






'''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							:						int ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict=1_3 ,      lowercase							:						Optional[int]=3_0 ,      lowercase							:						Optional[Any]=2 ,      lowercase							:						Optional[int]=3 ,      lowercase							:						str=True ,      lowercase							:						Optional[int]=True ,      lowercase							:						Union[str, Any]=3_2 ,      lowercase							:						Dict=5 ,      lowercase							:						str=4 ,      lowercase							:						Tuple=3_7 ,      lowercase							:						List[str]="gelu" ,      lowercase							:						Optional[int]=0.1 ,      lowercase							:						int=0.1 ,      lowercase							:						Optional[Any]=1_0 ,      lowercase							:						Union[str, Any]=0.0_2 ,      lowercase							:						Optional[Any]=3 ,      lowercase							:						str=0.6 ,      lowercase							:						str=None ,      )							->    int:

									'''simple docstring'''







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

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






			def 				A      (   self							:						str			)							->    Optional[Any]:

									'''simple docstring'''







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

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

									UpperCamelCase__         = self.get_config()

									return config, pixel_values, labels






			def 				A      (   self							:						Optional[Any]			)							->    List[Any]:

									'''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 				A      (   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						List[str]			)							->    Dict:

									'''simple docstring'''







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






			def 				A      (   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Dict ,      lowercase							:						Any			)							->    List[str]:

									'''simple docstring'''







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







			def 				A      (   self							:						List[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = config_and_inputs
									UpperCamelCase__         = {"""pixel_values""": pixel_values}
									return config, inputs_dict
@require_torch
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			__a							:							Union[str, Any]					     =					{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

			__a							:							Any					     =					False
			__a							:							str					     =					False
			__a							:							List[str]					     =					False
			__a							:							int					     =					False






			def 				A      (   self							:						int			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = ViTMAEModelTester(self			)
									UpperCamelCase__         = ConfigTester(self ,      config_class=lowercase ,      has_text_modality=lowercase ,      hidden_size=3_7			)






			def 				A      (   self							:						Dict			)							->    Optional[int]:

									'''simple docstring'''







									self.config_tester.run_common_tests()






			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""			)
			def 				A      (   self							:						Dict			)							->    Optional[int]:

									'''simple docstring'''







									pass






			def 				A      (   self							:						str			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.model_tester.prepare_config_and_inputs_for_common()

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






			def 				A      (   self							:						Optional[int]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.model_tester.prepare_config_and_inputs_for_common()

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

															UpperCamelCase__         = ["""pixel_values"""]
															self.assertListEqual(arg_names[:1] ,      lowercase			)






			def 				A      (   self							:						str			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase			)






			def 				A      (   self							:						List[str]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowercase			)






			def 				A      (   self							:						List[str] ,      lowercase							:						Union[str, Any] ,      lowercase							:						str ,      lowercase							:						str			)							->    Optional[Any]:

									'''simple docstring'''







									np.random.seed(2			)

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

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

									super().check_pt_tf_models(lowercase ,      lowercase ,      lowercase			)






			def 				A      (   self							:						Tuple			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																					# Make sure we don't have nans
																					UpperCamelCase__         = after_outputs[0].cpu().numpy()
																					UpperCamelCase__         = 0
																					UpperCamelCase__         = 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
    to get deterministic results."""			)
			def 				A      (   self							:						Optional[int]			)							->    Dict:

									'''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							:						Union[str, Any]			)							->    Union[str, Any]:

									'''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							:						List[Any]			)							->    List[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							:						List[str]			)							->    List[Any]:

									'''simple docstring'''







									pass






			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
			def 				A      (   self							:						int			)							->    Tuple:

									'''simple docstring'''







									pass







			@slow
			def 				A      (   self							:						Tuple			)							->    Optional[Any]:

									'''simple docstring'''







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







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = 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 				A      (   self							:						List[Any]			)							->    Optional[int]:

									'''simple docstring'''







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







			@slow
			def 				A      (   self							:						Optional[int]			)							->    str:

									'''simple docstring'''







									np.random.seed(2			)

									UpperCamelCase__         = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""			).to(lowercase			)

									UpperCamelCase__         = self.default_image_processor
									UpperCamelCase__         = prepare_img()
									UpperCamelCase__         = 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)
									UpperCamelCase__         = ViTMAEConfig()
									UpperCamelCase__         = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
									UpperCamelCase__         = np.random.uniform(size=(1, num_patches)			)

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

									# verify the logits
									UpperCamelCase__         = torch.Size((1, 1_9_6, 7_6_8)			)
									self.assertEqual(outputs.logits.shape ,      lowercase			)

									UpperCamelCase__         = 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(lowercase			) ,      atol=1e-4			)			)








'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["pixel_values"]






			def __init__(   self							:						str ,      lowercase							:						bool = True ,      lowercase							:						int = 3_2 ,      lowercase							:						List[Any]=PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									UpperCamelCase__         = do_resize
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = size_divisor
									UpperCamelCase__         = resample
									super().__init__(**lowercase			)






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						int ,      lowercase							:						List[Any] ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						Any			)							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = get_image_size(lowercase			)
									# Rounds the height and width down to the closest multiple of size_divisor
									UpperCamelCase__         = height // size_divisor * size_divisor
									UpperCamelCase__         = width // size_divisor * size_divisor
									UpperCamelCase__         = resize(lowercase ,      (new_h, new_w) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
									return image






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						float ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						List[str]			)							->    np.ndarray:

									'''simple docstring'''







									return rescale(image=lowercase ,      scale=lowercase ,      data_format=lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[TensorType, str]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						str ,      )							->    BatchFeature:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = size_divisor if size_divisor is not None else self.size_divisor
									UpperCamelCase__         = resample if resample is not None else self.resample

									if do_resize and size_divisor is None:
															raise ValueError("""size_divisor is required for resizing"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

									if not valid_images(lowercase			):
															raise ValueError("""Invalid image(s)"""			)

									# All transformations expect numpy arrays.
									UpperCamelCase__         = [to_numpy_array(lowercase			) for img in images]

									if do_resize:
															UpperCamelCase__         = [self.resize(lowercase ,      size_divisor=lowercase ,      resample=lowercase			) for image in images]

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      scale=1 / 2_5_5			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)
