





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   UpperCamelCase_       (     unittest.TestCase    ):


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



									"""simple docstring"""



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


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



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      self.get_config()

									return config, pixel_values


							def      _snake_case  (							self    :Optional[int]		)   ->   Tuple:



									"""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    :str							,				__A    :int							,				__A    :str		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      FlaxRegNetModel(config=__A		)
									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Optional[Any]							,				__A    :Dict							,				__A    :Any		)   ->   Optional[int]:



									"""simple docstring"""



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


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



									"""simple docstring"""



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




@require_flax
class   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

							lowerCamelCase_           =					False
							lowerCamelCase_           =					False
							lowerCamelCase_           =					False


							def      _snake_case  (							self    :int		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      FlaxRegNetModelTester(self		)
									SCREAMING_SNAKE_CASE__			=      ConfigTester(self							,				config_class=__A							,				has_text_modality=__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :int		)   ->   Optional[Any]:



									"""simple docstring"""



									pass


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



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()

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

											SCREAMING_SNAKE_CASE__			=      ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,				__A		)


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



									"""simple docstring"""



									def check_hidden_states_output(__A    :Optional[int]							,				__A    :str							,				__A    :Optional[Any]		):
											SCREAMING_SNAKE_CASE__			=      model_class(__A		)

											SCREAMING_SNAKE_CASE__			=      model(**self._prepare_for_class(__A							,				__A		)		)

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

											SCREAMING_SNAKE_CASE__			=      self.model_tester.num_stages
											self.assertEqual(len(__A		)							,				expected_num_stages + 1		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE__			=      True
											check_hidden_states_output(__A							,				__A							,				__A		)

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

											check_hidden_states_output(__A							,				__A							,				__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													SCREAMING_SNAKE_CASE__			=      self._prepare_for_class(__A							,				__A		)
													SCREAMING_SNAKE_CASE__			=      model_class(__A		)

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

													with self.subTest("""JIT Enabled"""		):
															SCREAMING_SNAKE_CASE__			=      model_jitted(**__A		).to_tuple()

													with self.subTest("""JIT Disabled"""		):
															with jax.disable_jit():
																	SCREAMING_SNAKE_CASE__			=      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 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
		return image




@require_flax
class   UpperCamelCase_       (     unittest.TestCase    ):


							@cached_property
							def      _snake_case  (							self    :List[Any]		)   ->   Optional[Any]:



									"""simple docstring"""



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


							@slow
							def      _snake_case  (							self    :Any		)   ->   Optional[Any]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      self.default_image_processor
									SCREAMING_SNAKE_CASE__			=      prepare_img()
									SCREAMING_SNAKE_CASE__			=      image_processor(images=__A							,				return_tensors="""np"""		)

									SCREAMING_SNAKE_CASE__			=      model(**__A		)

									# verify the logits
									SCREAMING_SNAKE_CASE__			=      (1, 1000)
									self.assertEqual(outputs.logits.shape							,				__A		)

									SCREAMING_SNAKE_CASE__			=      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]							,				__A							,				atol=1E-4		)		)











from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
		SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
		return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError("""Input Error: Molar mass values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)