


"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ViTMAEForPreTraining, ViTMAEModel
   from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor





class __snake_case		:







   """simple docstring"""




   def __init__(		self				:Optional[Any]					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:Dict=13					,	UpperCamelCase__				:Union[str, Any]=30					,	UpperCamelCase__				:str=2					,	UpperCamelCase__				:Optional[int]=3					,	UpperCamelCase__				:List[Any]=True					,	UpperCamelCase__				:List[Any]=True					,	UpperCamelCase__				:Optional[Any]=32					,	UpperCamelCase__				:Union[str, Any]=5					,	UpperCamelCase__				:Optional[int]=4					,	UpperCamelCase__				:Optional[Any]=37					,	UpperCamelCase__				:Union[str, Any]="gelu"					,	UpperCamelCase__				:List[Any]=0.1					,	UpperCamelCase__				:Union[str, Any]=0.1					,	UpperCamelCase__				:List[Any]=10					,	UpperCamelCase__				:Tuple=0.02					,	UpperCamelCase__				:Optional[Any]=3					,	UpperCamelCase__				:List[str]=0.6					,	UpperCamelCase__				:Tuple=None					,	):
          _a							 =					parent
          _a							 =					batch_size
          _a							 =					image_size
          _a							 =					patch_size
          _a							 =					num_channels
          _a							 =					is_training
          _a							 =					use_labels
          _a							 =					hidden_size
          _a							 =					num_hidden_layers
          _a							 =					num_attention_heads
          _a							 =					intermediate_size
          _a							 =					hidden_act
          _a							 =					hidden_dropout_prob
          _a							 =					attention_probs_dropout_prob
          _a							 =					type_sequence_label_size
          _a							 =					initializer_range
          _a							 =					mask_ratio
          _a							 =					scope

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




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          _a							 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          _a							 =					None
          if self.use_labels:
                 _a							 =					ids_tensor([self.batch_size]					,	self.type_sequence_label_size							)

          _a							 =					self.get_config()

          return config, pixel_values, labels




   def SCREAMING_SNAKE_CASE_   (		self				: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					,	hidden_act=self.hidden_act					,	hidden_dropout_prob=self.hidden_dropout_prob					,	attention_probs_dropout_prob=self.attention_probs_dropout_prob					,	is_decoder=snake_case_					,	initializer_range=self.initializer_range					,	mask_ratio=self.mask_ratio					,	)




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]					,	UpperCamelCase__				:Optional[int]					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:Dict							):
          _a							 =					ViTMAEModel(config=snake_case_							)
          model.to(snake_case_							)
          model.eval()
          _a							 =					model(snake_case_							)
          self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)							)




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[Any]					,	UpperCamelCase__				:Union[str, Any]					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:Tuple							):
          _a							 =					ViTMAEForPreTraining(snake_case_							)
          model.to(snake_case_							)
          model.eval()
          _a							 =					model(snake_case_							)
          _a							 =					(self.image_size // self.patch_size) ** 2
          _a							 =					self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, num_patches, expected_num_channels)							)

          # test greyscale images
          _a							 =					1
          _a							 =					ViTMAEForPreTraining(snake_case_							)
          model.to(snake_case_							)
          model.eval()
          _a							 =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          _a							 =					model(snake_case_							)
          _a							 =					self.patch_size**2
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, num_patches, expected_num_channels)							)




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          _a							 =					self.prepare_config_and_inputs()
          _a ,			_a ,			_a							 =					config_and_inputs
          _a							 =					{"pixel_values": pixel_values}
          return config, inputs_dict






@require_torch
class __snake_case		(       __snake_case					,							__snake_case					,							unittest.TestCase   ):







   """simple docstring"""




   lowerCAmelCase_					: Optional[Any]  						=      (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   lowerCAmelCase_					: Any  						=      {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

   lowerCAmelCase_					: Optional[Any]  						=      False
   lowerCAmelCase_					: int  						=      False
   lowerCAmelCase_					: Dict  						=      False
   lowerCAmelCase_					: List[Any]  						=      False




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]							):
          _a							 =					ViTMAEModelTester(self							)
          _a							 =					ConfigTester(self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37							)




   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          self.config_tester.run_common_tests()




   @unittest.skip(reason="ViTMAE does not use inputs_embeds"							)
   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          pass




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple							):
          _a ,			_a							 =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 _a							 =					model_class(snake_case_							)
                 self.assertIsInstance(model.get_input_embeddings()					,	(nn.Module)							)
                 _a							 =					model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear							)							)




   def SCREAMING_SNAKE_CASE_   (		self				:Any							):
          _a ,			_a							 =					self.model_tester.prepare_config_and_inputs_for_common()

