



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						:





   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=0.02       ,     _lowerCAmelCase=None       ,     ):
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  image_size
         _lowerCAmelCase 			=  patch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  is_training
         _lowerCAmelCase 			=  use_labels
         _lowerCAmelCase 			=  hidden_size
         _lowerCAmelCase 			=  num_hidden_layers
         _lowerCAmelCase 			=  num_attention_heads
         _lowerCAmelCase 			=  intermediate_size
         _lowerCAmelCase 			=  hidden_act
         _lowerCAmelCase 			=  hidden_dropout_prob
         _lowerCAmelCase 			=  attention_probs_dropout_prob
         _lowerCAmelCase 			=  type_sequence_label_size
         _lowerCAmelCase 			=  initializer_range
         _lowerCAmelCase 			=  scope

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

         _lowerCAmelCase 			=  None
         if self.use_labels:
               _lowerCAmelCase 			=  ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)

         _lowerCAmelCase 			=  self.get_config()

         return config, pixel_values, labels





   def 	__lowerCAmelCase							(	self			):
         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			):
         _lowerCAmelCase 			=  ViTMSNModel(config=_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  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			):
         _lowerCAmelCase 			=  self.type_sequence_label_size
         _lowerCAmelCase 			=  ViTMSNForImageClassification(_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()
         _lowerCAmelCase 			=  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
         _lowerCAmelCase 			=  1
         _lowerCAmelCase 			=  ViTMSNForImageClassification(_lowerCAmelCase			)
         model.to(_lowerCAmelCase			)
         model.eval()

         _lowerCAmelCase 			=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.prepare_config_and_inputs()
         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  config_and_inputs
         _lowerCAmelCase 			=  {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class 		UpperCAmelCase						(						snake_case_       ,snake_case_       ,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 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ViTMSNModelTester(self			)
         _lowerCAmelCase 			=  ConfigTester(self       ,     config_class=_lowerCAmelCase       ,     has_text_modality=_lowerCAmelCase       ,     hidden_size=37			)





   def 	__lowerCAmelCase							(	self			):
         self.config_tester.run_common_tests()





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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs_for_common()

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





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowerCAmelCase 			=  ['''pixel_values''']
               self.assertListEqual(arg_names[:1]       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowerCAmelCase 			=  ViTMSNModel.from_pretrained(_lowerCAmelCase			)
               self.assertIsNotNone(_lowerCAmelCase			)






def 							UpperCAmelCase__		(				)->str:
      _lowerCAmelCase 			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
      return image





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





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





   @slow
   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(2			)
         _lowerCAmelCase 			=  ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''			).to(_lowerCAmelCase			)

         _lowerCAmelCase 			=  self.default_image_processor
         _lowerCAmelCase 			=  prepare_img()
         _lowerCAmelCase 			=  image_processor(images=_lowerCAmelCase       ,     return_tensors='''pt'''			).to(_lowerCAmelCase			)

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

         # verify the logits
         _lowerCAmelCase 			=  torch.Size((1, 1_000)			)
         self.assertEqual(outputs.logits.shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]			).to(_lowerCAmelCase			)

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




def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list[int]     ,				_SCREAMING_SNAKE_CASE   :	str				)->list[int]:
      _lowerCAmelCase 			=  int(_SCREAMING_SNAKE_CASE				)

      # Initialize Result
      _lowerCAmelCase 			=  []

      # Traverse through all denomination
      for denomination in reversed(_SCREAMING_SNAKE_CASE				):
            # Find denominations
            while int(_SCREAMING_SNAKE_CASE				) >= int(_SCREAMING_SNAKE_CASE				):
                  total_value -= int(_SCREAMING_SNAKE_CASE				)
                  answer.append(_SCREAMING_SNAKE_CASE				)  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
    UpperCAmelCase_									=							[]
    UpperCAmelCase_									=							"0"

    if (
        input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
        == "y"
    ):
        UpperCAmelCase_									=							int(input("Enter the number of denominations you want to add: ").strip())

        for i in range(0, n):
            denominations.append(int(input(F"""Denomination {i}: """).strip()))
        UpperCAmelCase_									=							input("Enter the change you want to make in Indian Currency: ").strip()
    else:
        # All denominations of Indian Currency if user does not enter
        UpperCAmelCase_									=							[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
        UpperCAmelCase_									=							input("Enter the change you want to make: ").strip()

    if int(value) == 0 or int(value) < 0:
        print("The total value cannot be zero or negative.")

    else:
        print(F"""Following is minimal change for {value}: """)
        UpperCAmelCase_									=							find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=" ")