"""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      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=3_0		,			a=2		,			a=3		,			a=True		,			a=True		,			a=3_2		,			a=2		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=1_0		,			a=0.02		,			a=3		,			a=0.6		,			a=None		,			)       ->					Any:
             lowercase__			:		List[str]       =	parent
             lowercase__			:		List[Any]       =	batch_size
             lowercase__			:		List[Any]       =	image_size
             lowercase__			:		List[Any]       =	patch_size
             lowercase__			:		List[Any]       =	num_channels
             lowercase__			:		List[str]       =	is_training
             lowercase__			:		int       =	use_labels
             lowercase__			:		Optional[Any]       =	hidden_size
             lowercase__			:		Union[str, Any]       =	num_hidden_layers
             lowercase__			:		Tuple       =	num_attention_heads
             lowercase__			:		Any       =	intermediate_size
             lowercase__			:		str       =	hidden_act
             lowercase__			:		Any       =	hidden_dropout_prob
             lowercase__			:		Optional[int]       =	attention_probs_dropout_prob
             lowercase__			:		int       =	type_sequence_label_size
             lowercase__			:		List[str]       =	initializer_range
             lowercase__			:		Optional[Any]       =	mask_ratio
             lowercase__			:		Union[str, Any]       =	scope

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


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__			:		Dict       =	None
             if self.use_labels:
                    lowercase__			:		int       =	ids_tensor([self.batch_size]		,			self.type_sequence_label_size							)

             lowercase__			:		str       =	self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             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=a		,			initializer_range=self.initializer_range		,			mask_ratio=self.mask_ratio		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[Any]:
             lowercase__			:		Optional[int]       =	TFViTMAEModel(config=a							)
             lowercase__			:		Any       =	model(a		,			training=a							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		Tuple       =	TFViTMAEForPreTraining(a							)
             lowercase__			:		Any       =	model(a		,			training=a							)
             # expected sequence length = num_patches
             lowercase__			:		Optional[Any]       =	(self.image_size // self.patch_size) ** 2
             lowercase__			:		Tuple       =	self.patch_size**2 * self.num_channels
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)							)

             # test greyscale images
             lowercase__			:		Union[str, Any]       =	1
             lowercase__			:		Optional[int]       =	TFViTMAEForPreTraining(a							)

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


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[str]       =	self.prepare_config_and_inputs()
             ((lowercase__)    ,      (lowercase__)    ,      (lowercase__))			:		str       =	config_and_inputs
             lowercase__			:		Union[str, Any]       =	{'pixel_values': pixel_values}
             return config, inputs_dict





@require_tf
class      UpperCAmelCase_	(    _a ,							_a ,							unittest.TestCase):
      lowerCamelCase__		:    List[Any]   =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
      lowerCamelCase__		:    List[str]   =  {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

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


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Dict       =	TFViTMAEModelTester(self							)
             lowercase__			:		Any       =	ConfigTester(self		,			config_class=a		,			has_text_modality=a		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             self.config_tester.run_common_tests()


      @unittest.skip(reason='ViTMAE does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		List[str]       =	model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()		,			(tf.keras.layers.Layer)							)
                    lowercase__			:		Optional[Any]       =	model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a		,			tf.keras.layers.Layer							)							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__    ,      lowercase__			:		Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__			:		Tuple       =	['pixel_values']
                    self.assertListEqual(arg_names[:1]		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[str]       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # make the mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

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

             for model_class in self.all_model_classes:
                    lowercase__			:		Tuple       =	model_class(a							)
                    lowercase__			:		str       =	self._prepare_for_class(a		,			a							)

                    lowercase__			:		Any       =	model(a		,			noise=a							)

                    lowercase__			:		List[str]       =	copy.deepcopy(self._prepare_for_class(a		,			a							)							)
                    lowercase__			:		str       =	model(**a		,			noise=a							)
                    lowercase__			:		str       =	outputs_dict[0].numpy()
                    lowercase__			:		Dict       =	outputs_keywords[0].numpy()

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


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             # make the mask reproducible
             np.random.seed(2							)

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

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

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

                    return inputs_np_dict

             for model_class in self.all_model_classes:
                    lowercase__			:		Any       =	model_class(a							)

                    lowercase__			:		Optional[int]       =	self._prepare_for_class(a		,			a							)
                    lowercase__			:		Tuple       =	prepare_numpy_arrays(a							)

                    lowercase__			:		str       =	model(a		,			noise=a							)
                    lowercase__			:		Any       =	model(**a		,			noise=a							)
                    self.assert_outputs_same(a		,			a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[str]:
             # make masks reproducible
             np.random.seed(2							)

             lowercase__			:		int       =	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
             lowercase__			:		str       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
             lowercase__			:		Union[str, Any]       =	tf.constant(a							)