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

                 _a							 =					["pixel_values"]
                 self.assertListEqual(arg_names[:1]					,	snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          _a							 =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:List[str]					,	UpperCamelCase__				:Union[str, Any]					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[int]							):
          # make masks reproducible
          np.random.seed(2							)

          _a							 =					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
          _a							 =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
          _a							 =					torch.from_numpy(snake_case_							)

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          _a							 =					pt_noise

          super().check_pt_tf_models(snake_case_					,	snake_case_					,	snake_case_							)




   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          _a ,			_a							 =					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 _a							 =					model_class(snake_case_							)
                 model.to(snake_case_							)
                 model.eval()
                 # make random mask reproducible
                 torch.manual_seed(2							)
                 with torch.no_grad():
                        _a							 =					model(**self._prepare_for_class(snake_case_					,	snake_case_							)							)

                 _a							 =					outputs[0].cpu().numpy()
                 _a							 =					0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(snake_case_							)
                        _a							 =					model_class.from_pretrained(snake_case_							)
                        model.to(snake_case_							)
                        # make random mask reproducible
                        torch.manual_seed(2							)
                        with torch.no_grad():
                               _a							 =					model(**self._prepare_for_class(snake_case_					,	snake_case_							)							)

                        # Make sure we don't have nans
                        _a							 =					after_outputs[0].cpu().numpy()
                        _a							 =					0
                        _a							 =					np.amax(np.abs(out_a - out_a							)							)
                        self.assertLessEqual(snake_case_					,	1E-5							)




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




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




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




   @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"							)
   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          pass




   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
   def SCREAMING_SNAKE_CASE_   (		self				:List[str]							):
          pass




   @slow
   def SCREAMING_SNAKE_CASE_   (		self				:str							):
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _a							 =					ViTMAEModel.from_pretrained(snake_case_							)
                 self.assertIsNotNone(snake_case_							)






def     __a   (	):





       """simple docstring"""







       _a							 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image





@require_torch
@require_vision
class __snake_case		(       unittest.TestCase   ):







   """simple docstring"""




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




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

          _a							 =					ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"							).to(snake_case_							)

          _a							 =					self.default_image_processor
          _a							 =					prepare_img()
          _a							 =					image_processor(images=snake_case_					,	return_tensors="pt"							).to(snake_case_							)

          # 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)
          _a							 =					ViTMAEConfig()
          _a							 =					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
          _a							 =					np.random.uniform(size=(1, num_patches)							)

          # forward pass
          with torch.no_grad():
                 _a							 =					model(**snake_case_					,	noise=torch.from_numpy(snake_case_							).to(device=snake_case_							)							)

          # verify the logits
          _a							 =					torch.Size((1, 196, 768)							)
          self.assertEqual(outputs.logits.shape					,	snake_case_							)

          _a							 =					torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,	expected_slice.to(snake_case_							)					,	atol=1E-4							)							)







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    _A     =							int(number**0.5				)
    return number == sq * sq





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> tuple[int, int]:




    """simple docstring"""




    _A     =							x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _A     =							x_den * y_den * z_den
    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    top //= hcf
    bottom //= hcf
    return top, bottom





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 35				)							-> int:




    """simple docstring"""




    _A     =							set()
    _A     =							42
    _A     =							Fraction(0				)
    _A     =							42

    for x_num in range(1							,     order + 1				):
        for x_den in range(x_num + 1							,     order + 1				):
            for y_num in range(1							,     order + 1				):
                for y_den in range(y_num + 1							,     order + 1				):
                    # n=1
                    _A     =							x_num * y_den + x_den * y_num
                    _A     =							x_den * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _A     =							x_den * x_den * y_den * y_den
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=-1
                    _A     =							x_num * y_num
                    _A     =							x_den * y_num + x_num * y_den
                    _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _A     =							add_three(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        unique_s.add(_SCREAMING_SNAKE_CASE				)

                    # n=2
                    _A     =							x_num * x_num * y_num * y_num
                    _A     =							(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(_SCREAMING_SNAKE_CASE				) and is_sq(_SCREAMING_SNAKE_CASE				):
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							int(sqrt(_SCREAMING_SNAKE_CASE				)				)
                        _A     =							gcd(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _A     =							add_three(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                            unique_s.add(_SCREAMING_SNAKE_CASE				)

    for num, den in unique_s:
        total += Fraction(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    return total.denominator + total.numerator


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





