





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, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						def UpperCAmelCase_			( self : str		)							->      int:







							"""simple docstring"""


							snake_case_						:     str     =		self.config_class(**self.inputs_dict		)
							self.parent.assertTrue(hasattr(_A						,				'tf_padding'		)		)
							self.parent.assertTrue(hasattr(_A						,				'depth_multiplier'		)		)
class SCREAMING_SNAKE_CASE_  :






						def __init__( self : Any						,				_A : List[str]						,				_A : List[str]=13						,				_A : List[str]=3						,				_A : Optional[Any]=32						,				_A : Tuple=0.2_5						,				_A : Union[str, Any]=8						,				_A : List[Any]=True						,				_A : Tuple=1024						,				_A : List[Any]=32						,				_A : Any="relu6"						,				_A : Tuple=0.1						,				_A : Union[str, Any]=0.0_2						,				_A : Optional[int]=True						,				_A : List[Any]=True						,				_A : List[str]=10						,				_A : Dict=None						,				)							->      Dict:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		parent
							snake_case_						:     List[str]     =		batch_size
							snake_case_						:     Optional[int]     =		num_channels
							snake_case_						:     int     =		image_size
							snake_case_						:     Optional[Any]     =		depth_multiplier
							snake_case_						:     str     =		min_depth
							snake_case_						:     Any     =		tf_padding
							snake_case_						:     str     =		int(last_hidden_size * depth_multiplier		)
							snake_case_						:     List[Any]     =		output_stride
							snake_case_						:     Optional[int]     =		hidden_act
							snake_case_						:     str     =		classifier_dropout_prob
							snake_case_						:     Any     =		use_labels
							snake_case_						:     Optional[Any]     =		is_training
							snake_case_						:     Tuple     =		num_labels
							snake_case_						:     Tuple     =		initializer_range
							snake_case_						:     Optional[int]     =		scope






						def UpperCAmelCase_			( self : List[Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							snake_case_						:     Union[str, Any]     =		None
							snake_case_						:     int     =		None
							if self.use_labels:
								snake_case_						:     Tuple     =		ids_tensor([self.batch_size]						,				self.num_labels		)
								snake_case_						:     Tuple     =		ids_tensor([self.batch_size, self.image_size, self.image_size]						,				self.num_labels		)

							snake_case_						:     List[str]     =		self.get_config()

							return config, pixel_values, labels, pixel_labels






						def UpperCAmelCase_			( self : Tuple		)							->      str:







							"""simple docstring"""


							return MobileNetVaConfig(
							    num_channels=self.num_channels						,				image_size=self.image_size						,				depth_multiplier=self.depth_multiplier						,				min_depth=self.min_depth						,				tf_padding=self.tf_padding						,				hidden_act=self.hidden_act						,				classifier_dropout_prob=self.classifier_dropout_prob						,				initializer_range=self.initializer_range						,				)






						def UpperCAmelCase_			( self : int						,				_A : Any						,				_A : List[str]						,				_A : Any						,				_A : List[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		MobileNetVaModel(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     List[Any]     =		model(_A		)
							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,
							    )						,				)






						def UpperCAmelCase_			( self : Any						,				_A : Optional[Any]						,				_A : Optional[Any]						,				_A : Any						,				_A : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		self.num_labels
							snake_case_						:     str     =		MobileNetVaForImageClassification(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Union[str, Any]     =		model(_A						,				labels=_A		)
							self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)		)






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.prepare_config_and_inputs()
							snake_case_						:     Dict     =		config_and_inputs
							snake_case_						:     List[str]     =		{'pixel_values': pixel_values}
							return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Tuple	 =    (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
						__magic_name__:     Any	 =    (
						    {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						__magic_name__:     Tuple	 =    False
						__magic_name__:     str	 =    False
						__magic_name__:     int	 =    False
						__magic_name__:     Union[str, Any]	 =    False






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		MobileNetVaModelTester(self		)
							snake_case_						:     Tuple     =		MobileNetVaConfigTester(self						,				config_class=_A						,				has_text_modality=_A		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      List[str]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						@unittest.skip(reason='MobileNetV1 does not use inputs_embeds'		)
						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							pass






						@unittest.skip(reason='MobileNetV1 does not support input and output embeddings'		)
						def UpperCAmelCase_			( self : int		)							->      Union[str, Any]:







							"""simple docstring"""


							pass






						@unittest.skip(reason='MobileNetV1 does not output attentions'		)
						def UpperCAmelCase_			( self : Optional[Any]		)							->      Tuple:







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Optional[int]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.model_tester.prepare_config_and_inputs_for_common()

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

								snake_case_						:     int     =		['pixel_values']
								self.assertListEqual(arg_names[:1]						,				_A		)






						def UpperCAmelCase_			( self : List[str]		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A		)






						def UpperCAmelCase_			( self : Dict		)							->      Optional[Any]:







							"""simple docstring"""


							def check_hidden_states_output(_A : int						,				_A : str						,				_A : Tuple		):
								snake_case_						:     Dict     =		model_class(_A		)
								model.to(_A		)
								model.eval()

								with torch.no_grad():
									snake_case_						:     Optional[Any]     =		model(**self._prepare_for_class(_A						,				_A		)		)

								snake_case_						:     str     =		outputs.hidden_states

								snake_case_						:     Dict     =		26
								self.assertEqual(len(_A		)						,				_A		)

							snake_case_						:     Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_						:     Union[str, Any]     =		True
								check_hidden_states_output(_A						,				_A						,				_A		)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_						:     Optional[int]     =		True

								check_hidden_states_output(_A						,				_A						,				_A		)






						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A		)






						@slow
						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						:     Tuple     =		MobileNetVaModel.from_pretrained(_A		)
								self.assertIsNotNone(_A		)


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     str     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image


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






						@cached_property
						def UpperCAmelCase_			( self : str		)							->      List[Any]:







							"""simple docstring"""


							return (
							    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'		) if is_vision_available() else None
							)






						@slow
						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Tuple     =		MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'		).to(_A		)

							snake_case_						:     int     =		self.default_image_processor
							snake_case_						:     List[Any]     =		prepare_img()
							snake_case_						:     List[Any]     =		image_processor(images=_A						,				return_tensors='pt'		).to(_A		)

							# forward pass
							with torch.no_grad():
								snake_case_						:     int     =		model(**_A		)

							# verify the logits
							snake_case_						:     Dict     =		torch.Size((1, 1001)		)
							self.assertEqual(outputs.logits.shape						,				_A		)

							snake_case_						:     Tuple     =		torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		).to(_A		)

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













def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		int(__a				)

	# Initialize Result
	snake_case_						:     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__":
						_SCREAMING_SNAKE_CASE 		=						[]
						_SCREAMING_SNAKE_CASE 		=						"""0"""

						if (
						    input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
						    == "y"
						):
												_SCREAMING_SNAKE_CASE 		=						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()))
												_SCREAMING_SNAKE_CASE 		=						input("""Enter the change you want to make in Indian Currency: """).strip()
						else:
												# All denominations of Indian Currency if user does not enter
												_SCREAMING_SNAKE_CASE 		=						[1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												_SCREAMING_SNAKE_CASE 		=						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}: ''')
												_SCREAMING_SNAKE_CASE 		=						find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=""" """)

