





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     lowercase__       :

   '''simple docstring'''


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






       """simple docstring"""
       UpperCamelCase__							:				str          =      parent
       UpperCamelCase__							:				str          =      batch_size
       UpperCamelCase__							:				List[Any]          =      image_size
       UpperCamelCase__							:				Union[str, Any]          =      patch_size
       UpperCamelCase__							:				Optional[Any]          =      num_channels
       UpperCamelCase__							:				Optional[int]          =      is_training
       UpperCamelCase__							:				str          =      use_labels
       UpperCamelCase__							:				Optional[Any]          =      hidden_size
       UpperCamelCase__							:				Any          =      num_hidden_layers
       UpperCamelCase__							:				List[str]          =      num_attention_heads
       UpperCamelCase__							:				Union[str, Any]          =      intermediate_size
       UpperCamelCase__							:				Dict          =      hidden_act
       UpperCamelCase__							:				Any          =      hidden_dropout_prob
       UpperCamelCase__							:				Any          =      attention_probs_dropout_prob
       UpperCamelCase__							:				int          =      type_sequence_label_size
       UpperCamelCase__							:				int          =      initializer_range
       UpperCamelCase__							:				Union[str, Any]          =      mask_ratio
       UpperCamelCase__							:				Optional[Any]          =      scope

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




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCamelCase__							:				List[Any]          =      None
       if self.use_labels:
           UpperCamelCase__							:				Union[str, Any]          =      ids_tensor([self.batch_size],		self.type_sequence_label_size							)

       UpperCamelCase__							:				Tuple          =      self.get_config()

       return config, pixel_values, labels




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """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=__magic_name__,		initializer_range=self.initializer_range,		mask_ratio=self.mask_ratio,		)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      ViTMAEModel(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Dict          =      model(__magic_name__							)
       self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      ViTMAEForPreTraining(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				List[Any]          =      model(__magic_name__							)
       UpperCamelCase__							:				str          =      (self.image_size // self.patch_size) ** 2
       UpperCamelCase__							:				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
       UpperCamelCase__							:				Optional[Any]          =      1
       UpperCamelCase__							:				Optional[Any]          =      ViTMAEForPreTraining(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				List[str]          =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       UpperCamelCase__							:				Tuple          =      model(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.patch_size**2
       self.parent.assertEqual(result.logits.shape,		(self.batch_size, num_patches, expected_num_channels)							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.prepare_config_and_inputs()
       UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      config_and_inputs
       UpperCamelCase__							:				str          =      {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


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

   a						:							str   		=		False
   a						:							Optional[Any]   		=		False
   a						:							int   		=		False
   a						:							Union[str, Any]   		=		False




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      ViTMAEModelTester(self							)
       UpperCamelCase__							:				Optional[int]          =      ConfigTester(self,		config_class=__magic_name__,		has_text_modality=__magic_name__,		hidden_size=37							)




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       self.config_tester.run_common_tests()




   @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''							)
   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       pass




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				Optional[Any]          =      self.model_tester.prepare_config_and_inputs_for_common()

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




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				str          =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:				Any          =      model_class(__magic_name__							)
           UpperCamelCase__							:				Union[str, Any]          =      inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__							:				Optional[Any]          =      [*signature.parameters.keys()]

           UpperCamelCase__							:				str          =      ['''pixel_values''']
           self.assertListEqual(arg_names[:1],		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       # make masks reproducible
       np.random.seed(2							)

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

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

       super().check_pt_tf_models(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				int          =      self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__							:				Dict          =      outputs[0].cpu().numpy()
           UpperCamelCase__							:				Union[str, Any]          =      0

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

               # Make sure we don't have nans
               UpperCamelCase__							:				List[Any]          =      after_outputs[0].cpu().numpy()
               UpperCamelCase__							:				int          =      0
               UpperCamelCase__							:				int          =      np.amax(np.abs(out_a - out_a							)							)
               self.assertLessEqual(__magic_name__,		1E-5							)




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       pass




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       pass




   @unittest.skip(
       reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       pass




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






       """simple docstring"""
       pass




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






       """simple docstring"""
       pass




   @slow
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:				str          =      ViTMAEModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




def 		lowerCAmelCase_    (  )      ->	Optional[int]:
    UpperCamelCase__							:				List[str]          =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    return image






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

   '''simple docstring'''


   @cached_property
   def       UpperCamelCase__  (    self							) ->      Tuple:






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




   @slow
   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       # make random mask reproducible across the PT and TF model
       np.random.seed(2							)

       UpperCamelCase__							:				Optional[int]          =      ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''							).to(__magic_name__							)

       UpperCamelCase__							:				str          =      self.default_image_processor
       UpperCamelCase__							:				List[Any]          =      prepare_img()
       UpperCamelCase__							:				Dict          =      image_processor(images=__magic_name__,		return_tensors='''pt'''							).to(__magic_name__							)

       # 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__							:				Union[str, Any]          =      ViTMAEConfig()
       UpperCamelCase__							:				Optional[int]          =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
       UpperCamelCase__							:				Optional[int]          =      np.random.uniform(size=(1, num_patches)							)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__							:				str          =      model(**__magic_name__,		noise=torch.from_numpy(__magic_name__							).to(device=__magic_name__							)							)

       # verify the logits
       UpperCamelCase__							:				Dict          =      torch.Size((1, 196, 768)							)
       self.assertEqual(outputs.logits.shape,		__magic_name__							)

       UpperCamelCase__							:				Union[str, Any]          =      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(__magic_name__							),		atol=1E-4							)							)








def 		lowerCAmelCase_    (  __UpperCAmelCase:			float		,   __UpperCAmelCase:			int      )      ->	float:
    if digit_amount > 0:
        return round(number - int(__UpperCAmelCase      )		,   __UpperCAmelCase      )
    return number - int(__UpperCAmelCase      )


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))

