




'''simple docstring'''





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							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=13   ,_SCREAMING_SNAKE_CASE=30   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=5   ,_SCREAMING_SNAKE_CASE=4   ,_SCREAMING_SNAKE_CASE=37   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=0.6   ,_SCREAMING_SNAKE_CASE=None   ,)				->     str:
          _snake_case				= parent
          _snake_case				= batch_size
          _snake_case				= image_size
          _snake_case				= patch_size
          _snake_case				= num_channels
          _snake_case				= is_training
          _snake_case				= use_labels
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= type_sequence_label_size
          _snake_case				= initializer_range
          _snake_case				= mask_ratio
          _snake_case				= scope

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






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

          _snake_case				= self.get_config()

          return config, pixel_values, labels






     def 							_lowercase      (					self       )				->     List[str]:
          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=_SCREAMING_SNAKE_CASE   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Any:
          _snake_case				= ViTMAEModel(config=_SCREAMING_SNAKE_CASE       )
          model.to(_SCREAMING_SNAKE_CASE       )
          model.eval()
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE       )
          model.to(_SCREAMING_SNAKE_CASE       )
          model.eval()
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          _snake_case				= (self.image_size // self.patch_size) ** 2
          _snake_case				= self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)       )

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






     def 							_lowercase      (					self       )				->     List[str]:
          _snake_case				= self.prepare_config_and_inputs()
          _snake_case				,   _snake_case				,   _snake_case				= config_and_inputs
          _snake_case				= {"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

     SCREAMING_SNAKE_CASE_				:       str       							=		False
     SCREAMING_SNAKE_CASE_				:       Tuple       							=		False
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		False
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		False






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= ViTMAEModelTester(self       )
          _snake_case				= ConfigTester(self   ,config_class=_SCREAMING_SNAKE_CASE   ,has_text_modality=_SCREAMING_SNAKE_CASE   ,hidden_size=37       )






     def 							_lowercase      (					self       )				->     Dict:
          self.config_tester.run_common_tests()






     @unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
     def 							_lowercase      (					self       )				->     List[Any]:
          pass






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

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






     def 							_lowercase      (					self       )				->     Dict:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

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

               _snake_case				= ["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     str:
          # make masks reproducible
          np.random.seed(2       )

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

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

          super().check_pt_tf_models(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                    # Make sure we don't have nans
                    _snake_case				= after_outputs[0].cpu().numpy()
                    _snake_case				= 0
                    _snake_case				= np.amax(np.abs(out_a - out_a       )       )
                    self.assertLessEqual(_SCREAMING_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 							_lowercase      (					self       )				->     Optional[int]:
          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 							_lowercase      (					self       )				->     List[Any]:
          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 							_lowercase      (					self       )				->     Tuple:
          pass






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






     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
     def 							_lowercase      (					self       )				->     int:
          pass






     @slow
     def 							_lowercase      (					self       )				->     str:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case				= ViTMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE       )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )







def       __a      (  )		->					Optional[int]:

     """simple docstring"""




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





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






     @cached_property
     def 							_lowercase      (					self       )				->     Tuple:
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None






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

          _snake_case				= ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       ).to(_SCREAMING_SNAKE_CASE       )

          _snake_case				= self.default_image_processor
          _snake_case				= prepare_img()
          _snake_case				= image_processor(images=_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       ).to(_SCREAMING_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)
          _snake_case				= ViTMAEConfig()
          _snake_case				= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
          _snake_case				= np.random.uniform(size=(1, num_patches)       )

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

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

          _snake_case				= 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(_SCREAMING_SNAKE_CASE       )   ,atol=1e-4       )       )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


