




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:






    """simple docstring"""





    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=0.02  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=0.6  ,_SCREAMING_SNAKE_CASE=None  ,)					->						Any:
         UpperCAmelCase_     :				str  =     parent
         UpperCAmelCase_     :				Optional[int]  =     batch_size
         UpperCAmelCase_     :				Optional[int]  =     image_size
         UpperCAmelCase_     :				Tuple  =     patch_size
         UpperCAmelCase_     :				Dict  =     num_channels
         UpperCAmelCase_     :				List[Any]  =     is_training
         UpperCAmelCase_     :				str  =     use_labels
         UpperCAmelCase_     :				Optional[int]  =     hidden_size
         UpperCAmelCase_     :				Optional[int]  =     num_hidden_layers
         UpperCAmelCase_     :				Optional[Any]  =     num_attention_heads
         UpperCAmelCase_     :				List[Any]  =     intermediate_size
         UpperCAmelCase_     :				Dict  =     hidden_act
         UpperCAmelCase_     :				str  =     hidden_dropout_prob
         UpperCAmelCase_     :				Dict  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				Dict  =     type_sequence_label_size
         UpperCAmelCase_     :				Union[str, Any]  =     initializer_range
         UpperCAmelCase_     :				Dict  =     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_     :				Union[str, Any]  =     (image_size // patch_size) ** 2
         UpperCAmelCase_     :				str  =     int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

         UpperCAmelCase_     :				Optional[Any]  =     None
         if self.use_labels:
              UpperCAmelCase_     :				Tuple  =     ids_tensor([self.batch_size]  ,self.type_sequence_label_size    )

         UpperCAmelCase_     :				int  =     self.get_config()

         return config, pixel_values, labels





    def 				a__ ( 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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Any  =     ViTMAEModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Any  =     model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				str  =     ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Dict  =     model(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     (self.image_size // self.patch_size) ** 2
         UpperCAmelCase_     :				Dict  =     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_     :				str  =     1
         UpperCAmelCase_     :				Optional[Any]  =     ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				int  =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         UpperCAmelCase_     :				Dict  =     model(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     self.patch_size**2
         self.parent.assertEqual(result.logits.shape  ,(self.batch_size, num_patches, expected_num_channels)    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Tuple  =     self.prepare_config_and_inputs()
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     config_and_inputs
         UpperCAmelCase_     :				List[str]  =     {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





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

    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False
    lowerCAmelCase         = False





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Optional[Any]  =     ViTMAEModelTester(self    )
         UpperCAmelCase_     :				List[str]  =     ConfigTester(self  ,config_class=_SCREAMING_SNAKE_CASE  ,has_text_modality=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





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





    @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''    )
    def 				a__ ( self    )					->						Dict:
         pass





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     self.model_tester.prepare_config_and_inputs_for_common()

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





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_     :				List[Any]  =     ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				int  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE    )





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

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

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





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				List[str]  =     model_class(_SCREAMING_SNAKE_CASE    )
              model.to(_SCREAMING_SNAKE_CASE    )
              model.eval()
              # make random mask reproducible
              torch.manual_seed(2    )
              with torch.no_grad():
                   UpperCAmelCase_     :				Dict  =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )

              UpperCAmelCase_     :				List[str]  =     outputs[0].cpu().numpy()
              UpperCAmelCase_     :				str  =     0

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

                   # Make sure we don't have nans
                   UpperCAmelCase_     :				Tuple  =     after_outputs[0].cpu().numpy()
                   UpperCAmelCase_     :				Optional[int]  =     0
                   UpperCAmelCase_     :				Tuple  =     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
    to get deterministic results.'''    )
    def 				a__ ( self    )					->						Union[str, Any]:
         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:
         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    )					->						Dict:
         pass





    @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''    )
    def 				a__ ( self    )					->						Union[str, Any]:
         pass





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





    @slow
    def 				a__ ( self    )					->						Dict:
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_     :				Tuple  =     ViTMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )







def lowerCamelCase__  (     ):

     '''simple docstring'''


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



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






    """simple docstring"""





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





    @slow
    def 				a__ ( self    )					->						List[str]:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2    )

         UpperCAmelCase_     :				List[str]  =     ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''    ).to(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     self.default_image_processor
         UpperCAmelCase_     :				str  =     prepare_img()
         UpperCAmelCase_     :				Union[str, Any]  =     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)
         UpperCAmelCase_     :				Any  =     ViTMAEConfig()
         UpperCAmelCase_     :				Any  =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
         UpperCAmelCase_     :				Optional[Any]  =     np.random.uniform(size=(1, num_patches)    )

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

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

         UpperCAmelCase_     :				Optional[Any]  =     torch.tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]    )

         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  ,expected_slice.to(_SCREAMING_SNAKE_CASE    )  ,atol=1e-4    )    )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a							     =					{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)