'''simple docstring'''





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 		SCREAMING_SNAKE_CASE     (					unittest.TestCase	):







				'''simple docstring'''





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


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						parent
										_UpperCamelCase			:			List[Any]   			=						batch_size
										_UpperCamelCase			:			int   			=						image_size
										_UpperCamelCase			:			Optional[int]   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						embeddings_size
										_UpperCamelCase			:			List[Any]   			=						hidden_sizes
										_UpperCamelCase			:			str   			=						depths
										_UpperCamelCase			:			str   			=						is_training
										_UpperCamelCase			:			Tuple   			=						use_labels
										_UpperCamelCase			:			str   			=						hidden_act
										_UpperCamelCase			:			str   			=						num_labels
										_UpperCamelCase			:			List[Any]   			=						scope
										_UpperCamelCase			:			int   			=						len(lowercase__					)





				def  snake_case__				(						self					:						Any					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										_UpperCamelCase			:			int   			=						self.get_config()

										return config, pixel_values





				def  snake_case__				(						self					:						List[str]					)							->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  snake_case__				(						self					:						Any       ,		lowercase__					:						Dict       ,		lowercase__					:						Optional[Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						FlaxRegNetModel(config=lowercase__					)
										_UpperCamelCase			:			Tuple   			=						model(lowercase__					)

										# 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  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.num_labels
										_UpperCamelCase			:			Tuple   			=						FlaxRegNetForImageClassification(config=lowercase__					)
										_UpperCamelCase			:			Any   			=						model(lowercase__					)
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)







				def  snake_case__				(						self					:						List[str]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						config_and_inputs
										_UpperCamelCase			:			Tuple   			=						{"pixel_values": pixel_values}
										return config, inputs_dict





@require_flax
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						str					)							->None:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						FlaxRegNetModelTester(self					)
										_UpperCamelCase			:			str   			=						ConfigTester(self       ,		config_class=lowercase__       ,		has_text_modality=lowercase__					)





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


										'''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  snake_case__				(						self					:						List[str]					)							->List[str]:


										'''simple docstring'''




										return





				def  snake_case__				(						self					:						Tuple					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__					)





				def  snake_case__				(						self					:						Union[str, Any]					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__					)





				@unittest.skip(reason="RegNet does not use inputs_embeds"					)
				def  snake_case__				(						self					:						int					)							->str:


										'''simple docstring'''




										pass





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


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						List[Any]					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase			:			List[str]   			=						["pixel_values"]
																self.assertListEqual(arg_names[:1]       ,		lowercase__					)





				def  snake_case__				(						self					:						Optional[int]					)							->Dict:


										'''simple docstring'''




										def check_hidden_states_output(lowercase__					:						Union[str, Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Optional[int]					):
																_UpperCamelCase			:			Tuple   			=						model_class(lowercase__					)

																_UpperCamelCase			:			List[str]   			=						model(**self._prepare_for_class(lowercase__       ,		lowercase__					)					)

																_UpperCamelCase			:			List[Any]   			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																_UpperCamelCase			:			Optional[int]   			=						self.model_tester.num_stages
																self.assertEqual(len(lowercase__					)       ,		expected_num_stages + 1					)

										_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Optional[int]   			=						True
																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCamelCase			:			str   			=						True

																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)







				def  snake_case__				(						self					:						Optional[int]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			str   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__					):
																						_UpperCamelCase			:			List[Any]   			=						self._prepare_for_class(lowercase__       ,		lowercase__					)
																						_UpperCamelCase			:			Union[str, Any]   			=						model_class(lowercase__					)

																						@jax.jit
																						def model_jitted(lowercase__					:						Optional[int]       ,		**lowercase__					:						Dict					):
																												return model(pixel_values=lowercase__       ,		**lowercase__					)

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

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

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





def 						__A							(     )     ->			int:




						'''simple docstring'''
						_UpperCamelCase			:			List[Any]   			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
						return image






@require_flax
class 		SCREAMING_SNAKE_CASE     (					unittest.TestCase	):







				'''simple docstring'''





				@cached_property
				def  snake_case__				(						self					:						Tuple					)							->Dict:


										'''simple docstring'''




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







				@slow
				def  snake_case__				(						self					:						Any					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"					)

										_UpperCamelCase			:			Tuple   			=						self.default_image_processor
										_UpperCamelCase			:			Optional[int]   			=						prepare_img()
										_UpperCamelCase			:			int   			=						image_processor(images=lowercase__       ,		return_tensors="np"					)

										_UpperCamelCase			:			str   			=						model(**lowercase__					)

										# verify the logits
										_UpperCamelCase			:			Tuple   			=						(1, 1_000)
										self.assertEqual(outputs.logits.shape       ,		lowercase__					)

										_UpperCamelCase			:			List[str]   			=						jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]					)

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



'''simple docstring'''





from __future__ import annotations

from cmath import sqrt
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			tuple[complex, complex]:




						'''simple docstring'''

						if a == 0:
												raise ValueError("Coefficient 'a' must not be zero."					)
						_UpperCamelCase			:			List[str]   			=						b * b - 4 * a * c

						_UpperCamelCase			:			Dict   			=						(-b + sqrt(UpperCAmelCase					)) / (2 * a)
						_UpperCamelCase			:			Optional[int]   			=						(-b - sqrt(UpperCAmelCase					)) / (2 * a)

						return (
						    root_a.real if not root_a.imag else root_a,
						    root_a.real if not root_a.imag else root_a,
						)
def 						__A							(     )     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						quadratic_roots(a=5     ,b=6     ,c=1					)
						print(f'''The solutions are: {solutiona} and {solutiona}'''					)


if __name__ == "__main__":
			main()


