"""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 			_lowerCamelCase      (unittest.TestCase  ):







				def __init__(			self     :							List[Any]      ,       lowerCamelCase_     :							Dict      ,       lowerCamelCase_     :							Tuple=3      ,       lowerCamelCase_     :							Dict=3_2      ,       lowerCamelCase_     :							Optional[int]=3      ,       lowerCamelCase_     :							Any=1_0      ,       lowerCamelCase_     :							List[Any]=[1_0, 2_0, 3_0, 4_0]      ,       lowerCamelCase_     :							Dict=[1, 1, 2, 1]      ,       lowerCamelCase_     :							Any=True      ,       lowerCamelCase_     :							Tuple=True      ,       lowerCamelCase_     :							str="relu"      ,       lowerCamelCase_     :							int=3      ,       lowerCamelCase_     :							int=None      ,       ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     Dict      					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

					_lowercase			:     Union[str, Any]      					=				self.get_config()

					return config, pixel_values







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""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 				__UpperCAmelCase       (			self     :							List[str]      ,       lowerCamelCase_     :							Any      ,       lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				FlaxResNetModel(config=lowerCamelCase_    )
					_lowercase			:     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 // 3_2, self.image_size // 3_2)      ,       )







				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							Optional[Any]      ,       lowerCamelCase_     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				self.num_labels
					_lowercase			:     List[str]      					=				FlaxResNetForImageClassification(config=lowerCamelCase_    )
					_lowercase			:     Any      					=				model(lowerCamelCase_    )
					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)    )







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






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



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

				_snake_case  =	False
				_snake_case  =	False
				_snake_case  =	False







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				FlaxResNetModelTester(self    )
					_lowercase			:     Tuple      					=				ConfigTester(self      ,       config_class=lowerCamelCase_      ,       has_text_modality=lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""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 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					return







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					_lowercase			:     str      					=				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 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					pass







				@unittest.skip(reason='ResNet does not support input and output embeddings'    )
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					pass







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					_lowercase					,   _lowercase			:     Any      					=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase			:     int      					=				model_class(lowerCamelCase_    )
						_lowercase			:     Optional[int]      					=				inspect.signature(model.__call__    )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowercase			:     str      					=				[*signature.parameters.keys()]

						_lowercase			:     Dict      					=				['pixel_values']
						self.assertListEqual(arg_names[:1]      ,       lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					def check_hidden_states_output(lowerCamelCase_     :							List[str]      ,       lowerCamelCase_     :							Optional[int]      ,       lowerCamelCase_     :							Optional[int]    ):
						_lowercase			:     str      					=				model_class(lowerCamelCase_    )

						_lowercase			:     Union[str, Any]      					=				model(**self._prepare_for_class(lowerCamelCase_      ,       lowerCamelCase_    )    )

						_lowercase			:     Union[str, Any]      					=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						_lowercase			:     int      					=				self.model_tester.num_stages
						self.assertEqual(len(lowerCamelCase_    )      ,       expected_num_stages + 1    )







				@unittest.skip(reason='ResNet does not use feedforward chunking'    )
				def 				__UpperCAmelCase       (			self     :							List[str]    ):

					"""simple docstring"""






					pass







				def 				__UpperCAmelCase       (			self     :							str    ):

					"""simple docstring"""






					_lowercase					,   _lowercase			:     List[str]      					=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__    ):
							_lowercase			:     Dict      					=				self._prepare_for_class(lowerCamelCase_      ,       lowerCamelCase_    )
							_lowercase			:     List[str]      					=				model_class(lowerCamelCase_    )

							@jax.jit
							def model_jitted(lowerCamelCase_     :							Tuple      ,       **lowerCamelCase_     :							Dict    ):
								return model(pixel_values=lowerCamelCase_      ,       **lowerCamelCase_    )

							with self.subTest('JIT Enabled'    ):
								_lowercase			:     Any      					=				model_jitted(**lowerCamelCase_    ).to_tuple()

							with self.subTest('JIT Disabled'    ):
								with jax.disable_jit():
									_lowercase			:     Union[str, 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   __lowerCAmelCase(		):






	"""simple docstring"""





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







@require_flax
class 			_lowerCamelCase      (unittest.TestCase  ):







				@cached_property
				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






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







				@slow
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					_lowercase			:     Union[str, Any]      					=				FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'    )

					_lowercase			:     List[Any]      					=				self.default_image_processor
					_lowercase			:     Optional[int]      					=				prepare_img()
					_lowercase			:     str      					=				image_processor(images=lowerCamelCase_      ,       return_tensors='np'    )

					_lowercase			:     List[Any]      					=				model(**lowerCamelCase_    )

					# verify the logits
					_lowercase			:     Union[str, Any]      					=				(1, 1_0_0_0)
					self.assertEqual(outputs.logits.shape      ,       lowerCamelCase_    )

					_lowercase			:     Any      					=				jnp.array([-11.1069, -9.7877, -8.3777]    )

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




"""simple docstring"""







from __future__ import annotations

import math
import random


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Optional[int]      					=				d * e - 1
	_lowercase			:     int      					=				0
	_lowercase			:     Union[str, Any]      					=				0
	while p == 0:
		_lowercase			:     int      					=				random.randint(2  ,n - 1      )
		_lowercase			:     str      					=				k
		while True:
			if t % 2 == 0:
				_lowercase			:     List[Any]      					=				t // 2
				_lowercase			:     int      					=				(g**t) % n
				_lowercase			:     Tuple      					=				math.gcd(x - 1  ,__UpperCAmelCase      )
				if x > 1 and y > 1:
					_lowercase			:     List[str]      					=				y
					_lowercase			:     str      					=				n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



