





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 :  Any      ,   lowercase__ :  Tuple      ,   lowercase__ :  Tuple=13      ,   lowercase__ :  Optional[int]=30      ,   lowercase__ :  List[str]=2      ,   lowercase__ :  Dict=3      ,   lowercase__ :  Optional[Any]=True      ,   lowercase__ :  List[str]=True      ,   lowercase__ :  str=32      ,   lowercase__ :  Union[str, Any]=5      ,   lowercase__ :  Optional[int]=4      ,   lowercase__ :  Union[str, Any]=37      ,   lowercase__ :  Tuple="gelu"      ,   lowercase__ :  List[str]=0.1      ,   lowercase__ :  Optional[int]=0.1      ,   lowercase__ :  str=10      ,   lowercase__ :  int=0.02      ,   lowercase__ :  Union[str, Any]=3      ,   lowercase__ :  int=0.6      ,   lowercase__ :  Optional[Any]=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = image_size
        lowerCAmelCase__					     = patch_size
        lowerCAmelCase__					     = num_channels
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = type_sequence_label_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = mask_ratio
        lowerCAmelCase__					     = scope

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




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



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

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

        lowerCAmelCase__					     = self.get_config()

        return config, pixel_values, labels




 def 		__snake_case						(      self :  str):







        '''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=lowercase__      ,   initializer_range=self.initializer_range      ,   mask_ratio=self.mask_ratio      ,   )




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Any      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Any):







        '''simple docstring'''



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

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




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



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






@require_torch
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE	,					unittest.TestCase						):






 '''simple docstring'''






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

 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = ViTMAEModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__      ,   has_text_modality=lowercase__      ,   hidden_size=37)




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







        '''simple docstring'''



        self.config_tester.run_common_tests()




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







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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

               lowerCAmelCase__					     = ['pixel_values']
               self.assertListEqual(arg_names[:1]      ,   lowercase__)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowercase__)




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Dict):







        '''simple docstring'''



        np.random.seed(2)

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

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

        super().check_pt_tf_models(lowercase__      ,   lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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







        '''simple docstring'''



        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 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        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 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        pass




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







        '''simple docstring'''



        pass




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







        '''simple docstring'''



        pass




 @slow
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



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






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image



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






 '''simple docstring'''






 @cached_property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



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




 @slow
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        np.random.seed(2)

        lowerCAmelCase__					     = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(lowercase__)

        lowerCAmelCase__					     = self.default_image_processor
        lowerCAmelCase__					     = prepare_img()
        lowerCAmelCase__					     = image_processor(images=lowercase__      ,   return_tensors='pt').to(lowercase__)

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

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

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

        lowerCAmelCase__					     = torch.tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]])

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

from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  pyspark.sql.DataFrame      ,   lowercase__ :  Optional[NamedSplit] = None      ,   lowercase__ :  Optional[Features] = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = False      ,   lowercase__ :  str = None      ,   lowercase__ :  bool = True      ,   lowercase__ :  str = "arrow"      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



        super().__init__(
            split=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   keep_in_memory=lowercase__      ,   streaming=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = load_from_cache_file
        lowerCAmelCase__					     = file_format
        lowerCAmelCase__					     = Spark(
            df=lowercase__      ,   features=lowercase__      ,   cache_dir=lowercase__      ,   working_dir=lowercase__      ,   **lowercase__      ,   )




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        if self.streaming:
               return self.builder.as_streaming_dataset(split=self.split)
        lowerCAmelCase__					     = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowercase__      ,   file_format=self._file_format      ,   )
        return self.builder.as_dataset(split=self.split)
