





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    lowerCAmelCase							:
 def __init__(      self  :      Tuple			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      List[Any]=13			,     UpperCAmelCase  :      Dict=30			,     UpperCAmelCase  :      Dict=2			,     UpperCAmelCase  :      int=3			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      Dict=32			,     UpperCAmelCase  :      List[Any]=5			,     UpperCAmelCase  :      str=4			,     UpperCAmelCase  :      List[Any]=37			,     UpperCAmelCase  :      Dict="gelu"			,     UpperCAmelCase  :      Optional[Any]=0.1			,     UpperCAmelCase  :      Tuple=0.1			,     UpperCAmelCase  :      List[Any]=10			,     UpperCAmelCase  :      List[Any]=0.0_2			,     UpperCAmelCase  :      Dict=3			,     UpperCAmelCase  :      int=0.6			,     UpperCAmelCase  :      Union[str, Any]=None			,     )			->	Any:
     lowerCamelCase__    :      Tuple					 =		parent
     lowerCamelCase__    :      Tuple					 =		batch_size
     lowerCamelCase__    :      Any					 =		image_size
     lowerCamelCase__    :      int					 =		patch_size
     lowerCamelCase__    :      Dict					 =		num_channels
     lowerCamelCase__    :      Union[str, Any]					 =		is_training
     lowerCamelCase__    :      Union[str, Any]					 =		use_labels
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_size
     lowerCamelCase__    :      str					 =		num_hidden_layers
     lowerCamelCase__    :      str					 =		num_attention_heads
     lowerCamelCase__    :      Tuple					 =		intermediate_size
     lowerCamelCase__    :      str					 =		hidden_act
     lowerCamelCase__    :      Optional[Any]					 =		hidden_dropout_prob
     lowerCamelCase__    :      Tuple					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Any					 =		type_sequence_label_size
     lowerCamelCase__    :      Any					 =		initializer_range
     lowerCamelCase__    :      str					 =		mask_ratio
     lowerCamelCase__    :      Tuple					 =		scope

     # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
     # (we add 1 for the [CLS] token)
     lowerCamelCase__    :      Optional[int]					 =		(image_size // patch_size) ** 2
     lowerCamelCase__    :      Dict					 =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)
 def      A_	(      self  :      List[Any]	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     lowerCamelCase__    :      List[str]					 =		None
     if self.use_labels:
         lowerCamelCase__    :      int					 =		ids_tensor([self.batch_size]			,     self.type_sequence_label_size	)

     lowerCamelCase__    :      List[Any]					 =		self.get_config()

     return config, pixel_values, labels
 def      A_	(      self  :      str	)			->	Any:
     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[Any]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      int	)			->	List[Any]:
     lowerCamelCase__    :      Optional[Any]					 =		ViTMAEModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Any					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]	)			->	Any:
     lowerCamelCase__    :      List[Any]					 =		ViTMAEForPreTraining(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      List[Any]					 =		model(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		(self.image_size // self.patch_size) ** 2
     lowerCamelCase__    :      str					 =		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__    :      str					 =		1
     lowerCamelCase__    :      Union[str, Any]					 =		ViTMAEForPreTraining(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      List[str]					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.patch_size**2
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, num_patches, expected_num_channels)	)







 def      A_	(      self  :      Dict	)			->	Union[str, Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		self.prepare_config_and_inputs()
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		config_and_inputs
     lowerCamelCase__    :      List[Any]					 =		{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 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      A_	(      self  :      List[str]	)			->	List[str]:
     lowerCamelCase__    :      List[Any]					 =		ViTMAEModelTester(self	)
     lowerCamelCase__    :      Optional[Any]					 =		ConfigTester(self			,     config_class=UpperCAmelCase			,     has_text_modality=UpperCAmelCase			,     hidden_size=37	)
 def      A_	(      self  :      str	)			->	Union[str, Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
 def      A_	(      self  :      Tuple	)			->	Union[str, Any]:
     pass
 def      A_	(      self  :      List[str]	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__    :      Optional[Any]					 =		model_class(UpperCAmelCase	)
         self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)	)
         lowerCamelCase__    :      int					 =		model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase			,     nn.Linear	)	)
 def      A_	(      self  :      Optional[int]	)			->	Optional[int]:
     lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__    :      str					 =		['pixel_values']
         self.assertListEqual(arg_names[:1]			,     UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	Optional[int]:
     lowerCamelCase__    :      str					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Any	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      int	)			->	str:
     # make masks reproducible
     np.random.seed(2	)

