


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
  from PIL import Image

  from transformers import ConvNextImageProcessor





class       _a							:




 def __init__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	List[str]=13	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=32	,      _SCREAMING_SNAKE_CASE		:	Any=3	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=4	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=[10, 20, 30, 40]	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=[2, 2, 3, 2]	,      _SCREAMING_SNAKE_CASE		:	Tuple=True	,      _SCREAMING_SNAKE_CASE		:	Tuple=True	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=37	,      _SCREAMING_SNAKE_CASE		:	List[str]="gelu"	,      _SCREAMING_SNAKE_CASE		:	Any=10	,      _SCREAMING_SNAKE_CASE		:	List[str]=0.02	,      _SCREAMING_SNAKE_CASE		:	Tuple=3	,      _SCREAMING_SNAKE_CASE		:	str=None	,      )-> Dict:
   lowerCAmelCase__     :    List[Any]					  =      parent
   lowerCAmelCase__     :    int					  =      batch_size
   lowerCAmelCase__     :    Tuple					  =      image_size
   lowerCAmelCase__     :    Optional[int]					  =      num_channels
   lowerCAmelCase__     :    Dict					  =      num_stages
   lowerCAmelCase__     :    str					  =      hidden_sizes
   lowerCAmelCase__     :    Any					  =      depths
   lowerCAmelCase__     :    List[Any]					  =      is_training
   lowerCAmelCase__     :    Any					  =      use_labels
   lowerCAmelCase__     :    Optional[Any]					  =      intermediate_size
   lowerCAmelCase__     :    Union[str, Any]					  =      hidden_act
   lowerCAmelCase__     :    Any					  =      type_sequence_label_size
   lowerCAmelCase__     :    List[Any]					  =      initializer_range
   lowerCAmelCase__     :    str					  =      scope




 def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> List[str]:
   lowerCAmelCase__     :    Dict					  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   lowerCAmelCase__     :    List[str]					  =      None
   if self.use_labels:
     lowerCAmelCase__     :    Optional[Any]					  =      ids_tensor([self.batch_size]	,      self.type_sequence_label_size	)

   lowerCAmelCase__     :    Dict					  =      self.get_config()

   return config, pixel_values, labels




 def 							UpperCAmelCase__(      self		:	Optional[int]	)-> Any:
   return ConvNextConfig(
       num_channels=self.num_channels	,      hidden_sizes=self.hidden_sizes	,      depths=self.depths	,      num_stages=self.num_stages	,      hidden_act=self.hidden_act	,      is_decoder=UpperCAmelCase_	,      initializer_range=self.initializer_range	,      )




 def 							UpperCAmelCase__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	)-> List[Any]:
   lowerCAmelCase__     :    Tuple					  =      TFConvNextModel(config=UpperCAmelCase_	)
   lowerCAmelCase__     :    str					  =      model(UpperCAmelCase_	,      training=UpperCAmelCase_	)
   # expected last hidden states: B, C, H // 32, W // 32
   self.parent.assertEqual(
       result.last_hidden_state.shape	,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,      )




 def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Dict	)-> List[Any]:
   lowerCAmelCase__     :    List[str]					  =      self.type_sequence_label_size
   lowerCAmelCase__     :    Dict					  =      TFConvNextForImageClassification(UpperCAmelCase_	)
   lowerCAmelCase__     :    Dict					  =      model(UpperCAmelCase_	,      labels=UpperCAmelCase_	,      training=UpperCAmelCase_	)
   self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)	)




 def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Tuple:
   lowerCAmelCase__     :    Optional[Any]					  =      self.prepare_config_and_inputs()
   lowerCAmelCase__     :    str					  =      config_and_inputs
   lowerCAmelCase__     :    int					  =      {'pixel_values': pixel_values}
   return config, inputs_dict





@require_tf
class       _a							(       _lowercase  ,		_lowercase  ,		unittest.TestCase):
 _a						:						List[str]   					=							(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
 _a						:						Optional[Any]   					=							(
     {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
     if is_tf_available()
     else {}
 )

