"""simple docstring"""




import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class 			__snake_case  (      unittest.TestCase     ):




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase=3   ,	__UpperCamelCase=32   ,	__UpperCamelCase=3   ,	__UpperCamelCase=10   ,	__UpperCamelCase=[10, 20, 30, 40]   ,	__UpperCamelCase=[1, 1, 2, 1]   ,	__UpperCamelCase=True   ,	__UpperCamelCase=True   ,	__UpperCamelCase="relu"   ,	__UpperCamelCase=3   ,	__UpperCamelCase=None   ,	)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				parent
									snake_case__ :     Dict 							=				batch_size
									snake_case__ :     Dict 							=				image_size
									snake_case__ :     Dict 							=				num_channels
									snake_case__ :     Tuple 							=				embeddings_size
									snake_case__ :     List[str] 							=				hidden_sizes
									snake_case__ :     List[str] 							=				depths
									snake_case__ :     Optional[int] 							=				is_training
									snake_case__ :     int 							=				use_labels
									snake_case__ :     Optional[int] 							=				hidden_act
									snake_case__ :     Union[str, Any] 							=				num_labels
									snake_case__ :     List[Any] 							=				scope
									snake_case__ :     Union[str, Any] 							=				len(_lowerCamelCase		)




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									snake_case__ :     int 							=				self.get_config()

									return config, pixel_values




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									return ResNetConfig(
									    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       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				FlaxResNetModel(config=_lowerCamelCase		)
									snake_case__ :     Optional[int] 							=				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       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				self.num_labels
									snake_case__ :     Tuple 							=				FlaxResNetForImageClassification(config=_lowerCamelCase		)
									snake_case__ :     Optional[Any] 							=				model(_lowerCamelCase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     str 							=				self.prepare_config_and_inputs()
									snake_case__							,  snake_case__ :     List[str] 							=				config_and_inputs
									snake_case__ :     List[Any] 							=				{'pixel_values': pixel_values}
									return config, inputs_dict





@require_flax
class 			__snake_case  (      _lowerCAmelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     Any 							=				FlaxResNetModelTester(self		)
									snake_case__ :     Optional[Any] 							=				ConfigTester(self   ,	config_class=_lowerCamelCase   ,	has_text_modality=_lowerCamelCase		)




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									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       __a      (       self		)					->  Any:



									'''simple docstring'''







									return




					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowerCamelCase		)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase		)




					@unittest.skip(reason='ResNet does not use inputs_embeds'		)
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									pass




					@unittest.skip(reason='ResNet does not support input and output embeddings'		)
					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Optional[int] 							=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													snake_case__ :     Tuple 							=				model_class(_lowerCamelCase		)
													snake_case__ :     Union[str, Any] 							=				inspect.signature(model.__call__		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													snake_case__ :     Optional[int] 							=				[*signature.parameters.keys()]

													snake_case__ :     str 							=				['pixel_values']
													self.assertListEqual(arg_names[:1]   ,	_lowerCamelCase		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									def check_hidden_states_output(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		):
													snake_case__ :     Optional[Any] 							=				model_class(_lowerCamelCase		)

													snake_case__ :     Optional[int] 							=				model(**self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase		)		)

													snake_case__ :     List[str] 							=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													snake_case__ :     List[Any] 							=				self.model_tester.num_stages
													self.assertEqual(len(_lowerCamelCase		)   ,	expected_num_stages + 1		)




					@unittest.skip(reason='ResNet does not use feedforward chunking'		)
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Optional[int] 							=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__		):
																	snake_case__ :     Any 							=				self._prepare_for_class(_lowerCamelCase   ,	_lowerCamelCase		)
																	snake_case__ :     List[Any] 							=				model_class(_lowerCamelCase		)

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

																	with self.subTest('JIT Enabled'		):
																					snake_case__ :     Dict 							=				model_jitted(**_lowerCamelCase		).to_tuple()

																	with self.subTest('JIT Disabled'		):
																					with jax.disable_jit():
																									snake_case__ :     Optional[Any] 							=				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__						(    )       -> int:
				snake_case__ :     Optional[int] 							=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
				return image





@require_flax
class 			__snake_case  (      unittest.TestCase     ):




					@cached_property
					def       __a      (       self		)					->  int:



									'''simple docstring'''







									return AutoImageProcessor.from_pretrained('microsoft/resnet-50'		) if is_vision_available() else None




					@slow
					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'		)

									snake_case__ :     List[Any] 							=				self.default_image_processor
									snake_case__ :     int 							=				prepare_img()
									snake_case__ :     int 							=				image_processor(images=_lowerCamelCase   ,	return_tensors='np'		)

									snake_case__ :     str 							=				model(**_lowerCamelCase		)

									# verify the logits
									snake_case__ :     Tuple 							=				(1, 1000)
									self.assertEqual(outputs.logits.shape   ,	_lowerCamelCase		)

									snake_case__ :     List[str] 							=				jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]		)

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



def 			UpperCamelCase__						(    A__   )       -> int:
				if number != int(A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if number < 0:
								raise ValueError('factorial() not defined for negative values'   )
				snake_case__ :     Optional[Any] 							=				1
				for i in range(1      ,			number + 1   ):
								value *= i
				return value
def 			UpperCamelCase__						(    A__   )       -> int:
				if not isinstance(A__      ,			A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if n < 0:
								raise ValueError('factorial() not defined for negative values'   )
				return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__					:       Any		=       int(input('''Enter a positive integer: ''').strip() or 0)
				print(F'''factorial{n} is {factorial(n)}''')


