




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 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     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    ,					)      ->      Tuple:






         """simple docstring"""
         a__  			=							parent
         a__  			=							batch_size
         a__  			=							image_size
         a__  			=							num_channels
         a__  			=							embeddings_size
         a__  			=							hidden_sizes
         a__  			=							depths
         a__  			=							is_training
         a__  			=							use_labels
         a__  			=							hidden_act
         a__  			=							num_labels
         a__  			=							scope
         a__  			=							len(A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      int:






         """simple docstring"""
         a__  			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

         a__  			=							self.get_config()

         return config, pixel_values

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Dict    ,					A			:							Optional[int]     )      ->      Any:






         """simple docstring"""
         a__  			=							FlaxResNetModel(config=A     )
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							str    ,					A			:							List[str]    ,					A			:							str     )      ->      List[str]:






         """simple docstring"""
         a__  			=							self.num_labels
         a__  			=							FlaxResNetForImageClassification(config=A     )
         a__  			=							model(A     )
         self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)     )






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






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







@require_flax
class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			List[str]							=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

     __UpperCAmelCase						:			Optional[int]							=     False
     __UpperCAmelCase						:			List[Any]							=     False
     __UpperCAmelCase						:			Any							=     False

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      None:






         """simple docstring"""
         a__  			=							FlaxResNetModelTester(self     )
         a__  			=							ConfigTester(self    ,					config_class=A    ,					has_text_modality=A     )

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






         """simple docstring"""
         return

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A     )

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






         """simple docstring"""
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      int:






         """simple docstring"""
         pass

     @unittest.skip(reason="""ResNet does not support input and output embeddings"""     )
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Dict:






         """simple docstring"""
         pass

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      List[Any]:






         """simple docstring"""
         a__			,		a__  			=							self.model_tester.prepare_config_and_inputs_for_common()

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

             a__  			=							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]    ,					A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[Any]:






         """simple docstring"""
         def check_hidden_states_output(A			:							Optional[int]    ,					A			:							int    ,					A			:							str     ):
             a__  			=							model_class(A     )

             a__  			=							model(**self._prepare_for_class(A    ,					A     )     )

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

             a__  			=							self.model_tester.num_stages
             self.assertEqual(len(A     )    ,					expected_num_stages + 1     )

     @unittest.skip(reason="""ResNet does not use feedforward chunking"""     )
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Tuple:






         """simple docstring"""
         pass






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






         """simple docstring"""
         a__			,		a__  			=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                 a__  			=							self._prepare_for_class(A    ,					A     )
                 a__  			=							model_class(A     )

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

                 with self.subTest("""JIT Enabled"""     ):
                     a__  			=							model_jitted(**A     ).to_tuple()

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







@require_flax
class 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     @cached_property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""     ) if is_vision_available() else None






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






         """simple docstring"""
         a__  			=							FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""     )

         a__  			=							self.default_image_processor
         a__  			=							prepare_img()
         a__  			=							image_processor(images=A    ,					return_tensors="""np"""     )

         a__  			=							model(**A     )

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

         a__  			=							jnp.array([-1_1.1_0_6_9, -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() = }")






