

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 						__magic_name__    :


 def __init__(						self		: Dict			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Optional[Any]=1_3			,						lowerCAmelCase__		: List[str]=3_0			,						lowerCAmelCase__		: Tuple=2			,						lowerCAmelCase__		: List[str]=3			,						lowerCAmelCase__		: str=True			,						lowerCAmelCase__		: Dict=True			,						lowerCAmelCase__		: str=3_2			,						lowerCAmelCase__		: Dict=5			,						lowerCAmelCase__		: Union[str, Any]=4			,						lowerCAmelCase__		: Union[str, Any]=3_7			,						lowerCAmelCase__		: Optional[int]="gelu"			,						lowerCAmelCase__		: Optional[int]=0.1			,						lowerCAmelCase__		: Tuple=0.1			,						lowerCAmelCase__		: Optional[Any]=1_0			,						lowerCAmelCase__		: List[Any]=0.02			,						lowerCAmelCase__		: Optional[int]=3			,						lowerCAmelCase__		: int=0.6			,						lowerCAmelCase__		: Any=None			,						)    ->      Optional[int]:
   UpperCAmelCase					      =  parent
   UpperCAmelCase					      =  batch_size
   UpperCAmelCase					      =  image_size
   UpperCAmelCase					      =  patch_size
   UpperCAmelCase					      =  num_channels
   UpperCAmelCase					      =  is_training
   UpperCAmelCase					      =  use_labels
   UpperCAmelCase					      =  hidden_size
   UpperCAmelCase					      =  num_hidden_layers
   UpperCAmelCase					      =  num_attention_heads
   UpperCAmelCase					      =  intermediate_size
   UpperCAmelCase					      =  hidden_act
   UpperCAmelCase					      =  hidden_dropout_prob
   UpperCAmelCase					      =  attention_probs_dropout_prob
   UpperCAmelCase					      =  type_sequence_label_size
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  mask_ratio
   UpperCAmelCase					      =  scope

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


 def _UpperCamelCase	(						self		: Any    )    ->      Optional[int]:
   UpperCAmelCase					      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   UpperCAmelCase					      =  self.get_config()

   return config, pixel_values, labels


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   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=lowerCAmelCase__			,						initializer_range=self.initializer_range			,						mask_ratio=self.mask_ratio			,						)


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: int    )    ->      Any:
   UpperCAmelCase					      =  ViTMAEModel(config=lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.eval()
   UpperCAmelCase					      =  model(lowerCAmelCase__    )
   self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Union[str, Any]    )    ->      int:
   UpperCAmelCase					      =  ViTMAEForPreTraining(lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.eval()
   UpperCAmelCase					      =  model(lowerCAmelCase__    )
   UpperCAmelCase					      =  (self.image_size // self.patch_size) ** 2
   UpperCAmelCase					      =  self.patch_size**2 * self.num_channels
   self.parent.assertEqual(result.logits.shape			,						(self.batch_size, num_patches, expected_num_channels)    )

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


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Tuple:
   UpperCAmelCase					      =  self.prepare_config_and_inputs()
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  config_and_inputs
   UpperCAmelCase					      =  {"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class 						__magic_name__    (       _snake_case	,						_snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 UpperCAmelCase							    =						{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      List[str]:
   UpperCAmelCase					      =  ViTMAEModelTester(self    )
   UpperCAmelCase					      =  ConfigTester(self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__			,						hidden_size=3_7    )


 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   self.config_tester.run_common_tests()


 @unittest.skip(reason="ViTMAE does not use inputs_embeds"    )
 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      int:
   pass


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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


 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[str]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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

     UpperCAmelCase					      =  ["pixel_values"]
     self.assertListEqual(arg_names[:1]			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Dict:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Any:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Any    )    ->      Optional[int]:
   # make masks reproducible
   np.random.seed(2    )

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

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

   super().check_pt_tf_models(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: str    )    ->      Union[str, Any]:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

       # Make sure we don't have nans
       UpperCAmelCase					      =  after_outputs[0].cpu().numpy()
       UpperCAmelCase					      =  0
       UpperCAmelCase					      =  np.amax(np.abs(out_a - out_a    )    )
       self.assertLessEqual(lowerCAmelCase__			,						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 _UpperCamelCase	(						self		: Any    )    ->      Tuple:
   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 _UpperCamelCase	(						self		: Any    )    ->      Optional[Any]:
   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 _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[int]:
   pass


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


 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Tuple:
   pass


 @slow
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase					      =  ViTMAEModel.from_pretrained(lowerCAmelCase__    )
     self.assertIsNotNone(lowerCAmelCase__    )

def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  return image






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


 @cached_property
 def _UpperCamelCase	(						self		: Tuple    )    ->      Dict:
   return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"    ) if is_vision_available() else None


 @slow
 def _UpperCamelCase	(						self		: str    )    ->      List[Any]:
   # make random mask reproducible across the PT and TF model
   np.random.seed(2    )

   UpperCAmelCase					      =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"    ).to(lowerCAmelCase__    )

   UpperCAmelCase					      =  self.default_image_processor
   UpperCAmelCase					      =  prepare_img()
   UpperCAmelCase					      =  image_processor(images=lowerCAmelCase__			,						return_tensors="pt"    ).to(lowerCAmelCase__    )

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

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

   # verify the logits
   UpperCAmelCase					      =  torch.Size((1, 1_9_6, 7_6_8)    )
   self.assertEqual(outputs.logits.shape			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  torch.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]]    )

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






def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  if n == 0:
    return 1

  elif n % 2 == 1:
    return (binary_exponentiation(__A   ,       n - 1   ,       __A			) * a) % mod

  else:
    UpperCAmelCase					      =  binary_exponentiation(__A   ,       n / 2   ,       __A			)
    return (b * b) % mod


# a prime number
lowerCAmelCase__       = 701

lowerCAmelCase__       = 1000000000
lowerCAmelCase__       = 10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)





print((a / b) % p == (a * b ** (p - 2)) % p)



