


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 		_snake_case  (				unittest.TestCase							):




	def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=3 ,  _lowerCamelCase=32 ,  _lowerCamelCase=3 ,  _lowerCamelCase=10 ,  _lowerCamelCase=[10, 20, 30, 40] ,  _lowerCamelCase=[1, 1, 2, 1] ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase="relu" ,  _lowerCamelCase=3 ,  _lowerCamelCase=None ,  ):
							a						:Optional[Any]										=	parent
							a						:Dict										=	batch_size
							a						:List[Any]										=	image_size
							a						:Union[str, Any]										=	num_channels
							a						:List[Any]										=	embeddings_size
							a						:List[Any]										=	hidden_sizes
							a						:int										=	depths
							a						:Any										=	is_training
							a						:Union[str, Any]										=	use_labels
							a						:int										=	hidden_act
							a						:List[Any]										=	num_labels
							a						:List[str]										=	scope
							a						:List[Any]										=	len(_snake_case   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:List[Any]										=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							a						:Tuple										=	self.get_config()

							return config, pixel_values




	def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
							a						:Tuple										=	FlaxRegNetModel(config=_snake_case   )
							a						:Union[str, Any]										=	model(_snake_case   )

							# 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 ,  _lowerCamelCase ,  _lowerCamelCase   ):
							a						:Optional[Any]										=	self.num_labels
							a						:str										=	FlaxRegNetForImageClassification(config=_snake_case   )
							a						:Union[str, Any]										=	model(_snake_case   )
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Any										=	self.prepare_config_and_inputs()
							a							,    a						:str										=	config_and_inputs
							a						:Union[str, Any]										=	{'''pixel_values''': pixel_values}
							return config, inputs_dict







@require_flax
class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
	SCREAMING_SNAKE_CASE__				=	(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	SCREAMING_SNAKE_CASE__				=	False
	SCREAMING_SNAKE_CASE__				=	False
	SCREAMING_SNAKE_CASE__				=	False




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Optional[int]										=	FlaxRegNetModelTester(self   )
							a						:Tuple										=	ConfigTester(self ,  config_class=_snake_case ,  has_text_modality=_snake_case   )




	def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self   ):
							return




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:List[Any]										=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_snake_case   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Any										=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_snake_case   )




	@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							pass




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




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a							,    a						:List[str]										=	self.model_tester.prepare_config_and_inputs_for_common()

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

													a						:int										=	['''pixel_values''']
													self.assertListEqual(arg_names[:1] ,  _snake_case   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):


							def check_hidden_states_output(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
													a						:Tuple										=	model_class(_snake_case   )

													a						:Union[str, Any]										=	model(**self._prepare_for_class(_snake_case ,  _snake_case   )   )

													a						:int										=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													a						:int										=	self.model_tester.num_stages
													self.assertEqual(len(_snake_case   ) ,  expected_num_stages + 1   )

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

							for model_class in self.all_model_classes:
													a						:Tuple										=	True
													check_hidden_states_output(_snake_case ,  _snake_case ,  _snake_case   )

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

													check_hidden_states_output(_snake_case ,  _snake_case ,  _snake_case   )




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a							,    a						:str										=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																			a						:Optional[int]										=	self._prepare_for_class(_snake_case ,  _snake_case   )
																			a						:str										=	model_class(_snake_case   )

																			@jax.jit
																			def model_jitted(_lowerCamelCase ,  **_lowerCamelCase   ):
																									return model(pixel_values=_snake_case ,  **_snake_case   )

																			with self.subTest('''JIT Enabled'''   ):
																									a						:Union[str, Any]										=	model_jitted(**_snake_case   ).to_tuple()

																			with self.subTest('''JIT Disabled'''   ):
																									with jax.disable_jit():
																															a						:Tuple										=	model_jitted(**_snake_case   ).to_tuple()

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







def     __lowerCamelCase						(			):




						"""simple docstring"""





						a						:Tuple										=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
						return image







@require_flax
class 		_snake_case  (				unittest.TestCase							):




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




	@slow
	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:List[str]										=	FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''   )

							a						:str										=	self.default_image_processor
							a						:int										=	prepare_img()
							a						:str										=	image_processor(images=_snake_case ,  return_tensors='''np'''   )

							a						:Optional[Any]										=	model(**_snake_case   )

							# verify the logits
							a						:Any										=	(1, 1000)
							self.assertEqual(outputs.logits.shape ,  _snake_case   )

							a						:List[Any]										=	jnp.array([-0.4180, -1.5051, -3.4836]   )

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









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