 _a						:						Optional[int]   					=							False
 _a						:						List[Any]   					=							False
 _a						:						Any   					=							False
 _a						:						str   					=							False
 _a						:						str   					=							False




 def 							UpperCAmelCase__(      self		:	Dict	)-> Any:
   lowerCAmelCase__     :    str					  =      TFConvNextModelTester(self	)
   lowerCAmelCase__     :    List[Any]					  =      ConfigTester(
       self	,      config_class=UpperCAmelCase_	,      has_text_modality=UpperCAmelCase_	,      hidden_size=37	,      )




 @unittest.skip(reason='''ConvNext does not use inputs_embeds'''	)
 def 							UpperCAmelCase__(      self		:	List[str]	)-> Dict:
   pass




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''	)	) == 0	,      reason='''TF does not support backprop for grouped convolutions on CPU.'''	,      )
 @slow
 def 							UpperCAmelCase__(      self		:	Optional[Any]	)-> Any:
   super().test_keras_fit()




 @unittest.skip(reason='''ConvNext does not support input and output embeddings'''	)
 def 							UpperCAmelCase__(      self		:	List[str]	)-> Union[str, Any]:
   pass




 def 							UpperCAmelCase__(      self		:	str	)-> str:
   lowerCAmelCase__     :    List[str]					  =      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCAmelCase__     :    Tuple					  =      model_class(UpperCAmelCase_	)
     lowerCAmelCase__     :    Tuple					  =      inspect.signature(model.call	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCAmelCase__     :    Optional[int]					  =      [*signature.parameters.keys()]

     lowerCAmelCase__     :    Dict					  =      ['pixel_values']
     self.assertListEqual(arg_names[:1]	,      UpperCAmelCase_	)




 def 							UpperCAmelCase__(      self		:	Any	)-> str:
   lowerCAmelCase__     :    Dict					  =      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_	)




 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''	)	) == 0	,      reason='''TF does not support backprop for grouped convolutions on CPU.'''	,      )
 def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
   super().test_dataset_conversion()




 def 							UpperCAmelCase__(      self		:	int	)-> Tuple:
   def check_hidden_states_output(_SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[str]	):
     lowerCAmelCase__     :    Optional[int]					  =      model_class(UpperCAmelCase_	)

     lowerCAmelCase__     :    Dict					  =      model(**self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	)	)
     lowerCAmelCase__     :    List[Any]					  =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     lowerCAmelCase__     :    List[str]					  =      self.model_tester.num_stages
     self.assertEqual(len(UpperCAmelCase_	)	,      expected_num_stages + 1	)

     # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	)	,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,      )

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

   for model_class in self.all_model_classes:
     lowerCAmelCase__     :    List[str]					  =      True
     check_hidden_states_output(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowerCAmelCase__     :    List[str]					  =      True

     check_hidden_states_output(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	)




 def 							UpperCAmelCase__(      self		:	List[str]	)-> Tuple:
   lowerCAmelCase__     :    List[Any]					  =      self.model_tester.prepare_config_and_inputs_for_common()

   def check_equivalence(_SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]={}	):
     lowerCAmelCase__     :    Union[str, Any]					  =      model(UpperCAmelCase_	,      return_dict=UpperCAmelCase_	,      **UpperCAmelCase_	)
     lowerCAmelCase__     :    List[str]					  =      model(UpperCAmelCase_	,      return_dict=UpperCAmelCase_	,      **UpperCAmelCase_	).to_tuple()

     def recursive_check(_SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Tuple	):
       if isinstance(UpperCAmelCase_	,      (List, Tuple)	):
         for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_	,      UpperCAmelCase_	):
           recursive_check(UpperCAmelCase_	,      UpperCAmelCase_	)
       elif tuple_object is None:
         return
       else:
         self.assertTrue(
             all(tf.equal(UpperCAmelCase_	,      UpperCAmelCase_	)	)	,      msg=(
                 '''Tuple and dict output are not equal. Difference:'''
                 F' {tf.math.reduce_max(tf.abs(tuple_object - dict_object	)	)}'
             )	,      )

