


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 	A_ :




      '''simple docstring'''


      def __init__(    self:	int						,				a:	Optional[int]						,				a:	List[Any]=13						,				a:	Optional[Any]=30						,				a:	str=2						,				a:	List[Any]=3						,				a:	int=True						,				a:	Any=True						,				a:	str=32						,				a:	List[Any]=5						,				a:	Optional[int]=4						,				a:	Tuple=37						,				a:	Dict="gelu"						,				a:	Dict=0.1						,				a:	Any=0.1						,				a:	List[Any]=10						,				a:	List[str]=0.0_2						,				a:	List[Any]=3						,				a:	List[str]=0.6						,				a:	List[Any]=None						,				):
       __lowerCamelCase		:							Optional[int]		 =						parent
       __lowerCamelCase		:							List[Any]		 =						batch_size
       __lowerCamelCase		:							int		 =						image_size
       __lowerCamelCase		:							List[Any]		 =						patch_size
       __lowerCamelCase		:							Optional[int]		 =						num_channels
       __lowerCamelCase		:							List[str]		 =						is_training
       __lowerCamelCase		:							int		 =						use_labels
       __lowerCamelCase		:							str		 =						hidden_size
       __lowerCamelCase		:							int		 =						num_hidden_layers
       __lowerCamelCase		:							List[str]		 =						num_attention_heads
       __lowerCamelCase		:							Optional[int]		 =						intermediate_size
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Optional[int]		 =						hidden_dropout_prob
       __lowerCamelCase		:							List[str]		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							Optional[int]		 =						type_sequence_label_size
       __lowerCamelCase		:							Any		 =						initializer_range
       __lowerCamelCase		:							Optional[Any]		 =						mask_ratio
       __lowerCamelCase		:							Tuple		 =						scope

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




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							Tuple		 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       __lowerCamelCase		:							Optional[int]		 =						None
       if self.use_labels:
        __lowerCamelCase		:							str		 =						ids_tensor([self.batch_size]						,				self.type_sequence_label_size    )

       __lowerCamelCase		:							Any		 =						self.get_config()

       return config, pixel_values, labels




      def 					_snake_case     (    self:	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=a						,				initializer_range=self.initializer_range						,				mask_ratio=self.mask_ratio						,				)




      def 					_snake_case     (    self:	int						,				a:	List[Any]						,				a:	str						,				a:	int    ):
       __lowerCamelCase		:							Optional[Any]		 =						ViTMAEModel(config=a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							Tuple		 =						model(a    )
       self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)    )




      def 					_snake_case     (    self:	int						,				a:	Union[str, Any]						,				a:	Union[str, Any]						,				a:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						ViTMAEForPreTraining(a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							str		 =						model(a    )
       __lowerCamelCase		:							Union[str, Any]		 =						(self.image_size // self.patch_size) ** 2
       __lowerCamelCase		:							List[Any]		 =						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		:							Tuple		 =						1
       __lowerCamelCase		:							int		 =						ViTMAEForPreTraining(a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							int		 =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       __lowerCamelCase		:							Tuple		 =						model(a    )
       __lowerCamelCase		:							Any		 =						self.patch_size**2
       self.parent.assertEqual(result.logits.shape						,				(self.batch_size, num_patches, expected_num_channels)    )




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



@require_torch
class 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      __snake_case 	=       {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

      __snake_case 	=       False
      __snake_case 	=       False
      __snake_case 	=       False
      __snake_case 	=       False




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Dict		 =						ViTMAEModelTester(self    )
       __lowerCamelCase		:							Tuple		 =						ConfigTester(self						,				config_class=a						,				has_text_modality=a						,				hidden_size=37    )




      def 					_snake_case     (    self:	Dict    ):
       self.config_tester.run_common_tests()




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




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							Union[str, Any]		 =						model_class(a    )
        self.assertIsInstance(model.get_input_embeddings()						,				(nn.Module)    )
        __lowerCamelCase		:							Tuple		 =						model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(a						,				nn.Linear    )    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase ,					__lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCamelCase		:							str		 =						['pixel_values']
        self.assertListEqual(arg_names[:1]						,				a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							str		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Any		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*a    )




      def 					_snake_case     (    self:	List[str]						,				a:	Dict						,				a:	Optional[Any]						,				a:	Any    ):
       # make masks reproducible
       np.random.seed(2    )

       __lowerCamelCase		:							Tuple		 =						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
       __lowerCamelCase		:							List[Any]		 =						np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
       __lowerCamelCase		:							Optional[Any]		 =						torch.from_numpy(a    )

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

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




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

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							str		 =						model_class(a    )
        model.to(a    )
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2    )
        with torch.no_grad():
         __lowerCamelCase		:							Optional[int]		 =						model(**self._prepare_for_class(a						,				a    )    )

        __lowerCamelCase		:							Optional[int]		 =						outputs[0].cpu().numpy()
        __lowerCamelCase		:							Optional[int]		 =						0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(a    )
         __lowerCamelCase		:							Union[str, Any]		 =						model_class.from_pretrained(a    )
         model.to(a    )
         # make random mask reproducible
         torch.manual_seed(2    )
         with torch.no_grad():
          __lowerCamelCase		:							Dict		 =						model(**self._prepare_for_class(a						,				a    )    )

         # Make sure we don't have nans
         __lowerCamelCase		:							List[str]		 =						after_outputs[0].cpu().numpy()
         __lowerCamelCase		:							Union[str, Any]		 =						0
         __lowerCamelCase		:							Any		 =						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:	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 					_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 					_snake_case     (    self:	int    ):
       pass




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




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




      @slow
      def 					_snake_case     (    self:	Dict    ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase		:							Tuple		 =						ViTMAEModel.from_pretrained(a    )
        self.assertIsNotNone(a    )



def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image



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




      '''simple docstring'''


      @cached_property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'    ) if is_vision_available() else None




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

       __lowerCamelCase		:							Any		 =						ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'    ).to(a    )

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

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							Union[str, Any]		 =						model(**a						,				noise=torch.from_numpy(a    ).to(device=a    )    )

       # verify the logits
       __lowerCamelCase		:							List[Any]		 =						torch.Size((1, 196, 768)    )
       self.assertEqual(outputs.logits.shape						,				a    )

       __lowerCamelCase		:							Union[str, Any]		 =						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    )    )





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

 for i in range(n - 1						):
  for j in range(i + 1      ,	SCREAMING_SNAKE_CASE__						):
   if arr[i] > arr[j]:
    num_inversions += 1

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  if p[i] > q[j]:
   # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
   # These are all inversions. The claim emerges from the
   # property that P is sorted.
   num_inversion += len(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

 if i < len(SCREAMING_SNAKE_CASE__						):
  r.extend(p[i:]						)
 else:
  r.extend(q[j:]						)

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

 # this arr has 8 inversions:
 # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # testing an array with zero inversion (a sorted arr_1)

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

