


"""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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=30 ,     lowerCamelCase__=2 ,     lowerCamelCase__=3 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=32 ,     lowerCamelCase__=5 ,     lowerCamelCase__=4 ,     lowerCamelCase__=37 ,     lowerCamelCase__="gelu" ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=10 ,     lowerCamelCase__=0.02 ,     lowerCamelCase__=3 ,     lowerCamelCase__=0.6 ,     lowerCamelCase__=None ,     )      ->							Any:
    lowercase__       :       List[Any]				=						parent
    lowercase__       :       Any				=						batch_size
    lowercase__       :       Union[str, Any]				=						image_size
    lowercase__       :       List[Any]				=						patch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       List[Any]				=						is_training
    lowercase__       :       List[Any]				=						use_labels
    lowercase__       :       Tuple				=						hidden_size
    lowercase__       :       int				=						num_hidden_layers
    lowercase__       :       Union[str, Any]				=						num_attention_heads
    lowercase__       :       Optional[int]				=						intermediate_size
    lowercase__       :       str				=						hidden_act
    lowercase__       :       List[Any]				=						hidden_dropout_prob
    lowercase__       :       Union[str, Any]				=						attention_probs_dropout_prob
    lowercase__       :       Tuple				=						type_sequence_label_size
    lowercase__       :       Optional[Any]				=						initializer_range
    lowercase__       :       Optional[int]				=						mask_ratio
    lowercase__       :       Any				=						scope

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


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

    lowercase__       :       Dict				=						None
    if self.use_labels:
     lowercase__       :       Tuple				=						ids_tensor([self.batch_size] ,     self.type_sequence_label_size						)

    lowercase__       :       List[str]				=						self.get_config()

    return config, pixel_values, labels


   def 					UpperCAmelCase__(						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=lowerCamelCase__ ,     initializer_range=self.initializer_range ,     mask_ratio=self.mask_ratio ,     )


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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Union[str, Any]				=						ViTMAEForPreTraining(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       List[Any]				=						model(lowerCamelCase__						)
    lowercase__       :       str				=						(self.image_size // self.patch_size) ** 2
    lowercase__       :       int				=						self.patch_size**2 * self.num_channels
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, num_patches, expected_num_channels)						)

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







   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Optional[Any]				=						self.prepare_config_and_inputs()
    lowercase__	,       lowercase__	,       lowercase__       :       Optional[int]				=						config_and_inputs
    lowercase__       :       List[Any]				=						{"""pixel_values""": pixel_values}
    return config, inputs_dict

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       int									=    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
   _a   :       Optional[Any]									=    {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

   _a   :       List[Any]									=    False
   _a   :       Optional[Any]									=    False
   _a   :       int									=    False
   _a   :       Union[str, Any]									=    False


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Tuple				=						ViTMAEModelTester(self						)
    lowercase__       :       List[Any]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     has_text_modality=lowerCamelCase__ ,     hidden_size=37						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    self.config_tester.run_common_tests()


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


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

    for model_class in self.all_model_classes:
     lowercase__       :       Tuple				=						model_class(lowerCamelCase__						)
     self.assertIsInstance(model.get_input_embeddings() ,     (nn.Module)						)
     lowercase__       :       Optional[Any]				=						model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(lowerCamelCase__ ,     nn.Linear						)						)


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

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

     lowercase__       :       Optional[Any]				=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Union[str, Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Union[str, Any]:
    # make masks reproducible
    np.random.seed(2						)

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

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

    super().check_pt_tf_models(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__	,       lowercase__       :       Tuple				=						self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__       :       Union[str, Any]				=						outputs[0].cpu().numpy()
     lowercase__       :       List[str]				=						0

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

      # Make sure we don't have nans
      lowercase__       :       int				=						after_outputs[0].cpu().numpy()
      lowercase__       :       List[Any]				=						0
      lowercase__       :       List[str]				=						np.amax(np.abs(out_a - out_a						)						)
      self.assertLessEqual(lowerCamelCase__ ,     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 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


   @unittest.skip(
       reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""						)
   def 					UpperCAmelCase__(						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 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    pass


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


   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    pass







   @slow
   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Dict				=						ViTMAEModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)


def    _lowerCamelCase   (     ):
 lowercase__       :       str				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
 return image






@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @cached_property
   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""						) if is_vision_available() else None







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

    lowercase__       :       List[str]				=						ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						).to(lowerCamelCase__						)

    lowercase__       :       List[str]				=						self.default_image_processor
    lowercase__       :       List[Any]				=						prepare_img()
    lowercase__       :       List[Any]				=						image_processor(images=lowerCamelCase__ ,     return_tensors="""pt"""						).to(lowerCamelCase__						)

    # 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)
    lowercase__       :       str				=						ViTMAEConfig()
    lowercase__       :       Optional[int]				=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
    lowercase__       :       Tuple				=						np.random.uniform(size=(1, num_patches)						)

    # forward pass
    with torch.no_grad():
     lowercase__       :       List[str]				=						model(**lowerCamelCase__ ,     noise=torch.from_numpy(lowerCamelCase__						).to(device=lowerCamelCase__						)						)

    # verify the logits
    lowercase__       :       List[Any]				=						torch.Size((1, 196, 768)						)
    self.assertEqual(outputs.logits.shape ,     lowerCamelCase__						)

    lowercase__       :       int				=						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(lowerCamelCase__						) ,     atol=1E-4						)						)



"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)