

import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class 				UpperCAmelCase_		(			lowercase__   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    str:
												_a	:    Dict         =		self.config_class(**self.inputs_dict       )
												self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,							'''tf_padding'''       )       )
												self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,							'''depth_multiplier'''       )       )



class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_3       ,							_a=3       ,							_a=3_2       ,							_a=0.25       ,							_a=8       ,							_a=8       ,							_a=6       ,							_a=3_2       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a="relu6"       ,							_a=1_2_8_0       ,							_a=0.1       ,							_a=0.02       ,							_a=True       ,							_a=True       ,							_a=1_0       ,							_a=None       ,							)    ->    Optional[int]:
												_a	:    Any         =		parent
												_a	:    List[str]         =		batch_size
												_a	:    Optional[int]         =		num_channels
												_a	:    Optional[int]         =		image_size
												_a	:    List[Any]         =		depth_multiplier
												_a	:    List[Any]         =		depth_divisible_by
												_a	:    Optional[Any]         =		min_depth
												_a	:    Tuple         =		expand_ratio
												_a	:    Tuple         =		tf_padding
												_a	:    Dict         =		output_stride
												_a	:    Optional[int]         =		first_layer_is_expansion
												_a	:    int         =		finegrained_output
												_a	:    Tuple         =		hidden_act
												_a	:    Any         =		last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier       )
												_a	:    List[str]         =		classifier_dropout_prob
												_a	:    Any         =		use_labels
												_a	:    Dict         =		is_training
												_a	:    Optional[Any]         =		num_labels
												_a	:    str         =		initializer_range
												_a	:    str         =		scope


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

												_a	:    Any         =		None
												_a	:    Optional[int]         =		None
												if self.use_labels:
																	_a	:    List[Any]         =		ids_tensor([self.batch_size]       ,							self.num_labels       )
																	_a	:    Tuple         =		ids_tensor([self.batch_size, self.image_size, self.image_size]       ,							self.num_labels       )

												_a	:    Dict         =		self.get_config()

												return config, pixel_values, labels, pixel_labels


							def 				__lowercase				(			self       )    ->    int:
												return MobileNetVaConfig(
												    num_channels=self.num_channels       ,							image_size=self.image_size       ,							depth_multiplier=self.depth_multiplier       ,							depth_divisible_by=self.depth_divisible_by       ,							min_depth=self.min_depth       ,							expand_ratio=self.expand_ratio       ,							output_stride=self.output_stride       ,							first_layer_is_expansion=self.first_layer_is_expansion       ,							finegrained_output=self.finegrained_output       ,							hidden_act=self.hidden_act       ,							tf_padding=self.tf_padding       ,							classifier_dropout_prob=self.classifier_dropout_prob       ,							initializer_range=self.initializer_range       ,							)


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		MobileNetVaModel(config=SCREAMING_SNAKE_CASE__       )
												model.to(SCREAMING_SNAKE_CASE__       )
												model.eval()
												_a	:    List[Any]         =		model(SCREAMING_SNAKE_CASE__       )
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,							(
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )       ,							)
												self.parent.assertEqual(
												    result.pooler_output.shape       ,							(self.batch_size, self.last_hidden_size)       ,							)


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Union[str, Any]:
												_a	:    str         =		self.num_labels
												_a	:    Union[str, Any]         =		MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__       )
												model.to(SCREAMING_SNAKE_CASE__       )
												model.eval()
												_a	:    Optional[Any]         =		model(SCREAMING_SNAKE_CASE__       ,							labels=SCREAMING_SNAKE_CASE__       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    Any         =		self.num_labels
												_a	:    str         =		MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE__       )
												model.to(SCREAMING_SNAKE_CASE__       )
												model.eval()
												_a	:    Union[str, Any]         =		model(SCREAMING_SNAKE_CASE__       )
												self.parent.assertEqual(
												    result.logits.shape       ,							(
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )       ,							)
												_a	:    Any         =		model(SCREAMING_SNAKE_CASE__       ,							labels=SCREAMING_SNAKE_CASE__       )
												self.parent.assertEqual(
												    result.logits.shape       ,							(
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )       ,							)




							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    List[Any]         =		self.prepare_config_and_inputs()
												_a	:    str         =		config_and_inputs
												_a	:    Any         =		{'pixel_values': pixel_values}
												return config, inputs_dict



@require_torch
class 				UpperCAmelCase_		(			lowercase__				,       lowercase__				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    (
							    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__       :						List[str]	    =    (
							    {
							        '''feature-extraction''': MobileNetVaModel,
							        '''image-classification''': MobileNetVaForImageClassification,
							        '''image-segmentation''': MobileNetVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__       :						int	    =    False
							UpperCAmelCase__       :						str	    =    False
							UpperCAmelCase__       :						str	    =    False
							UpperCAmelCase__       :						Optional[Any]	    =    False