       recursive_check(UpperCAmelCase_	,      UpperCAmelCase_	)

   for model_class in self.all_model_classes:
     lowerCAmelCase__     :    Union[str, Any]					  =      model_class(UpperCAmelCase_	)

     lowerCAmelCase__     :    Tuple					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	)
     lowerCAmelCase__     :    Union[str, Any]					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	)
     check_equivalence(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	)

     lowerCAmelCase__     :    Tuple					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	,      return_labels=UpperCAmelCase_	)
     lowerCAmelCase__     :    Tuple					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	,      return_labels=UpperCAmelCase_	)
     check_equivalence(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	)

     lowerCAmelCase__     :    Union[str, Any]					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	)
     lowerCAmelCase__     :    Optional[Any]					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	)
     check_equivalence(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      {'''output_hidden_states''': True}	)

     lowerCAmelCase__     :    Union[str, Any]					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	,      return_labels=UpperCAmelCase_	)
     lowerCAmelCase__     :    int					  =      self._prepare_for_class(UpperCAmelCase_	,      UpperCAmelCase_	,      return_labels=UpperCAmelCase_	)
     check_equivalence(UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      {'''output_hidden_states''': True}	)




 def 							UpperCAmelCase__(      self		:	int	)-> int:
   lowerCAmelCase__     :    Any					  =      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_	)




 @slow
 def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Union[str, Any]:
   lowerCAmelCase__     :    Dict					  =      TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''	)
   self.assertIsNotNone(UpperCAmelCase_	)






def   lowerCamelCase_					( ):




  """simple docstring"""






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





@require_tf
@require_vision
class       _a							(       unittest.TestCase):




 @cached_property
 def 							UpperCAmelCase__(      self		:	Optional[int]	)-> Optional[Any]:
   return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''	) if is_vision_available() else None




 @slow
 def 							UpperCAmelCase__(      self		:	int	)-> Any:
   lowerCAmelCase__     :    Tuple					  =      TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''	)

   lowerCAmelCase__     :    Optional[Any]					  =      self.default_image_processor
   lowerCAmelCase__     :    str					  =      prepare_img()
   lowerCAmelCase__     :    Dict					  =      image_processor(images=UpperCAmelCase_	,      return_tensors='''tf'''	)

   # forward pass
   lowerCAmelCase__     :    List[Any]					  =      model(**UpperCAmelCase_	)

   # verify the logits
   lowerCAmelCase__     :    List[Any]					  =      tf.TensorShape((1, 1000)	)
   self.assertEqual(outputs.logits.shape	,      UpperCAmelCase_	)

   lowerCAmelCase__     :    Any					  =      tf.constant([-0.0260, -0.4739, 0.1911]	)

   tf.debugging.assert_near(outputs.logits[0, :3]	,      UpperCAmelCase_	,      atol=1E-4	)








from itertools import count
from math import asin, pi, sqrt






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		return (
		    (1 - 2 * point) * sqrt(point - point**2 ) + 2 * point + asin(sqrt(1 - point ) )
		) / 4






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Union[str, Any]					  =      (circles_number + 1 - sqrt(2 * circles_number )) / (
		    2 * (circles_number**2 + 1)
		)
		lowerCAmelCase__     :    Tuple					  =      circles_number * intersection_y

		lowerCAmelCase__     :    Any					  =      intersection_x * intersection_y / 2
		lowerCAmelCase__     :    Optional[Any]					  =      circle_bottom_arc_integral(
		    1 / 2 ) - circle_bottom_arc_integral(_a )

		return triangle_area + concave_region_area






def   lowerCamelCase_					( _a = 1 / 1_000 ):




		"""simple docstring"""






		lowerCAmelCase__     :    Any					  =      (1 - pi / 4) / 4

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

		return -1


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




