


'''simple docstring'''
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					str	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]=13	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Union[str, Any]=3	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					List[Any]=[10, 20, 30, 40]	,				_lowerCamelCase				:					Optional[int]=[2, 2, 3, 2]	,				_lowerCamelCase				:					Dict=True	,				_lowerCamelCase				:					List[str]=True	,				_lowerCamelCase				:					Dict=37	,				_lowerCamelCase				:					List[Any]="gelu"	,				_lowerCamelCase				:					str=10	,				_lowerCamelCase				:					Optional[Any]=0.02	,				_lowerCamelCase				:					List[Any]=3	,				_lowerCamelCase				:					List[str]=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = batch_size
										__magic_name__	  = image_size
										__magic_name__	  = num_channels
										__magic_name__	  = num_stages
										__magic_name__	  = hidden_sizes
										__magic_name__	  = depths
										__magic_name__	  = is_training
										__magic_name__	  = use_labels
										__magic_name__	  = intermediate_size
										__magic_name__	  = hidden_act
										__magic_name__	  = type_sequence_label_size
										__magic_name__	  = initializer_range
										__magic_name__	  = scope
						def 					__A					( self				:					Tuple  )							->					Tuple:
										__magic_name__	  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

										__magic_name__	  = self.get_config()

										return config, pixel_values, labels
						def 					__A					( self				:					int  )							->					int:
										return ConvNextConfig(
										    num_channels=self.num_channels	,				hidden_sizes=self.hidden_sizes	,				depths=self.depths	,				num_stages=self.num_stages	,				hidden_act=self.hidden_act	,				is_decoder=_lowerCamelCase	,				initializer_range=self.initializer_range	,				)
						def 					__A					( self				:					str	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Optional[Any]  )							->					Tuple:
										__magic_name__	  = TFConvNextModel(config=_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase	,				training=_lowerCamelCase  )
										# expected last hidden states: B, C, H // 32, W // 32
										self.parent.assertEqual(
										    result.last_hidden_state.shape	,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,				)
						def 					__A					( self				:					int	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]  )							->					Any:
										__magic_name__	  = self.type_sequence_label_size
										__magic_name__	  = TFConvNextForImageClassification(_lowerCamelCase  )
										__magic_name__	  = model(_lowerCamelCase	,				labels=_lowerCamelCase	,				training=_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.type_sequence_label_size)  )

						def 					__A					( self				:					Dict  )							->					Any:
										__magic_name__	  = self.prepare_config_and_inputs()
										__magic_name__  ,      __magic_name__  ,      __magic_name__	  = config_and_inputs
										__magic_name__	  = {"pixel_values": pixel_values}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Dict         =     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
						UpperCAmelCase__	:       str         =     (
						    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       Any         =     False
						UpperCAmelCase__	:       Optional[Any]         =     False
						UpperCAmelCase__	:       Optional[Any]         =     False
						UpperCAmelCase__	:       Dict         =     False
						def 					__A					( self				:					int  )							->					Tuple:
										__magic_name__	  = TFConvNextModelTester(self  )
										__magic_name__	  = ConfigTester(
										    self	,				config_class=_lowerCamelCase	,				has_text_modality=_lowerCamelCase	,				hidden_size=37	,				)
						@unittest.skip(reason="ConvNext does not use inputs_embeds"  )
						def 					__A					( self				:					Dict  )							->					List[Any]:
										pass
						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"  )  ) == 0	,				reason="TF does not support backprop for grouped convolutions on CPU."	,				)
						@slow
						def 					__A					( self				:					Any  )							->					List[Any]:
										super().test_keras_fit()
						@unittest.skip(reason="ConvNext does not support input and output embeddings"  )
						def 					__A					( self				:					List[Any]  )							->					int:
										pass
						def 					__A					( self				:					List[str]  )							->					Optional[int]:
										__magic_name__  ,      __magic_name__	  = self.model_tester.prepare_config_and_inputs_for_common()

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

														__magic_name__	  = ["pixel_values"]
														self.assertListEqual(arg_names[:1]	,				_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices("GPU"  )  ) == 0	,				reason="TF does not support backprop for grouped convolutions on CPU."	,				)
						def 					__A					( self				:					str  )							->					str:
										super().test_dataset_conversion()
						def 					__A					( self				:					Optional[Any]  )							->					Union[str, Any]:
										def check_hidden_states_output(_lowerCamelCase				:					str	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]  ):
														__magic_name__	  = model_class(_lowerCamelCase  )

														__magic_name__	  = model(**self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase  )  )
														__magic_name__	  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

														# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]  )	,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,				)

										__magic_name__  ,      __magic_name__	  = self.model_tester.prepare_config_and_inputs_for_common()

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

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

														check_hidden_states_output(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]  )							->					Optional[int]:
										__magic_name__  ,      __magic_name__	  = self.model_tester.prepare_config_and_inputs_for_common()

										def check_equivalence(_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]={}  ):
														__magic_name__	  = model(_lowerCamelCase	,				return_dict=_lowerCamelCase	,				**_lowerCamelCase  )
														__magic_name__	  = model(_lowerCamelCase	,				return_dict=_lowerCamelCase	,				**_lowerCamelCase  ).to_tuple()

														def recursive_check(_lowerCamelCase				:					int	,				_lowerCamelCase				:					Union[str, Any]  ):
																		if isinstance(_lowerCamelCase	,				(List, Tuple)  ):
																						for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase	,				_lowerCamelCase  ):
																										recursive_check(_lowerCamelCase	,				_lowerCamelCase  )
																		elif tuple_object is None:
																						return
																		else:
																						self.assertTrue(
																						    all(tf.equal(_lowerCamelCase	,				_lowerCamelCase  )  )	,				msg=(
																						        "Tuple and dict output are not equal. Difference:"
																						        f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object  )  )}'
																						    )	,				)

																		recursive_check(_lowerCamelCase	,				_lowerCamelCase  )

										for model_class in self.all_model_classes:
														__magic_name__	  = model_class(_lowerCamelCase  )

														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase  )
														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase  )
														check_equivalence(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )
														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )
														check_equivalence(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase  )
														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase  )
														check_equivalence(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				{"output_hidden_states": True}  )

														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )
														__magic_name__	  = self._prepare_for_class(_lowerCamelCase	,				_lowerCamelCase	,				return_labels=_lowerCamelCase  )
														check_equivalence(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				{"output_hidden_states": True}  )
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					List[str]  )							->					List[Any]:
										__magic_name__	  = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"  )
										self.assertIsNotNone(_lowerCamelCase  )





def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image






@require_tf
@require_vision
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@cached_property
						def 					__A					( self				:					Any  )							->					Optional[int]:
										return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"  ) if is_vision_available() else None

						@slow
						def 					__A					( self				:					str  )							->					str:
										__magic_name__	  = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"  )

										__magic_name__	  = self.default_image_processor
										__magic_name__	  = prepare_img()
										__magic_name__	  = image_processor(images=_lowerCamelCase	,				return_tensors="tf"  )

										# forward pass
										__magic_name__	  = model(**_lowerCamelCase  )

										# verify the logits
										__magic_name__	  = tf.TensorShape((1, 10_00)  )
										self.assertEqual(outputs.logits.shape	,				_lowerCamelCase  )

										__magic_name__	  = tf.constant([-0.0_260, -0.4_739, 0.1_911]  )

										tf.debugging.assert_near(outputs.logits[0, :3]	,				_lowerCamelCase	,				atol=1e-4  )







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



