuple	      =    num_attention_heads
           snake_case__       :   List[str]	      =    intermediate_size
           snake_case__       :   List[str]	      =    hidden_act
           snake_case__       :   int	      =    hidden_dropout_prob
           snake_case__       :   Any	      =    attention_probs_dropout_prob
           snake_case__       :   Any	      =    type_sequence_label_size
           snake_case__       :   Any	      =    initializer_range
           snake_case__       :   str	      =    scope

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





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Tuple	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

           snake_case__       :   List[Any]	      =    self.get_config()

           return config, pixel_values, labels





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           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 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Union[str, Any]			):
           snake_case__       :   List[str]	      =    ViTMSNModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[str]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__       :   Union[str, Any]	      =    self.type_sequence_label_size
           snake_case__       :   Optional[int]	      =    ViTMSNForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Optional[Any]	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           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
           snake_case__       :   Tuple	      =    1
           snake_case__       :   Any	      =    ViTMSNForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

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





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Dict	      =    self.prepare_config_and_inputs()
           snake_case__       :   List[Any]	      =    config_and_inputs
           snake_case__       :   Optional[Any]	      =    {'pixel_values': pixel_values}
           return config, inputs_dict






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

     A_											=			False
     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Optional[Any]	      =    ViTMSNModelTester(self			)
           snake_case__       :   Dict	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							has_text_modality=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Any			):
           self.config_tester.run_common_tests()





     @unittest.skip(reason='ViTMSN does not use inputs_embeds'			)
     def 			_lowerCAmelCase      (     self     :					str			):
           pass





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

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





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   int	      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case__       :   Union[str, Any]	      =    ['pixel_values']
                 self.assertListEqual(arg_names[:1]     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Dict	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):


           for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   int	      =    ViTMSNModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






def 		UpperCamelCase__      (		)    ->   Optional[Any]:
      snake_case__       :   Tuple	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






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





     @cached_property
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'			) if is_vision_available() else None





     @slow
     def 			_lowerCAmelCase      (     self     :					List[str]			):
           torch.manual_seed(2			)
           snake_case__       :   Optional[int]	      =    ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'			).to(__lowerCamelCase			)

           snake_case__       :   List[str]	      =    self.default_image_processor
           snake_case__       :   Any	      =    prepare_img()
           snake_case__       :   Optional[int]	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

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

           # verify the logits
           snake_case__       :   Dict	      =    torch.Size((1, 1000)			)
           self.assertEqual(outputs.logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Tuple	      =    torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]			).to(__lowerCamelCase			)

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





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()