     lowerCamelCase__    :      List[str]					 =		int((pt_model.config.image_size // pt_model.config.patch_size) ** 2	)
     lowerCamelCase__    :      Any					 =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
     lowerCamelCase__    :      List[str]					 =		torch.from_numpy(UpperCAmelCase	)

     # Add `noise` argument.
     # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
     lowerCamelCase__    :      List[Any]					 =		pt_noise

     super().check_pt_tf_models(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
 def      A_	(      self  :      List[Any]	)			->	Optional[int]:
     lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__    :      Union[str, Any]					 =		outputs[0].cpu().numpy()
         lowerCamelCase__    :      int					 =		0

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

             # Make sure we don't have nans
             lowerCamelCase__    :      str					 =		after_outputs[0].cpu().numpy()
             lowerCamelCase__    :      List[Any]					 =		0
             lowerCamelCase__    :      List[Any]					 =		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\n    to get deterministic results.'	)
 def      A_	(      self  :      int	)			->	Union[str, 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      A_	(      self  :      List[str]	)			->	str:
     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      A_	(      self  :      List[str]	)			->	Any:
     pass
 @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
 def      A_	(      self  :      Tuple	)			->	Tuple:
     pass
 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
 def      A_	(      self  :      List[Any]	)			->	Dict:
     pass







 @slow
 def      A_	(      self  :      int	)			->	Union[str, Any]:
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      Optional[Any]					 =		ViTMAEModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)


def        SCREAMING_SNAKE_CASE      (       )			->     List[str]:
    lowerCamelCase__    :      Tuple					 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image


@require_torch
@require_vision
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      Dict	)			->	List[str]:
     return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None







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

     lowerCamelCase__    :      str					 =		ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	).to(UpperCAmelCase	)

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

     # forward pass
     with torch.no_grad():
         lowerCamelCase__    :      Any					 =		model(**UpperCAmelCase			,     noise=torch.from_numpy(UpperCAmelCase	).to(device=UpperCAmelCase	)	)

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

     lowerCamelCase__    :      str					 =		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	)	)












from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     list[tuple[int, int]]:
    lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		position
    lowerCamelCase__    :      Optional[Any]					 =		[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    lowerCamelCase__    :      Dict					 =		[]

    for position in positions:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(_UpperCAmelCase )

    return permissible_positions


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:
    return not any(elem == 0 for row in board for elem in row )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     bool:

    if is_complete(_UpperCAmelCase ):
        return True

    for position in get_valid_pos(_UpperCAmelCase						,	len(_UpperCAmelCase ) ):
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position

        if board[y][x] == 0:
            lowerCamelCase__    :      List[Any]					 =		curr + 1
            if open_knight_tour_helper(_UpperCAmelCase						,	_UpperCAmelCase						,	curr + 1 ):
                return True
            lowerCamelCase__    :      Optional[Any]					 =		0

    return False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[list[int]]:
    lowerCamelCase__    :      Any					 =		[[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]

    for i in range(_UpperCAmelCase ):
        for j in range(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		1
            if open_knight_tour_helper(_UpperCAmelCase						,	(i, j)						,	1 ):
                return board
            lowerCamelCase__    :      Optional[Any]					 =		0

    lowerCamelCase__    :      Any					 =		F"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(_UpperCAmelCase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





