
"""simple docstring"""







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=13       ,		_SCREAMING_SNAKE_CASE=30       ,		_SCREAMING_SNAKE_CASE=2       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=2       ,		_SCREAMING_SNAKE_CASE=4       ,		_SCREAMING_SNAKE_CASE=37       ,		_SCREAMING_SNAKE_CASE="gelu"       ,		_SCREAMING_SNAKE_CASE=0.1       ,		_SCREAMING_SNAKE_CASE=0.1       ,		_SCREAMING_SNAKE_CASE=10       ,		_SCREAMING_SNAKE_CASE=0.02       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=0.6       ,		_SCREAMING_SNAKE_CASE=None       ,		):
			__lowerCAmelCase			:						Any									=      parent
			__lowerCAmelCase			:						Dict									=      batch_size
			__lowerCAmelCase			:						Union[str, Any]									=      image_size
			__lowerCAmelCase			:						str									=      patch_size
			__lowerCAmelCase			:						Tuple									=      num_channels
			__lowerCAmelCase			:						List[str]									=      is_training
			__lowerCAmelCase			:						Union[str, Any]									=      use_labels
			__lowerCAmelCase			:						Optional[Any]									=      hidden_size
			__lowerCAmelCase			:						Union[str, Any]									=      num_hidden_layers
			__lowerCAmelCase			:						str									=      num_attention_heads
			__lowerCAmelCase			:						Tuple									=      intermediate_size
			__lowerCAmelCase			:						Optional[Any]									=      hidden_act
			__lowerCAmelCase			:						Dict									=      hidden_dropout_prob
			__lowerCAmelCase			:						int									=      attention_probs_dropout_prob
			__lowerCAmelCase			:						List[str]									=      type_sequence_label_size
			__lowerCAmelCase			:						Union[str, Any]									=      initializer_range
			__lowerCAmelCase			:						Union[str, Any]									=      mask_ratio
			__lowerCAmelCase			:						List[str]									=      scope

			# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
			# (we add 1 for the [CLS] token)
			__lowerCAmelCase			:						Any									=      (image_size // patch_size) ** 2
			__lowerCAmelCase			:						Optional[Any]									=      int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

			__lowerCAmelCase			:						Optional[Any]									=      self.get_config()

			return config, pixel_values, labels

		def 							__lowerCamelCase							(      self ):
			return ViTMAEConfig(
			    image_size=self.image_size       ,		patch_size=self.patch_size       ,		num_channels=self.num_channels       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		intermediate_size=self.intermediate_size       ,		decoder_hidden_size=self.hidden_size       ,		decoder_num_hidden_layers=self.num_hidden_layers       ,		decoder_num_attention_heads=self.num_attention_heads       ,		decoder_intermediate_size=self.intermediate_size       ,		hidden_act=self.hidden_act       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		is_decoder=_SCREAMING_SNAKE_CASE       ,		initializer_range=self.initializer_range       ,		mask_ratio=self.mask_ratio       ,		)

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      TFViTMAEModel(config=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      model(_SCREAMING_SNAKE_CASE       ,		training=_SCREAMING_SNAKE_CASE )
			self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.seq_length, self.hidden_size) )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						str									=      TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Union[str, Any]									=      model(_SCREAMING_SNAKE_CASE       ,		training=_SCREAMING_SNAKE_CASE )
			# expected sequence length = num_patches
			__lowerCAmelCase			:						List[Any]									=      (self.image_size // self.patch_size) ** 2
			__lowerCAmelCase			:						Dict									=      self.patch_size**2 * self.num_channels
			self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, num_patches, expected_num_channels) )

			# test greyscale images
			__lowerCAmelCase			:						Optional[int]									=      1
			__lowerCAmelCase			:						Optional[Any]									=      TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
			__lowerCAmelCase			:						Any									=      model(_SCREAMING_SNAKE_CASE       ,		training=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Dict									=      self.patch_size**2
			self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, num_patches, expected_num_channels) )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      self.prepare_config_and_inputs()
			((__lowerCAmelCase)			,   (__lowerCAmelCase)			,   (__lowerCAmelCase))			:						Union[str, Any]									=      config_and_inputs
			__lowerCAmelCase			:						Optional[int]									=      {'pixel_values': pixel_values}
			return config, inputs_dict




@require_tf
class  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     unittest.TestCase):
		A_						:       Any				   =	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		A_						:       Union[str, Any]				   =	{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

		A_						:       Any				   =	False
		A_						:       List[Any]				   =	False
		A_						:       List[str]				   =	False
		A_						:       int				   =	False

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      TFViTMAEModelTester(self )
			__lowerCAmelCase			:						Union[str, Any]									=      ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		has_text_modality=_SCREAMING_SNAKE_CASE       ,		hidden_size=37 )

		def 							__lowerCamelCase							(      self ):
			self.config_tester.run_common_tests()

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						Dict									=      model_class(_SCREAMING_SNAKE_CASE )
				self.assertIsInstance(model.get_input_embeddings()       ,		(tf.keras.layers.Layer) )
				__lowerCAmelCase			:						int									=      model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,		tf.keras.layers.Layer ) )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase			:						Tuple									=      ['pixel_values']
				self.assertListEqual(arg_names[:1]       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			# make the mask reproducible
			np.random.seed(2 )

