



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  __magic_name__   (							unittest.TestCase						):






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







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






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[Any]:
								'''simple docstring'''







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

								UpperCAmelCase         =     self.get_config()

								return config, pixel_values






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Any:
								'''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 SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[str]      )				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxRegNetModel(config=UpperCamelCase__      )
								UpperCAmelCase         =     model(UpperCamelCase__      )

								# 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 SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     str      )				->					Union[str, Any]:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.prepare_config_and_inputs()
								UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
								UpperCAmelCase         =     {"pixel_values": pixel_values}
								return config, inputs_dict





@require_flax
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       List[str]          =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

		lowercase      :       Tuple          =False
		lowercase      :       Union[str, Any]          =False
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					None:
								'''simple docstring'''







								UpperCAmelCase         =     FlaxRegNetModelTester(self      )
								UpperCAmelCase         =     ConfigTester(self				,		config_class=UpperCamelCase__				,		has_text_modality=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[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 SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Dict:
								'''simple docstring'''







								return






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )






		@unittest.skip(reason="RegNet does not use inputs_embeds"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''simple docstring'''







								pass






		@unittest.skip(reason="RegNet does not support input and output embeddings"      )
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Optional[Any]:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCAmelCase         =     ["pixel_values"]
														self.assertListEqual(arg_names[:1]				,		UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
								'''simple docstring'''







								def check_hidden_states_output(UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Any      ):
														UpperCAmelCase         =     model_class(UpperCamelCase__      )

														UpperCAmelCase         =     model(**self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )      )

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

														UpperCAmelCase         =     self.model_tester.num_stages
														self.assertEqual(len(UpperCamelCase__      )				,		expected_num_stages + 1      )

								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														UpperCAmelCase         =     True
														check_hidden_states_output(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )

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

														check_hidden_states_output(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																				UpperCAmelCase         =     self._prepare_for_class(UpperCamelCase__				,		UpperCamelCase__      )
																				UpperCAmelCase         =     model_class(UpperCamelCase__      )

																				@jax.jit
																				def model_jitted(UpperCamelCase__      :     Optional[int]				,		**UpperCamelCase__      :     Union[str, Any]      ):
																										return model(pixel_values=UpperCamelCase__				,		**UpperCamelCase__      )

																				with self.subTest("JIT Enabled"      ):
																										UpperCAmelCase         =     model_jitted(**UpperCamelCase__      ).to_tuple()

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

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

def    lowerCamelCase_() ->       Union[str, Any]:
						UpperCAmelCase         =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image





@require_flax
class  __magic_name__   (							unittest.TestCase						):






		@cached_property
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[Any]:
								'''simple docstring'''







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







		@slow
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Tuple:
								'''simple docstring'''







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

								UpperCAmelCase         =     self.default_image_processor
								UpperCAmelCase         =     prepare_img()
								UpperCAmelCase         =     image_processor(images=UpperCamelCase__				,		return_tensors="np"      )

								UpperCAmelCase         =     model(**UpperCamelCase__      )

								# verify the logits
								UpperCAmelCase         =     (1, 10_00)
								self.assertEqual(outputs.logits.shape				,		UpperCamelCase__      )

								UpperCAmelCase         =     jnp.array([-0.41_80, -1.50_51, -3.48_36]      )

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








import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation





class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("swish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					List[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("silu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.SiLU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("mish"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.Mish      )

								self.assertEqual(act(torch.tensor(-2_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )







		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     get_activation("gelu"      )

								self.assertIsInstance(UpperCamelCase__				,		nn.GELU      )

								self.assertEqual(act(torch.tensor(-1_00				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertNotEqual(act(torch.tensor(-1				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(0				,		dtype=torch.floataa      )      ).item()				,		0      )
								self.assertEqual(act(torch.tensor(20				,		dtype=torch.floataa      )      ).item()				,		20      )



