





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__							:


      def __init__(self			: Dict							,     a__			: Union[str, Any]							,     a__			: Dict=13							,     a__			: Optional[Any]=30							,     a__			: Optional[Any]=2							,     a__			: Tuple=3							,     a__			: List[Any]=True							,     a__			: Optional[Any]=True							,     a__			: List[Any]=32							,     a__			: int=5							,     a__			: Union[str, Any]=4							,     a__			: Union[str, Any]=37							,     a__			: Optional[int]="gelu"							,     a__			: Dict=0.1							,     a__			: List[str]=0.1							,     a__			: Optional[Any]=10							,     a__			: List[str]=0.0_2							,     a__			: int=3							,     a__			: Any=0.6							,     a__			: Dict=None							,     ):





         """simple docstring"""



         __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   a					(self			: Tuple ):





         """simple docstring"""



         __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   a					(self			: Dict ):





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


      def   a					(self			: Optional[int]							,     a__			: Any							,     a__			: Union[str, Any]							,     a__			: str ):





         """simple docstring"""



         __snake_case							=  ViTMAEModel(config=__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         __snake_case							=  model(__UpperCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size) )


      def   a					(self			: List[Any]							,     a__			: Optional[int]							,     a__			: List[str]							,     a__			: Dict ):





         """simple docstring"""



         __snake_case							=  ViTMAEForPreTraining(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         __snake_case							=  model(__UpperCAmelCase )
         __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(__UpperCAmelCase )
         model.to(__UpperCAmelCase )
         model.eval()
         __snake_case							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         __snake_case							=  model(__UpperCAmelCase )
         __snake_case							=  self.patch_size**2
         self.parent.assertEqual(result.logits.shape							,     (self.batch_size, num_patches, expected_num_channels) )


      def   a					(self			: str ):





         """simple docstring"""



         __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 						SCREAMING_SNAKE_CASE__							(			A__		, A__		, unittest.TestCase    ):
      A_		:							Optional[int]				=       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      A_		:							str				=       {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

      A_		:							Tuple				=       False
      A_		:							List[str]				=       False
      A_		:							str				=       False
      A_		:							List[Any]				=       False


      def   a					(self			: Dict ):





         """simple docstring"""



         __snake_case							=  ViTMAEModelTester(self )
         __snake_case							=  ConfigTester(self							,     config_class=__UpperCAmelCase							,     has_text_modality=__UpperCAmelCase							,     hidden_size=37 )


      def   a					(self			: Union[str, Any] ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      @unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
      def   a					(self			: List[Any] ):





         """simple docstring"""



         pass


      def   a					(self			: List[str] ):





         """simple docstring"""



         __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(__UpperCAmelCase )
            self.assertIsInstance(model.get_input_embeddings()							,     (nn.Module) )
            __snake_case							=  model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__UpperCAmelCase							,     nn.Linear ) )


      def   a					(self			: Optional[Any] ):





         """simple docstring"""



         __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(__UpperCAmelCase )
            __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]							,     __UpperCAmelCase )


      def   a					(self			: Any ):





         """simple docstring"""



         __snake_case							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase )


      def   a					(self			: Optional[int] ):





         """simple docstring"""



         __snake_case							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )


      def   a					(self			: int							,     a__			: int							,     a__			: List[Any]							,     a__			: Optional[Any] ):





         """simple docstring"""



         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(__UpperCAmelCase )

         # 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(__UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase )


      def   a					(self			: List[Any] ):





         """simple docstring"""



         __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(__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            # make random mask reproducible
            torch.manual_seed(2 )
            with torch.no_grad():
               __snake_case							=  model(**self._prepare_for_class(__UpperCAmelCase							,     __UpperCAmelCase ) )

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

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

               # 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(__UpperCAmelCase							,     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   a					(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   a					(self			: str ):





         """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   a					(self			: Optional[Any] ):





         """simple docstring"""



         pass


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





         """simple docstring"""



         pass


      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def   a					(self			: Union[str, Any] ):





         """simple docstring"""



         pass


      @slow
      def   a					(self			: str ):





         """simple docstring"""



         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							=  ViTMAEModel.from_pretrained(__UpperCAmelCase )
            self.assertIsNotNone(__UpperCAmelCase )






def lowerCamelCase__       (				) ->							int:
   __snake_case							=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
   return image







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


      @cached_property
      def   a					(self			: Any ):





         """simple docstring"""



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


      @slow
      def   a					(self			: Optional[Any] ):





         """simple docstring"""



         np.random.seed(2 )

         __snake_case							=  ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(__UpperCAmelCase )

         __snake_case							=  self.default_image_processor
         __snake_case							=  prepare_img()
         __snake_case							=  image_processor(images=__UpperCAmelCase							,     return_tensors='''pt''' ).to(__UpperCAmelCase )

         # 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(**__UpperCAmelCase							,     noise=torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase ) )

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

         __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(__UpperCAmelCase )							,     atol=1E-4 ) )




"""simple docstring"""




A_				:      Any           =			"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich


