





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:
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=30				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     snake_case_=True				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=10				,     snake_case_=0.02				,     snake_case_=3				,     snake_case_=0.6				,     snake_case_=None				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							image_size
         _A     =							patch_size
         _A     =							num_channels
         _A     =							is_training
         _A     =							use_labels
         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							type_sequence_label_size
         _A     =							initializer_range
         _A     =							mask_ratio
         _A     =							scope

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




     def        lowerCAmelCase__		(    self							):
         _A     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

         _A     =							self.get_config()

         return config, pixel_values, labels




     def        lowerCAmelCase__		(    self							):
         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=snake_case_				,     initializer_range=self.initializer_range				,     mask_ratio=self.mask_ratio				,     )




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							ViTMAEModel(config=snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							ViTMAEForPreTraining(snake_case_							)
         model.to(snake_case_							)
         model.eval()
         _A     =							model(snake_case_							)
         _A     =							(self.image_size // self.patch_size) ** 2
         _A     =							self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, num_patches, expected_num_channels)							)

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




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







@require_torch
class      lowerCamelCase(						__snake_case			, __snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     __magic_name__   =			{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False
     __magic_name__   =			False




     def        lowerCAmelCase__		(    self							):
         _A     =							ViTMAEModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     has_text_modality=snake_case_				,     hidden_size=37							)




     def        lowerCAmelCase__		(    self							):
         self.config_tester.run_common_tests()




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




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

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




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

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

             _A     =							['pixel_values']
             self.assertListEqual(arg_names[:1]				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         # make masks reproducible
         np.random.seed(2							)

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

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

         super().check_pt_tf_models(snake_case_				,     snake_case_				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                 # Make sure we don't have nans
                 _A     =							after_outputs[0].cpu().numpy()
                 _A     =							0
                 _A     =							np.amax(np.abs(out_a - out_a							)							)
                 self.assertLessEqual(snake_case_				,     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        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self							):
         pass




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




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




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =							ViTMAEModel.from_pretrained(snake_case_							)
             self.assertIsNotNone(snake_case_							)





def 				__lowerCAmelCase(       )							-> Any:




    """simple docstring"""




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







@require_torch
@require_vision
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @cached_property
     def        lowerCAmelCase__		(    self							):
         return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'							) if is_vision_available() else None




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

         _A     =							ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							).to(snake_case_							)

         _A     =							self.default_image_processor
         _A     =							prepare_img()
         _A     =							image_processor(images=snake_case_				,     return_tensors='pt'							).to(snake_case_							)

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

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

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

         _A     =							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(snake_case_							)				,     atol=1E-4							)							)












import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__A							:			List[Any]         =			"python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    require_version(deps[pkg]							,     _SCREAMING_SNAKE_CASE				)





