
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        lowercase	(      unittest.TestCase	):




							def __init__(       self     ,A__     ,A__=3     ,A__=3_2     ,A__=3     ,A__=1_0     ,A__=[1_0, 2_0, 3_0, 4_0]     ,A__=[1, 1, 2, 1]     ,A__=True     ,A__=True     ,A__="relu"     ,A__=3     ,A__=None     ,):
												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 				A__   (       self):
												lowercase					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

												lowercase					=		self.get_config()

												return config, pixel_values




							def 				A__   (       self):
												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 				A__   (       self     ,A__     ,A__):
												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 // 3_2, self.image_size // 3_2)     ,)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		self.num_labels
												lowercase					=		FlaxRegNetForImageClassification(config=A__)
												lowercase					=		model(A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))





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







@require_flax
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Union[str, Any]       =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

							lowercase_ : Optional[int]       =False
							lowercase_ : str       =False
							lowercase_ : Any       =False




							def 				A__   (       self):
												lowercase					=		FlaxRegNetModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,has_text_modality=A__)




							def 				A__   (       self):
												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):
												return




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 				A__   (       self):
												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 				A__   (       self):
												pass




							@unittest.skip(reason='''RegNet does not support input and output embeddings''')
							def 				A__   (       self):
												pass




							def 				A__   (       self):
												lowercase			,		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 				A__   (       self):
												def check_hidden_states_output(A__     ,A__     ,A__):
																	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			,		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 				A__   (       self):
												lowercase			,		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(A__     ,**A__):
																											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					(							):
					'''simple docstring'''
					lowercase					=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image







@require_flax
class        lowercase	(      unittest.TestCase	):




							@cached_property
							def 				A__   (       self):
												return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''') if is_vision_available() else None





							@slow
							def 				A__   (       self):
												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, 1_0_0_0)
												self.assertEqual(outputs.logits.shape     ,A__)

												lowercase					=		jnp.array([-0.4180, -1.5051, -3.4836])

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






class        lowercase	:




							def __init__(       self     ,A__):
												lowercase					=		val
												lowercase					=		None
												lowercase					=		None





							def 				A__   (       self     ,A__):
												if self.val:
																	if val < self.val:
																						if self.left is None:
																											lowercase					=		Node(A__)
																						else:
																											self.left.insert(A__)
																	elif val > self.val:
																						if self.right is None:
																											lowercase					=		Node(A__)
																						else:
																											self.right.insert(A__)
												else:
																	lowercase					=		val



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Recursive traversal
					if root:
										inorder(root.left				,    lowerCAmelCase__	)
										res.append(root.val	)
										inorder(root.right				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					# Build BST
					if len(lowerCAmelCase__	) == 0:
										return arr
					lowercase					=		Node(arr[0]	)
					for i in range(1				,    len(lowerCAmelCase__	)	):
										root.insert(arr[i]	)
					# Traverse BST in order.
					lowercase					=		[]
					inorder(lowerCAmelCase__				,    lowerCAmelCase__	)
					return res


if __name__ == "__main__":
							print(tree_sort([10, 1, 3, 2, 9, 14, 13]))




