


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 	__lowerCamelCase			:







       def __init__(			self: Dict,A_: List[Any],A_: Dict=13,A_: Union[str, Any]=30,A_: str=2,A_: int=3,A_: Any=True,A_: Union[str, Any]=True,A_: Dict=32,A_: Optional[Any]=5,A_: Optional[Any]=4,A_: Tuple=37,A_: List[Any]="gelu",A_: str=0.1,A_: Tuple=0.1,A_: List[str]=10,A_: Union[str, Any]=0.0_2,A_: Dict=3,A_: Any=0.6,A_: int=None,):






            '''simple docstring'''




            __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 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __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 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            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=A_,initializer_range=self.initializer_range,mask_ratio=self.mask_ratio,)







       def 		snake_case_	(			self: Optional[Any],A_: List[str],A_: List[Any],A_: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			ViTMAEModel(config=A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_					)
            self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)					)







       def 		snake_case_	(			self: List[Any],A_: Optional[int],A_: Dict,A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			ViTMAEForPreTraining(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			model(A_					)
            __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(A_					)
            model.to(A_					)
            model.eval()
            __UpperCamelCase =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            __UpperCamelCase =			model(A_					)
            __UpperCamelCase =			self.patch_size**2
            self.parent.assertEqual(result.logits.shape,(self.batch_size, num_patches, expected_num_channels)					)







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.prepare_config_and_inputs()
            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			config_and_inputs
            __UpperCamelCase =			{'pixel_values': pixel_values}
            return config, inputs_dict



@require_torch
class 	__lowerCamelCase			(_a				,      _a				,      unittest.TestCase						):
       _lowercase											=     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
       _lowercase											=     {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False
       _lowercase											=     False







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			ViTMAEModelTester(self					)
            __UpperCamelCase =			ConfigTester(self,config_class=A_,has_text_modality=A_,hidden_size=37					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            self.config_tester.run_common_tests()







       @unittest.skip(reason='ViTMAE does not use inputs_embeds'					)
       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            pass







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __UpperCamelCase =			model_class(A_					)
                 __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],A_					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A_					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_pretraining(*A_					)







       def 		snake_case_	(			self: str,A_: str,A_: List[str],A_: Union[str, Any]					):






            '''simple docstring'''




            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(A_					)

            # 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(A_,A_,A_					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                      # 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(A_,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 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            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 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            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 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







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






            '''simple docstring'''




            pass







       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'					)
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            pass







       @slow
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __UpperCamelCase =			ViTMAEModel.from_pretrained(A_					)
                 self.assertIsNotNone(A_					)






def 		_A		(		)					-> Any:


     """simple docstring"""


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



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







       @cached_property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'					) if is_vision_available() else None







       @slow
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            np.random.seed(2					)

            __UpperCamelCase =			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'					).to(A_					)

            __UpperCamelCase =			self.default_image_processor
            __UpperCamelCase =			prepare_img()
            __UpperCamelCase =			image_processor(images=A_,return_tensors='pt'					).to(A_					)

            # 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(**A_,noise=torch.from_numpy(A_					).to(device=A_					)					)

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

            __UpperCamelCase =			torch.tensor(
                [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]					)

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




"""simple docstring"""






from collections.abc import Callable





class 				a__					:



     def __init__(							self :Tuple      ,  _lowerCamelCase :Callable | None = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					list   	=[]
      # Stores indexes of each item for supporting updates and deletion.
      UpperCamelCase_     :					dict   	={}
      # Stores current size of heap.
      UpperCamelCase_     :					Any   	=0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      UpperCamelCase_     :					List[str]   	=key or (lambda _lowerCamelCase   : x)



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return int((i - 1) / 2   ) if i > 0 else None



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=int(2 * i + 1   )
      return left if 0 < left < self.size else None



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=int(2 * i + 2   )
      return right if 0 < right < self.size else None



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[int]   	=(
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      UpperCamelCase_	,     UpperCamelCase_     :					Union[str, Any]   	=self.arr[j], self.arr[i]



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      return self.arr[i][1] < self.arr[j][1]



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self._left(_lowerCamelCase   )
      UpperCamelCase_     :					List[Any]   	=self._right(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=i

      if left is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=left
      if right is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       UpperCamelCase_     :					List[Any]   	=right

      return valid_parent



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=self._parent(_lowerCamelCase   )
      while parent is not None and not self._cmp(_lowerCamelCase      ,  _lowerCamelCase   ):
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					Dict   	=parent, self._parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self._get_valid_parent(_lowerCamelCase   )
      while valid_parent != index:
       self._swap(_lowerCamelCase      ,  _lowerCamelCase   )
       UpperCamelCase_	,     UpperCamelCase_     :					int   	=valid_parent, self._get_valid_parent(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					List[Any]   	=self.pos_map[item]
      UpperCamelCase_     :					int   	=[item, self.key(_lowerCamelCase   )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_lowerCamelCase   )
      self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      if item not in self.pos_map:
       return
      UpperCamelCase_     :					Any   	=self.pos_map[item]
      del self.pos_map[item]
      UpperCamelCase_     :					Dict   	=self.arr[self.size - 1]
      UpperCamelCase_     :					Optional[int]   	=index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
       self._heapify_up(_lowerCamelCase   )
       self._heapify_down(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Optional[int]      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=len(self.arr   )
      if arr_len == self.size:
       self.arr.append([item, self.key(_lowerCamelCase   )]   )
      else:
       UpperCamelCase_     :					str   	=[item, self.key(_lowerCamelCase   )]
      UpperCamelCase_     :					Optional[int]   	=self.size
      self.size += 1
      self._heapify_up(self.size - 1   )



     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self.arr[0] if self.size else None




     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_top()
      if top_item_tuple:
       self.delete_item(top_item_tuple[0]   )
      return top_item_tuple




def       A_  (				):
 pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()
