
import inspect
import unittest

from transformers import ResNetConfig, 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.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class 	A__				(	unittest.TestCase      ):






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






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						parent
							lowerCAmelCase__ : List[str]   =						batch_size
							lowerCAmelCase__ : Dict   =						image_size
							lowerCAmelCase__ : Any   =						num_channels
							lowerCAmelCase__ : Union[str, Any]   =						embeddings_size
							lowerCAmelCase__ : Tuple   =						hidden_sizes
							lowerCAmelCase__ : Optional[Any]   =						depths
							lowerCAmelCase__ : Optional[int]   =						is_training
							lowerCAmelCase__ : List[Any]   =						use_labels
							lowerCAmelCase__ : Union[str, Any]   =						hidden_act
							lowerCAmelCase__ : Any   =						num_labels
							lowerCAmelCase__ : Optional[int]   =						scope
							lowerCAmelCase__ : Dict   =						len(a  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

							lowerCAmelCase__ : Any   =						self.get_config()

							return config, pixel_values






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							return ResNetConfig(
							    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 				_lowerCamelCase						(		self   : int       ,	a   : Dict       ,	a   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						FlaxResNetModel(config=a  )
							lowerCAmelCase__ : Tuple   =						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 				_lowerCamelCase						(		self   : str       ,	a   : List[str]       ,	a   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						self.num_labels
							lowerCAmelCase__ : Optional[Any]   =						FlaxResNetForImageClassification(config=a  )
							lowerCAmelCase__ : Tuple   =						model(a  )
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						self.prepare_config_and_inputs()
							lowerCAmelCase__ ,							lowerCAmelCase__ : List[Any]   =						config_and_inputs
							lowerCAmelCase__ : List[str]   =						{'pixel_values': pixel_values}
							return config, inputs_dict







@require_flax
class 	A__				(	lowercase__		,  unittest.TestCase      ):
				lowercase            =    (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

				lowercase            =    False
				lowercase            =    False
				lowercase            =    False






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						FlaxResNetModelTester(self  )
							lowerCAmelCase__ : int   =						ConfigTester(self       ,	config_class=a       ,	has_text_modality=a  )






				def 				_lowerCamelCase						(		self   : 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 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							return






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*a  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*a  )






				@unittest.skip(reason='ResNet does not use inputs_embeds'  )
				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							pass






				@unittest.skip(reason='ResNet does not support input and output embeddings'  )
				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							pass






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







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

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

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






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							def check_hidden_states_output(a   : Optional[int]       ,	a   : int       ,	a   : str  ):
										lowerCAmelCase__ : Union[str, Any]   =						model_class(a  )

										lowerCAmelCase__ : List[str]   =						model(**self._prepare_for_class(a       ,	a  )  )

										lowerCAmelCase__ : Union[str, Any]   =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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






				@unittest.skip(reason='ResNet does not use feedforward chunking'  )
				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							pass






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ ,							lowerCAmelCase__ : Any   =						self.model_tester.prepare_config_and_inputs_for_common()

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

													@jax.jit
													def model_jitted(a   : List[str]       ,	**a   : Dict  ):
																return model(pixel_values=a       ,	**a  )

													with self.subTest('JIT Enabled'  ):
																lowerCAmelCase__ : List[str]   =						model_jitted(**a  ).to_tuple()

													with self.subTest('JIT Disabled'  ):
																with jax.disable_jit():
																			lowerCAmelCase__ : Dict   =						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 					lowerCAmelCase__     (    )							->						Any:
			lowerCAmelCase__ : Union[str, Any]   =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
			return image







@require_flax
class 	A__				(	unittest.TestCase      ):






				@cached_property
				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							return AutoImageProcessor.from_pretrained('microsoft/resnet-50'  ) if is_vision_available() else None






				@slow
				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50'  )

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

							lowerCAmelCase__ : List[Any]   =						model(**a  )

							# verify the logits
							lowerCAmelCase__ : Any   =						(1, 1_000)
							self.assertEqual(outputs.logits.shape       ,	a  )

							lowerCAmelCase__ : Tuple   =						jnp.array([-11.1_069, -9.7_8_7_7, -8.3_7_7_7]  )

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





from itertools import count
from math import asin, pi, sqrt
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return (
        (1 - 2 * point) * sqrt(point - point**2		) + 2 * point + asin(sqrt(1 - point		)		)
    ) / 4
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(circles_number + 1 - sqrt(2 * circles_number		)) / (
        2 * (circles_number**2 + 1)
    )
    a__  			=							circles_number * intersection_y

    a__  			=							intersection_x * intersection_y / 2
    a__  			=							circle_bottom_arc_integral(
        1 / 2		) - circle_bottom_arc_integral(UpperCAmelCase_		)

    return triangle_area + concave_region_area
def 							__lowerCAmelCase			(UpperCAmelCase_ = 1 / 1_000		):
    a__  			=							(1 - pi / 4) / 4

    for n in count(1		):
        if concave_triangle_area(UpperCAmelCase_		) / l_section_area < fraction:
            return n

    return -1


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