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Dict         =		MobileNetVaModelTester(self       )
												_a	:    Union[str, Any]         =		MobileNetVaConfigTester(self       ,							config_class=SCREAMING_SNAKE_CASE__       ,							has_text_modality=SCREAMING_SNAKE_CASE__       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds'''       )
							def 				__lowercase				(			self       )    ->    List[Any]:
												pass


							@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings'''       )
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												pass


							@unittest.skip(reason='''MobileNetV2 does not output attentions'''       )
							def 				__lowercase				(			self       )    ->    List[str]:
												pass


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[Any]         =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a	:    Tuple         =		['pixel_values']
																	self.assertListEqual(arg_names[:1]       ,							SCREAMING_SNAKE_CASE__       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												def check_hidden_states_output(_a       ,							_a       ,							_a       ):
																	_a	:    Optional[Any]         =		model_class(SCREAMING_SNAKE_CASE__       )
																	model.to(SCREAMING_SNAKE_CASE__       )
																	model.eval()

																	with torch.no_grad():
																						_a	:    Tuple         =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       )       )

																	_a	:    Optional[Any]         =		outputs.hidden_states

																	_a	:    Optional[int]         =		1_6
																	self.assertEqual(len(SCREAMING_SNAKE_CASE__       )       ,							SCREAMING_SNAKE_CASE__       )

												_a	:    str         =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a	:    List[Any]         =		True
																	check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	_a	:    Tuple         =		True

																	check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       ,							SCREAMING_SNAKE_CASE__       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Dict         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__       )


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE__       )




							@slow
							def 				__lowercase				(			self       )    ->    Dict:
												for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a	:    Any         =		MobileNetVaModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )



def    __UpperCAmelCase (	)			->     Dict:

					"""simple docstring"""

					_a	:    Union[str, 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 				__lowercase				(			self       )    ->    Optional[Any]:
												return (
												    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224'''       ) if is_vision_available() else None
												)


							@slow
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Any         =		MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224'''       ).to(SCREAMING_SNAKE_CASE__       )

												_a	:    Any         =		self.default_image_processor
												_a	:    int         =		prepare_img()
												_a	:    Optional[Any]         =		image_processor(images=SCREAMING_SNAKE_CASE__       ,							return_tensors='''pt'''       ).to(SCREAMING_SNAKE_CASE__       )

												# forward pass
												with torch.no_grad():
																	_a	:    Dict         =		model(**SCREAMING_SNAKE_CASE__       )

												# verify the logits
												_a	:    Optional[int]         =		torch.Size((1, 1_0_0_1)       )
												self.assertEqual(outputs.logits.shape       ,							SCREAMING_SNAKE_CASE__       )

												_a	:    Tuple         =		torch.tensor([0.2445, -1.1993, 0.1905]       ).to(SCREAMING_SNAKE_CASE__       )

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




							@slow
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Union[str, Any]         =		MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''       )
												_a	:    Optional[Any]         =		model.to(SCREAMING_SNAKE_CASE__       )

												_a	:    int         =		MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''       )

												_a	:    Dict         =		prepare_img()
												_a	:    List[Any]         =		image_processor(images=SCREAMING_SNAKE_CASE__       ,							return_tensors='''pt'''       ).to(SCREAMING_SNAKE_CASE__       )

												# forward pass
												with torch.no_grad():
																	_a	:    Dict         =		model(**SCREAMING_SNAKE_CASE__       )
												_a	:    str         =		outputs.logits

												# verify the logits
												_a	:    str         =		torch.Size((1, 2_1, 6_5, 6_5)       )
												self.assertEqual(logits.shape       ,							SCREAMING_SNAKE_CASE__       )

												_a	:    Dict         =		torch.tensor(
												    [
												        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
												        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
												        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
												    ]       ,							device=SCREAMING_SNAKE_CASE__       ,							)

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







def        SCREAMING_SNAKE_CASE_						(			__A :			list[int]  ,    __A :			str				)	->		list[int]:

       """simple docstring"""

       a_					:		Any							=  int(__A				)

       # Initialize Result
       a_					:		Tuple							=  []

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

       return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase_      : Union[str, Any]  					=       []
   UpperCAmelCase_      : Union[str, Any]  					=       '0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      UpperCAmelCase_      : List[Any]  					=       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_      : str  					=       input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase_      : List[Any]  					=       [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase_      : str  					=       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_      : Optional[Any]  					=       find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')


