'''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 					__magic_name__		:







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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Optional[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowercase_      :    Union[str, Any]       		= None
										if self.use_labels:
																	lowercase_      :    List[str]       		= ids_tensor([self.batch_size]       ,    self.type_sequence_label_size					)

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

										return config, pixel_values, labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						Tuple       ,    lowercase_       :						Any       ,    lowercase_       :						str					):
										lowercase_      :    Optional[Any]       		= ViTMAEModel(config=lowercase_					)
										model.to(lowercase_					)
										model.eval()
										lowercase_      :    List[str]       		= model(lowercase_					)
										self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)					)







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







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





@require_torch
class 					__magic_name__		(					_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 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    str       		= ViTMAEModelTester(self					)
										lowercase_      :    Any       		= ConfigTester(self       ,    config_class=lowercase_       ,    has_text_modality=lowercase_       ,    hidden_size=37					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										self.config_tester.run_common_tests()







			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_		,		lowercase_      :    str       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    Optional[int]       		= model_class(lowercase_					)
																	self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)					)
																	lowercase_      :    Dict       		= model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(lowercase_       ,    nn.Linear					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_		,		lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_      :    Optional[int]       		= ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						List[str]       ,    lowercase_       :						str					):
										# make masks reproducible
										np.random.seed(2					)

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

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

										super().check_pt_tf_models(lowercase_       ,    lowercase_       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_		,		lowercase_      :    Optional[Any]       		= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																								# Make sure we don't have nans
																								lowercase_      :    int       		= after_outputs[0].cpu().numpy()
																								lowercase_      :    Optional[int]       		= 0
																								lowercase_      :    List[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										pass







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







			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										pass







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







			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										pass







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :    str       		= ViTMAEModel.from_pretrained(lowercase_					)
																	self.assertIsNotNone(lowercase_					)




def 							lowerCamelCase			(   )    ->   Optional[int]:
							lowercase_      :    Optional[Any]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





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







			@cached_property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										# make random mask reproducible across the PT and TF model
										np.random.seed(2					)

										lowercase_      :    Dict       		= ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					).to(lowercase_					)

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

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

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

										lowercase_      :    str       		= 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(lowercase_					)       ,    atol=1E-4					)					)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