			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase			:						Union[str, Any]									=      int((config.image_size // config.patch_size) ** 2 )
			__lowerCAmelCase			:						Optional[int]									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						List[Any]									=      model_class(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Tuple									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						Dict									=      model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						int									=      copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ) )
				__lowerCAmelCase			:						Optional[int]									=      model(**_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Any									=      outputs_dict[0].numpy()
				__lowerCAmelCase			:						Optional[Any]									=      outputs_keywords[0].numpy()

				self.assertLess(np.sum(np.abs(output_dict - output_keywords ) )       ,		1E-6 )

		def 							__lowerCamelCase							(      self ):
			# make the mask reproducible
			np.random.seed(2 )

			__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase			:						Union[str, Any]									=      int((config.image_size // config.patch_size) ** 2 )
			__lowerCAmelCase			:						Dict									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

			def prepare_numpy_arrays(_SCREAMING_SNAKE_CASE ):
				__lowerCAmelCase			:						Union[str, Any]									=      {}
				for k, v in inputs_dict.items():
					if tf.is_tensor(_SCREAMING_SNAKE_CASE ):
						__lowerCAmelCase			:						List[Any]									=      v.numpy()
					else:
						__lowerCAmelCase			:						Tuple									=      np.array(_SCREAMING_SNAKE_CASE )

				return inputs_np_dict

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						List[Any]									=      model_class(_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						Optional[int]									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Optional[Any]									=      prepare_numpy_arrays(_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						Tuple									=      model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Union[str, Any]									=      model(**_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )
				self.assert_outputs_same(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			# make masks reproducible
			np.random.seed(2 )

			__lowerCAmelCase			:						Optional[int]									=      int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
			__lowerCAmelCase			:						Any									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
			__lowerCAmelCase			:						Tuple									=      tf.constant(_SCREAMING_SNAKE_CASE )

			# Add `noise` argument.
			# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
			__lowerCAmelCase			:						str									=      tf_noise

			super().check_pt_tf_models(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			# make mask reproducible
			np.random.seed(2 )

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

			__lowerCAmelCase			:						int									=      {
			    module_member
			    for model_class in self.all_model_classes
			    for module in (import_module(model_class.__module__ ),)
			    for module_member_name in dir(_SCREAMING_SNAKE_CASE )
			    if module_member_name.endswith('MainLayer' )
			    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
			    and module_member_name[: -len('MainLayer' )] == model_class.__name__[: -len('Model' )]
			    for module_member in (getattr(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ),)
			    if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			    and tf.keras.layers.Layer in module_member.__bases__
			    and getattr(_SCREAMING_SNAKE_CASE       ,		'_keras_serializable'       ,		_SCREAMING_SNAKE_CASE )
			}

			__lowerCAmelCase			:						Any									=      int((config.image_size // config.patch_size) ** 2 )
			__lowerCAmelCase			:						str									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
			__lowerCAmelCase			:						List[str]									=      tf.convert_to_tensor(_SCREAMING_SNAKE_CASE )
			inputs_dict.update({'noise': noise} )

			for main_layer_class in tf_main_layer_classes:
				__lowerCAmelCase			:						Dict									=      main_layer_class(_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						Union[str, Any]									=      {
				    name: tf.keras.Input(tensor.shape[1:]       ,		dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
				}

				__lowerCAmelCase			:						Optional[int]									=      tf.keras.Model(_SCREAMING_SNAKE_CASE       ,		outputs=main_layer(_SCREAMING_SNAKE_CASE ) )
				__lowerCAmelCase			:						Union[str, Any]									=      model(_SCREAMING_SNAKE_CASE )

				with tempfile.TemporaryDirectory() as tmpdirname:
					__lowerCAmelCase			:						Tuple									=      os.path.join(_SCREAMING_SNAKE_CASE       ,		'keras_model.h5' )
					model.save(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						int									=      tf.keras.models.load_model(
					    _SCREAMING_SNAKE_CASE       ,		custom_objects={main_layer_class.__name__: main_layer_class} )
					assert isinstance(_SCREAMING_SNAKE_CASE       ,		tf.keras.Model )
					__lowerCAmelCase			:						Optional[Any]									=      model(_SCREAMING_SNAKE_CASE )
					self.assert_outputs_same(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		@slow
		def 							__lowerCamelCase							(      self ):
			# make mask reproducible
			np.random.seed(2 )

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

			__lowerCAmelCase			:						List[str]									=      int((config.image_size // config.patch_size) ** 2 )
			__lowerCAmelCase			:						Optional[Any]									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						List[Any]									=      model_class(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						str									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						int									=      model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )

				if model_class.__name__ == "TFViTMAEModel":
					__lowerCAmelCase			:						Union[str, Any]									=      outputs.last_hidden_state.numpy()
					__lowerCAmelCase			:						int									=      0
				else:
					__lowerCAmelCase			:						List[Any]									=      outputs.logits.numpy()
					__lowerCAmelCase			:						Optional[Any]									=      0

