





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      _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self       :     Union[str, Any] ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     Any=13 ,				UpperCAmelCase_       :     Optional[int]=30 ,				UpperCAmelCase_       :     Union[str, Any]=2 ,				UpperCAmelCase_       :     str=3 ,				UpperCAmelCase_       :     Tuple=True ,				UpperCAmelCase_       :     Dict=True ,				UpperCAmelCase_       :     int=32 ,				UpperCAmelCase_       :     Optional[int]=5 ,				UpperCAmelCase_       :     Optional[int]=4 ,				UpperCAmelCase_       :     str=37 ,				UpperCAmelCase_       :     Any="gelu" ,				UpperCAmelCase_       :     Union[str, Any]=0.1 ,				UpperCAmelCase_       :     List[str]=0.1 ,				UpperCAmelCase_       :     Optional[int]=10 ,				UpperCAmelCase_       :     Tuple=0.02 ,				UpperCAmelCase_       :     str=3 ,				UpperCAmelCase_       :     Union[str, Any]=0.6 ,				UpperCAmelCase_       :     Dict=None ,				) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   int    						=parent
   lowerCamelCase__:   int    						=batch_size
   lowerCamelCase__:   Union[str, Any]    						=image_size
   lowerCamelCase__:   Dict    						=patch_size
   lowerCamelCase__:   List[Any]    						=num_channels
   lowerCamelCase__:   Tuple    						=is_training
   lowerCamelCase__:   Dict    						=use_labels
   lowerCamelCase__:   Optional[int]    						=hidden_size
   lowerCamelCase__:   Any    						=num_hidden_layers
   lowerCamelCase__:   List[Any]    						=num_attention_heads
   lowerCamelCase__:   Optional[Any]    						=intermediate_size
   lowerCamelCase__:   str    						=hidden_act
   lowerCamelCase__:   int    						=hidden_dropout_prob
   lowerCamelCase__:   List[str]    						=attention_probs_dropout_prob
   lowerCamelCase__:   Optional[Any]    						=type_sequence_label_size
   lowerCamelCase__:   int    						=initializer_range
   lowerCamelCase__:   List[str]    						=mask_ratio
   lowerCamelCase__:   Optional[int]    						=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[str]    						=(image_size // patch_size) ** 2
   lowerCamelCase__:   Union[str, Any]    						=int(math.ceil((1 - mask_ratio) * (num_patches + 1)))

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

   lowerCamelCase__:   Optional[Any]    						=self.get_config()

   return config, pixel_values, labels

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->str:



   '''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 							SCREAMING_SNAKE_CASE_      (self       :     int ,				UpperCAmelCase_       :     List[str] ,				UpperCAmelCase_       :     List[str] ,				UpperCAmelCase_       :     List[str]) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=ViTMAEModel(config=__A)
   model.to(__A)
   model.eval()
   lowerCamelCase__:   List[str]    						=model(__A)
   self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size))

 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     Tuple ,				UpperCAmelCase_       :     Dict ,				UpperCAmelCase_       :     int) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=ViTMAEForPreTraining(__A)
   model.to(__A)
   model.eval()
   lowerCamelCase__:   Tuple    						=model(__A)
   lowerCamelCase__:   List[str]    						=(self.image_size // self.patch_size) ** 2
   lowerCamelCase__:   str    						=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__:   Dict    						=1
   lowerCamelCase__:   str    						=ViTMAEForPreTraining(__A)
   model.to(__A)
   model.eval()
   lowerCamelCase__:   Any    						=floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowerCamelCase__:   Any    						=model(__A)
   lowerCamelCase__:   List[Any]    						=self.patch_size**2
   self.parent.assertEqual(result.logits.shape ,				(self.batch_size, num_patches, expected_num_channels))







 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=self.prepare_config_and_inputs()
   lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__:   List[Any]    						=config_and_inputs
   lowerCamelCase__:   Any    						={"pixel_values": pixel_values}
   return config, inputs_dict