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

             super().check_pt_tf_models(a		,			a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             # make mask reproducible
             np.random.seed(2							)

             lowercase__    ,      lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__			:		List[Any]       =	{
                 module_member
                 for model_class in self.all_model_classes
                 for module in (import_module(model_class.__module__							),)
                 for module_member_name in dir(a							)
                 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(a		,			a							),)
                 if isinstance(a		,			a							)
                 and tf.keras.layers.Layer in module_member.__bases__
                 and getattr(a		,			'_keras_serializable'		,			a							)
             }

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

             for main_layer_class in tf_main_layer_classes:
                    lowercase__			:		int       =	main_layer_class(a							)

                    lowercase__			:		Dict       =	{
                        name: tf.keras.Input(tensor.shape[1:]		,			dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
                    }

                    lowercase__			:		List[str]       =	tf.keras.Model(a		,			outputs=main_layer(a							)							)
                    lowercase__			:		List[Any]       =	model(a							)

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


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             # make mask reproducible
             np.random.seed(2							)

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

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

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

                    if model_class.__name__ == "TFViTMAEModel":
                           lowercase__			:		List[Any]       =	outputs.last_hidden_state.numpy()
                           lowercase__			:		Tuple       =	0
                    else:
                           lowercase__			:		Tuple       =	outputs.logits.numpy()
                           lowercase__			:		Optional[int]       =	0

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(a		,			saved_model=a							)
                           lowercase__			:		Tuple       =	model_class.from_pretrained(a							)
                           lowercase__			:		List[str]       =	model(a		,			noise=a							)

                           if model_class.__name__ == "TFViTMAEModel":
                                  lowercase__			:		List[Any]       =	after_outputs['last_hidden_state'].numpy()
                                  lowercase__			:		List[Any]       =	0
                           else:
                                  lowercase__			:		int       =	after_outputs['logits'].numpy()
                                  lowercase__			:		Any       =	0

                           lowercase__			:		Dict       =	np.amax(np.abs(out_a - out_a							)							)
                           self.assertLessEqual(a		,			1e-5							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             # make mask reproducible
             np.random.seed(2							)

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

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

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

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

                    self.assert_outputs_same(a		,			a							)


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


      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'							)
      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		List[str]       =	TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'							)
             self.assertIsNotNone(a							)





def 				a_     (      ):





       '''simple docstring'''

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






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


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


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

             lowercase__			:		List[str]       =	TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							)

             lowercase__			:		int       =	self.default_image_processor
             lowercase__			:		Tuple       =	prepare_img()
             lowercase__			:		Dict       =	image_processor(images=a		,			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)
             lowercase__			:		Optional[Any]       =	ViTMAEConfig()
             lowercase__			:		Optional[int]       =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
             lowercase__			:		Optional[Any]       =	np.random.uniform(size=(1, num_patches)							)

             # forward pass
             lowercase__			:		List[Any]       =	model(**a		,			noise=a							)

             # verify the logits
             lowercase__			:		str       =	tf.convert_to_tensor([1, 1_9_6, 7_6_8]							)
             self.assertEqual(outputs.logits.shape		,			a							)

             lowercase__			:		List[str]       =	tf.convert_to_tensor(
                 [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]							)

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




def 		_lowercase (     lowercase__   ,	lowercase__						):
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def 		_lowercase (     lowercase__   ,	lowercase__=0						):
      return sorted(lowercase__   ,	key=lambda lowercase__						: x[column]						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(points_counts - 1						):
            for j in range(i + 1   ,	lowercase__						):
                  __lowerCAmelCase					:						List[str]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						Tuple					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=float('''inf'''						)						):

      for i in range(min(6   ,	points_counts - 1						)   ,	lowercase__						):
            for j in range(max(0   ,	i - 6						)   ,	lowercase__						):
                  __lowerCAmelCase					:						Union[str, Any]					=					euclidean_distance_sqr(points[i]   ,	points[j]						)
                  if current_dis < min_dis:
                        __lowerCAmelCase					:						int					=					current_dis
      return min_dis







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(lowercase__   ,	lowercase__						)

      # recursion
      __lowerCAmelCase					:						Optional[Any]					=					points_counts // 2
      __lowerCAmelCase					:						Optional[Any]					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[:mid]   ,	lowercase__						)
      __lowerCAmelCase					:						str					=					closest_pair_of_points_sqr(
          lowercase__   ,	points_sorted_on_y[mid:]   ,	points_counts - mid						)
      __lowerCAmelCase					:						Optional[int]					=					min(lowercase__   ,	lowercase__						)
      __lowerCAmelCase					:						Tuple					=					[]
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
                  cross_strip.append(lowercase__						)

      __lowerCAmelCase					:						List[Any]					=					dis_between_closest_in_strip(
          lowercase__   ,	len(lowercase__						)   ,	lowercase__						)
      return min(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					column_based_sort(lowercase__   ,	column=0						)
      __lowerCAmelCase					:						Any					=					column_based_sort(lowercase__   ,	column=1						)
      return (
          closest_pair_of_points_sqr(
              lowercase__   ,	lowercase__   ,	lowercase__						)
      ) ** 0.5


if __name__ == "__main__":
 _UpperCamelCase 			=		[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
 print("Distance:", closest_pair_of_points(points, len(points)))


