


import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Dict=3				, _UpperCAmelCase   :						Dict=32				, _UpperCAmelCase   :						List[Any]=3				, _UpperCAmelCase   :						Union[str, Any]=10				, _UpperCAmelCase   :						int=[10, 20, 30, 40]				, _UpperCAmelCase   :						List[Any]=[1, 1, 2, 1]				, _UpperCAmelCase   :						List[Any]=True				, _UpperCAmelCase   :						Optional[Any]=True				, _UpperCAmelCase   :						Tuple="relu"				, _UpperCAmelCase   :						Optional[int]=3				, _UpperCAmelCase   :						List[Any]=None				, )						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		num_channels
							lowercase__					      =		embeddings_size
							lowercase__					      =		hidden_sizes
							lowercase__					      =		depths
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		hidden_act
							lowercase__					      =		num_labels
							lowercase__					      =		scope
							lowercase__					      =		len(_A		)

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




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowercase__					      =		self.get_config()

							return config, pixel_values

					def   lowerCamelCase__					(self   :						Dict		)						->       List[str]:




							"""simple docstring"""


							return RegNetConfig(
							    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   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		FlaxRegNetModel(config=_A		)
							lowercase__					      =		model(_A		)

							# 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   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						List[Any]		)						->       str:




							"""simple docstring"""


							lowercase__					      =		self.num_labels
							lowercase__					      =		FlaxRegNetForImageClassification(config=_A		)
							lowercase__					      =		model(_A		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)		)


					def   lowerCamelCase__					(self   :						Any		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__					      =		config_and_inputs
							lowercase__					      =		{'pixel_values': pixel_values}
							return config, inputs_dict






@require_flax
class 							A   (	snake_case_			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						str		)						->       None:




							"""simple docstring"""


							lowercase__					      =		FlaxRegNetModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_A				, has_text_modality=_A		)

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




							"""simple docstring"""


							return

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A		)

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A		)

					@unittest.skip(reason="""RegNet does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""RegNet does not support input and output embeddings"""		)
					def   lowerCamelCase__					(self   :						Union[str, Any]		)						->       Dict:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						str		)						->       str:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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

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




							"""simple docstring"""


							def check_hidden_states_output(_UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Any				, _UpperCAmelCase   :						int		):
									lowercase__					      =		model_class(_A		)

									lowercase__					      =		model(**self._prepare_for_class(_A				, _A		)		)

									lowercase__					      =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase__					      =		self.model_tester.num_stages
									self.assertEqual(len(_A		)				, expected_num_stages + 1		)

							lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		True
									check_hidden_states_output(_A				, _A				, _A		)

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

									check_hidden_states_output(_A				, _A				, _A		)


					def   lowerCamelCase__					(self   :						List[str]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__		):
											lowercase__					      =		self._prepare_for_class(_A				, _A		)
											lowercase__					      =		model_class(_A		)

											@jax.jit
											def model_jitted(_UpperCAmelCase   :						Optional[int]				, **_UpperCAmelCase   :						List[str]		):
													return model(pixel_values=_A				, **_A		)

											with self.subTest("""JIT Enabled"""		):
													lowercase__					      =		model_jitted(**_A		).to_tuple()

											with self.subTest("""JIT Disabled"""		):
													with jax.disable_jit():
															lowercase__					      =		model_jitted(**_A		).to_tuple()

											self.assertEqual(len(_A		)				, len(_A		)		)
											for jitted_output, output in zip(_A				, _A		):
													self.assertEqual(jitted_output.shape				, output.shape		)






def 			UpperCamelCase (     )       ->  Dict:
		"""simple docstring"""




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







@require_flax
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






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




							"""simple docstring"""


							return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""		) if is_vision_available() else None


					@slow
					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		image_processor(images=_A				, return_tensors="""np"""		)

							lowercase__					      =		model(**_A		)

							# verify the logits
							lowercase__					      =		(1, 1000)
							self.assertEqual(outputs.logits.shape				, _A		)

							lowercase__					      =		jnp.array([-0.4_180, -1.5_051, -3.4_836]		)

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





def 			UpperCamelCase (     __magic_name__					:							str					)       ->  List[str]:  # noqa: E741
		"""simple docstring"""




		lowercase__					      =		len(__magic_name__					)
		lowercase__					      =		0
		lowercase__					      =		[0] * n
		lowercase__					      =		[False] * n
		lowercase__					      =		[False] * n

		def dfs(__magic_name__					:							str   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							str   ,   __magic_name__					:							Any					):
				if parent == root:
						out_edge_count += 1
				lowercase__					      =		True
				lowercase__					      =		at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
								lowercase__					      =		min(low[at]   ,   low[to]					)

								# AP found via bridge
								if at < low[to]:
										lowercase__					      =		True
								# AP found via cycle
								if at == low[to]:
										lowercase__					      =		True
						else:
								lowercase__					      =		min(low[at]   ,   __magic_name__					)
				return out_edge_count

		for i in range(__magic_name__					):
				if not visited[i]:
						lowercase__					      =		0
						lowercase__					      =		dfs(__magic_name__   ,   __magic_name__   ,   -1   ,   __magic_name__					)
						lowercase__					      =		out_edge_count > 1

		for x in range(len(__magic_name__					)					):
				if is_art[x] is True:
						print(__magic_name__					)


# Adjacency list of graph
A	:		List[str]    =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)