@require_torch
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 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 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=ViTMAEModelTester(self)
   lowerCamelCase__:   str    						=ConfigTester(self ,				config_class=__A ,				has_text_modality=__A ,				hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->str:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="ViTMAE does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->int:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Any:



   '''simple docstring'''



   lowerCamelCase__						,      lowerCamelCase__:   Optional[int]    						=self.model_tester.prepare_config_and_inputs_for_common()

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->Dict:



   '''simple docstring'''



   lowerCamelCase__						,      lowerCamelCase__:   str    						=self.model_tester.prepare_config_and_inputs_for_common()

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

     lowerCamelCase__:   Tuple    						=["pixel_values"]
     self.assertListEqual(arg_names[:1] ,				__A)

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__A)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*__A)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     List[Any] ,				UpperCAmelCase_       :     Tuple ,				UpperCAmelCase_       :     Optional[int]) ->List[str]:



   '''simple docstring'''



   np.random.seed(2)

   lowerCamelCase__:   List[Any]    						=int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
   lowerCamelCase__:   Union[str, Any]    						=np.random.uniform(size=(self.model_tester.batch_size, num_patches))
   lowerCamelCase__:   List[str]    						=torch.from_numpy(__A)

   # Add `noise` argument.
   # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
   lowerCamelCase__:   Union[str, Any]    						=pt_noise

   super().check_pt_tf_models(__A ,				__A ,				__A)

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Dict:



   '''simple docstring'''



   lowerCamelCase__						,      lowerCamelCase__:   str    						=self.model_tester.prepare_config_and_inputs_for_common()

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

     lowerCamelCase__:   Union[str, Any]    						=outputs[0].cpu().numpy()
     lowerCamelCase__:   Optional[int]    						=0

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

       # Make sure we don't have nans
       lowerCamelCase__:   Dict    						=after_outputs[0].cpu().numpy()
       lowerCamelCase__:   List[Any]    						=0
       lowerCamelCase__:   str    						=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 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->int:



   '''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 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->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 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Optional[Any]:



   '''simple docstring'''



   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]) ->Any:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   pass







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Dict:



   '''simple docstring'''



   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:   Any    						=ViTMAEModel.from_pretrained(__A)
     self.assertIsNotNone(__A)





def   lowerCAmelCase_				( )  ->	List[Any]:







  """simple docstring"""





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







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







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Any:



   '''simple docstring'''



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







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Union[str, Any]:



   '''simple docstring'''



   np.random.seed(2)

   lowerCamelCase__:   Optional[Any]    						=ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(__A)

   lowerCamelCase__:   int    						=self.default_image_processor
   lowerCamelCase__:   Union[str, Any]    						=prepare_img()
   lowerCamelCase__:   Dict    						=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__:   Tuple    						=ViTMAEConfig()
   lowerCamelCase__:   Optional[Any]    						=int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
   lowerCamelCase__:   Dict    						=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__:   Optional[int]    						=torch.Size((1, 196, 768))
   self.assertEqual(outputs.logits.shape ,				__A)

   lowerCamelCase__:   str    						=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(__A) ,				atol=1E-4))








from bisect import bisect
from itertools import accumulate





def   lowerCAmelCase_				( __a			,					__a			,					__a			,					__a	)  ->	List[Any]:







  """simple docstring"""





  lowerCamelCase__:   str    						=sorted(zip(__a			,					__a	)			,					key=lambda __a	: x[0] / x[1]			,					reverse=__a	)
  lowerCamelCase__						,      lowerCamelCase__:   str    						=[i[0] for i in r], [i[1] for i in r]
  lowerCamelCase__:   Tuple    						=list(accumulate(__a	)	)
  lowerCamelCase__:   Any    						=bisect(__a			,					__a	)
  return (
      0
      if k == 0
      else sum(vl[:k]	) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]	)
  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