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(_SCREAMING_SNAKE_CASE       ,		saved_model=_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						Any									=      model_class.from_pretrained(_SCREAMING_SNAKE_CASE )
					__lowerCAmelCase			:						List[Any]									=      model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )

					if model_class.__name__ == "TFViTMAEModel":
						__lowerCAmelCase			:						Optional[int]									=      after_outputs['last_hidden_state'].numpy()
						__lowerCAmelCase			:						Dict									=      0
					else:
						__lowerCAmelCase			:						Tuple									=      after_outputs['logits'].numpy()
						__lowerCAmelCase			:						int									=      0

					__lowerCAmelCase			:						Dict									=      np.amax(np.abs(out_a - out_a ) )
					self.assertLessEqual(_SCREAMING_SNAKE_CASE       ,		1E-5 )

		def 							__lowerCamelCase							(      self ):
			# make mask reproducible
			np.random.seed(2 )

			__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase			:						Tuple									=      int((config.image_size // config.patch_size) ** 2 )
			__lowerCAmelCase			:						Optional[int]									=      np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						List[str]									=      model_class(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						List[Any]									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

				__lowerCAmelCase			:						Any									=      model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						Dict									=      model.get_config()
				# make sure that returned config is jsonifiable, which is required by keras
				json.dumps(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						List[Any]									=      model_class.from_config(model.get_config() )
				# make sure it also accepts a normal config
				__lowerCAmelCase			:						List[str]									=      model_class.from_config(model.config )
				__lowerCAmelCase			:						Any									=      new_model(_SCREAMING_SNAKE_CASE )  # Build model
				new_model.set_weights(model.get_weights() )
				__lowerCAmelCase			:						Any									=      new_model(_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )

				self.assert_outputs_same(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		@unittest.skip(
		    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.' )
		def 							__lowerCamelCase							(      self ):
			pass

		@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
		def 							__lowerCamelCase							(      self ):
			pass







		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      TFViTMAEModel.from_pretrained('google/vit-base-patch16-224' )
			self.assertIsNotNone(_SCREAMING_SNAKE_CASE )



def     __lowerCAmelCase				():
	__lowerCAmelCase			:						Optional[int]									=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
	return image




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

		@cached_property
		def 							__lowerCamelCase							(      self ):
			return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None







		@slow
		def 							__lowerCamelCase							(      self ):
			# make random mask reproducible across the PT and TF model
			np.random.seed(2 )

			__lowerCAmelCase			:						Optional[Any]									=      TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' )

			__lowerCAmelCase			:						Tuple									=      self.default_image_processor
			__lowerCAmelCase			:						Dict									=      prepare_img()
			__lowerCAmelCase			:						Union[str, Any]									=      image_processor(images=_SCREAMING_SNAKE_CASE       ,		return_tensors='tf' )

			# prepare a noise vector that will be also used for testing the TF model
			# (this way we can ensure that the PT and TF models operate on the same inputs)
			__lowerCAmelCase			:						List[str]									=      ViTMAEConfig()
			__lowerCAmelCase			:						Optional[Any]									=      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
			__lowerCAmelCase			:						Dict									=      np.random.uniform(size=(1, num_patches) )

			# forward pass
			__lowerCAmelCase			:						Union[str, Any]									=      model(**_SCREAMING_SNAKE_CASE       ,		noise=_SCREAMING_SNAKE_CASE )

			# verify the logits
			__lowerCAmelCase			:						Any									=      tf.convert_to_tensor([1, 1_96, 7_68] )
			self.assertEqual(outputs.logits.shape       ,		_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[Any]									=      tf.convert_to_tensor(
			    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )

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






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				self.__linear
          elif kernel == "rbf":
                 if self.gamma == 0:
                        raise ValueError('rbf kernel requires gamma')
                 if not isinstance(self.gamma					,  (float, int)):
                        raise ValueError('gamma must be float or int')
                 if not self.gamma > 0:
                        raise ValueError('gamma must be > 0')
                 a__						:	str =				self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
          else:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				classes

          # using Wolfe's Dual to calculate w.
          # Primal problem: minimize 1/2*norm_squared(w)
          #   constraint: yn(w . xn + b) >= 1
          #
          # With l a vector
          # Dual problem: maximize sum_n(ln) -
          #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
          #   constraint: self.C >= ln >= 0
          #           and sum_n(ln*yn) = 0
          # Then we get w using w = sum_n(ln*yn*xn)
          # At the end we can get b ~= mean(yn - w . xn)
          #
          # Since we use kernels, we only need l_star to calculate b
          # and to classify observations

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)
                 for i in range(lowercase):
                        for j in range(lowercase):
                               s += (
                                   candidate[i]
                                   * candidate[j]
                                   * classes[i]
                                   * classes[j]
                                   * self.kernel(observations[i]					,  observations[j])
                               )
                 return 1 / 2 * s - sum(lowercase)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				0
          for i in range(lowercase):
                 for j in range(lowercase):
                        s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                            observations[i]					,  observations[j])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	int =				sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n]					,  lowercase)
              for n in range(len(self.classes)))
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


