'''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       UpperCAmelCase  :





       '''simple docstring'''



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

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

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Union[str, Any]						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

        lowercase__    :   Tuple						= self.get_config()

        return config, pixel_values, labels

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        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     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Any:
        lowercase__    :   Optional[int]						= ViTMSNModel(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   str						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   Union[str, Any]						= self.type_sequence_label_size
        lowercase__    :   Dict						= ViTMSNForImageClassification(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   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
        lowercase__    :   Any						= 1
        lowercase__    :   int						= ViTMSNForImageClassification(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()

        lowercase__    :   Optional[Any]						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__    :   str						= model(__lowerCAmelCase			)
        self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)			)


       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   int						= self.prepare_config_and_inputs()
        lowercase__			,							lowercase__			,							lowercase__    :   Tuple						= config_and_inputs
        lowercase__    :   Dict						= {'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_torch
class       UpperCAmelCase  (						a__   ,						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       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 _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   int						= ViTMSNModelTester(self			)
        lowercase__    :   List[str]						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       has_text_modality=__lowerCAmelCase     ,       hidden_size=37			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        self.config_tester.run_common_tests()

       @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''			)
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        pass

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__    :   int						= model_class(__lowerCAmelCase			)
         self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)			)
         lowercase__    :   Any						= model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__lowerCAmelCase     ,       nn.Linear			)			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__			,							lowercase__    :   List[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

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

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

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Union[str, Any]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[str]						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase			)


       @slow
       def _lowerCAmelCase(						self			)  ->  str:
        for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__    :   Union[str, Any]						= ViTMSNModel.from_pretrained(__lowerCAmelCase			)
         self.assertIsNotNone(__lowerCAmelCase			)


def       __UpperCamelCase	(       ):
 lowercase__    :   List[Any]						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
 return image





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





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(						self			)  ->  str:
        return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''			) if is_vision_available() else None


       @slow
       def _lowerCAmelCase(						self			)  ->  List[str]:
        torch.manual_seed(2			)
        lowercase__    :   Union[str, Any]						= ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''			).to(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= self.default_image_processor
        lowercase__    :   List[str]						= prepare_img()
        lowercase__    :   str						= image_processor(images=__lowerCAmelCase     ,       return_tensors='''pt'''			).to(__lowerCAmelCase			)

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

        # verify the logits
        lowercase__    :   Optional[int]						= torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,       __lowerCAmelCase			)

        lowercase__    :   Any						= 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			)			)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

