



import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 		UpperCAmelCase						(						unittest.TestCase				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=32       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=10       ,     _lowerCAmelCase=[10, 20, 30, 40]       ,     _lowerCAmelCase=[1, 1, 2, 1]       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase="relu"       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=None       ,     ):
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  image_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  embeddings_size
         _lowerCAmelCase 			=  hidden_sizes
         _lowerCAmelCase 			=  depths
         _lowerCAmelCase 			=  is_training
         _lowerCAmelCase 			=  use_labels
         _lowerCAmelCase 			=  hidden_act
         _lowerCAmelCase 			=  num_labels
         _lowerCAmelCase 			=  scope
         _lowerCAmelCase 			=  len(_lowerCAmelCase			)





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

         _lowerCAmelCase 			=  self.get_config()

         return config, pixel_values





   def 	__lowerCAmelCase							(	self			):
         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       ,     image_size=self.image_size       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  FlaxRegNetModel(config=_lowerCAmelCase			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase			)

         # Output shape (b, c, h, w)
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.num_labels
         _lowerCAmelCase 			=  FlaxRegNetForImageClassification(config=_lowerCAmelCase			)
         _lowerCAmelCase 			=  model(_lowerCAmelCase			)
         self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)			)





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






@require_flax
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  FlaxRegNetModelTester(self			)
         _lowerCAmelCase 			=  ConfigTester(self       ,     config_class=_lowerCAmelCase       ,     has_text_modality=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         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 	__lowerCAmelCase							(	self			):
         return





   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			)





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





   @unittest.skip(reason='''RegNet does not support input and output embeddings'''			)
   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			)
               _lowerCAmelCase 			=  inspect.signature(model.__call__			)
               # 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			):
         def check_hidden_states_output(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  model_class(_lowerCAmelCase			)

               _lowerCAmelCase 			=  model(**self._prepare_for_class(_lowerCAmelCase       ,     _lowerCAmelCase			)			)

               _lowerCAmelCase 			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _lowerCAmelCase 			=  self.model_tester.num_stages
               self.assertEqual(len(_lowerCAmelCase			)       ,     expected_num_stages + 1			)

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _lowerCAmelCase 			=  True
               check_hidden_states_output(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _lowerCAmelCase 			=  True

               check_hidden_states_output(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





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

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                     _lowerCAmelCase 			=  self._prepare_for_class(_lowerCAmelCase       ,     _lowerCAmelCase			)
                     _lowerCAmelCase 			=  model_class(_lowerCAmelCase			)

                     @jax.jit
                     def model_jitted(_lowerCAmelCase       ,     **_lowerCAmelCase			):
                           return model(pixel_values=_lowerCAmelCase       ,     **_lowerCAmelCase			)

                     with self.subTest('''JIT Enabled'''			):
                           _lowerCAmelCase 			=  model_jitted(**_lowerCAmelCase			).to_tuple()

                     with self.subTest('''JIT Disabled'''			):
                           with jax.disable_jit():
                                 _lowerCAmelCase 			=  model_jitted(**_lowerCAmelCase			).to_tuple()

                     self.assertEqual(len(_lowerCAmelCase			)       ,     len(_lowerCAmelCase			)			)
                     for jitted_output, output in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           self.assertEqual(jitted_output.shape       ,     output.shape			)






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





@require_flax
class 		UpperCAmelCase						(						unittest.TestCase				):





   @cached_property
   def 	__lowerCAmelCase							(	self			):
         return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''			) if is_vision_available() else None





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''			)

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

         _lowerCAmelCase 			=  model(**_lowerCAmelCase			)

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

         _lowerCAmelCase 			=  jnp.array([-0.4_180, -1.5_051, -3.4_836]			)

         self.assertTrue(jnp.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=" ")