






'''simple docstring'''







import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Tuple:
							try:
														lowerCamelCase__      :			Dict 						= os.environ[key]
							except KeyError:
														# KEY isn't set, default to `default`.
														lowerCamelCase__      :			Union[str, Any] 						= default
							else:
														# KEY is set, convert it to True or False.
														try:
																					lowerCamelCase__      :			Optional[Any] 						= strtobool(UpperCamelCase   )
														except ValueError:
																					# More values are supported, but let's keep the message simple.
																					raise ValueError(f'''If set, {key} must be yes or no.'''   )
							return _value


_A			:    Dict					  =parse_flag_from_env('''RUN_SLOW''', default=False)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Tuple:
							return unittest.skip("""Test was skipped"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							return unittest.skipUnless(_run_slow_tests       ,    """test is slow"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							return unittest.skipUnless(not torch.cuda.is_available()       ,    """test requires only a CPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							return unittest.skipUnless(torch.cuda.is_available()       ,    """test requires a GPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Dict:
							return unittest.skipUnless(is_xpu_available()       ,    """test requires a XPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Dict:
							return unittest.skipUnless(is_mps_available()       ,    """test requires a `mps` backend support in `torch`"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Tuple:
							return unittest.skipUnless(
							    is_transformers_available() and is_datasets_available()       ,    """test requires the Hugging Face suite"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[int]:
							return unittest.skipUnless(is_bnb_available()       ,    """test requires the bitsandbytes library"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							return unittest.skipUnless(is_tpu_available()       ,    """test requires TPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							return unittest.skipUnless(torch.cuda.device_count() == 1       ,    """test requires a GPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							return unittest.skipUnless(torch.xpu.device_count() == 1       ,    """test requires a XPU"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[Any]:
							return unittest.skipUnless(torch.cuda.device_count() > 1       ,    """test requires multiple GPUs"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							return unittest.skipUnless(torch.xpu.device_count() > 1       ,    """test requires multiple XPUs"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							return unittest.skipUnless(is_safetensors_available()       ,    """test requires safetensors"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							return unittest.skipUnless(is_deepspeed_available()       ,    """test requires DeepSpeed"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> int:
							return unittest.skipUnless(is_torch_version(""">="""       ,    """1.12.0"""   )       ,    """test requires torch version >= 1.12.0"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase=None       ,    UpperCamelCase=None   )   -> List[Any]:
							if test_case is None:
														return partial(UpperCamelCase       ,    version=UpperCamelCase   )
							return unittest.skipUnless(is_torch_version(""">="""       ,    UpperCamelCase   )       ,    f'''test requires torch version >= {version}'''   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[Any]:
							return unittest.skipUnless(is_tensorboard_available()       ,    """test requires Tensorboard"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							return unittest.skipUnless(is_wandb_available()       ,    """test requires wandb"""   )(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[Any]:
							return unittest.skipUnless(is_comet_ml_available()       ,    """test requires comet_ml"""   )(UpperCamelCase   )


_A			:    Union[str, Any]					  =(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Any:
							return unittest.skipUnless(
							    _atleast_one_tracker_available       ,    """test requires at least one tracker to be available and for `comet_ml` to not be installed"""       ,    )(UpperCamelCase   )

class _lowercase			(       unittest.TestCase	):
							a          = True




							@classmethod
							def 	lowerCamelCase_       (							cls:       Dict  ):
														lowerCamelCase__      :			str 						= tempfile.mkdtemp()




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]  ):
														if os.path.exists(cls.tmpdir  ):
																					shutil.rmtree(cls.tmpdir  )




							def 	lowerCamelCase_       (							self:       int  ):
														if self.clear_on_setup:
																					for path in Path(self.tmpdir  ).glob("""**/*"""  ):
																												if path.is_file():
																																			path.unlink()
																												elif path.is_dir():
																																			shutil.rmtree(UpperCamelCase__  )



class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														super().tearDown()
														# Reset the state of the AcceleratorState singleton.
														AcceleratorState._reset_state()
														PartialState._reset_state()



class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Union[mock.Mock, List[mock.Mock]]  ):
														lowerCamelCase__      :			List[str] 						= mocks if isinstance(UpperCamelCase__    ,							(tuple, list)  ) else [mocks]
														for m in self.mocks:
																					m.start()
																					self.addCleanup(m.stop  )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Tuple 						= AcceleratorState()
							lowerCamelCase__      :			List[str] 						= tensor[None].clone().to(state.device   )
							lowerCamelCase__      :			Optional[Any] 						= gather(UpperCamelCase   ).cpu()
							lowerCamelCase__      :			Optional[Any] 						= tensor[0].cpu()
							for i in range(tensors.shape[0]   ):
														if not torch.equal(tensors[i]       ,    UpperCamelCase   ):
																					return False
							return True

class _lowercase			:




							def __init__(							self:       Dict    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			Any 						= returncode
														lowerCamelCase__      :			Tuple 						= stdout
														lowerCamelCase__      :			Union[str, Any] 						= stderr





async def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							while True:
														lowerCamelCase__      :			str 						= await stream.readline()
														if line:
																					callback(UpperCamelCase   )
														else:
																					break





async def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=False       ,    UpperCamelCase=False   )   -> _RunOutput:
							if echo:
														print("""\nRunning: """       ,    """ """.join(UpperCamelCase   )   )

							lowerCamelCase__      :			Optional[Any] 						= await asyncio.create_subprocess_exec(
							    cmd[0]       ,    *cmd[1:]       ,    stdin=UpperCamelCase       ,    stdout=asyncio.subprocess.PIPE       ,    stderr=asyncio.subprocess.PIPE       ,    env=UpperCamelCase       ,    )

							# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
							# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
							#
							# If it starts hanging, will need to switch to the following code. The problem is that no data
							# will be seen until it's done and if it hangs for example there will be no debug info.
							# out, err = await p.communicate()
							# return _RunOutput(p.returncode, out, err)

							lowerCamelCase__      :			Any 						= []
							lowerCamelCase__      :			Union[str, Any] 						= []

							def tee(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=""   ):
														lowerCamelCase__      :			Union[str, Any] 						= line.decode("""utf-8"""   ).rstrip()
														sink.append(UpperCamelCase   )
														if not quiet:
																					print(UpperCamelCase       ,    UpperCamelCase       ,    file=UpperCamelCase   )

    # XXX: the timeout doesn't seem to make any difference here
							await asyncio.wait(
							    [
							        asyncio.create_task(_read_stream(p.stdout       ,    lambda UpperCamelCase   : tee(UpperCamelCase       ,    UpperCamelCase       ,    sys.stdout       ,    label="""stdout:"""   )   )   ),
							        asyncio.create_task(_read_stream(p.stderr       ,    lambda UpperCamelCase   : tee(UpperCamelCase       ,    UpperCamelCase       ,    sys.stderr       ,    label="""stderr:"""   )   )   ),
							    ]       ,    timeout=UpperCamelCase       ,    )
							return _RunOutput(await p.wait()       ,    UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=180       ,    UpperCamelCase=False       ,    UpperCamelCase=True   )   -> _RunOutput:
							lowerCamelCase__      :			Dict 						= asyncio.get_event_loop()
							lowerCamelCase__      :			int 						= loop.run_until_complete(
							    _stream_subprocess(UpperCamelCase       ,    env=UpperCamelCase       ,    stdin=UpperCamelCase       ,    timeout=UpperCamelCase       ,    quiet=UpperCamelCase       ,    echo=UpperCamelCase   )   )

							lowerCamelCase__      :			str 						= """ """.join(UpperCamelCase   )
							if result.returncode > 0:
														lowerCamelCase__      :			int 						= """\n""".join(result.stderr   )
														raise RuntimeError(
														    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
														    f'''The combined stderr from workers follows:\n{stderr}'''   )

							return result

class _lowercase			(       _lowercase	):
							pass





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Dict:
							try:
														lowerCamelCase__      :			Union[str, Any] 						= subprocess.check_output(UpperCamelCase       ,    stderr=subprocess.STDOUT   )
														if return_stdout:
																					if hasattr(UpperCamelCase       ,    """decode"""   ):
																												lowerCamelCase__      :			List[str] 						= output.decode("""utf-8"""   )
																					return output
							except subprocess.CalledProcessError as e:
														raise SubprocessCallException(
														    f'''Command `{' '.join(UpperCamelCase   )}` failed with the following error:\n\n{e.output.decode()}'''   ) from e









'''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 _lowercase			:




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

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




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

														lowerCamelCase__      :			Any 						= self.get_config()

														return config, pixel_values, labels




							def 	lowerCamelCase_       (							self:       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=UpperCamelCase__    ,							initializer_range=self.initializer_range    ,							mask_ratio=self.mask_ratio    ,							)




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Tuple 						= ViTMAEModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )




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




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[int] 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Any 						= config_and_inputs
														lowerCamelCase__      :			Optional[int] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							a          = {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Tuple 						= ViTMAEModelTester(self  )
														lowerCamelCase__      :			Union[str, Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														self.config_tester.run_common_tests()




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




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()

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




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			List[str] 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[int]  ):
														# make masks reproducible
														np.random.seed(2  )

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

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

														super().check_pt_tf_models(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																												# Make sure we don't have nans
																												lowerCamelCase__      :			Dict 						= after_outputs[0].cpu().numpy()
																												lowerCamelCase__      :			Tuple 						= 0
																												lowerCamelCase__      :			Union[str, 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
    to get deterministic results."""  )
							def 	lowerCamelCase_       (							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 	lowerCamelCase_       (							self:       Any  ):
														pass




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




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




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




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Tuple 						= ViTMAEModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> List[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




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




							@slow
							def 	lowerCamelCase_       (							self:       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__      :			Tuple 						= self.default_image_processor
														lowerCamelCase__      :			List[str] 						= prepare_img()
														lowerCamelCase__      :			int 						= 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__      :			List[str] 						= ViTMAEConfig()
														lowerCamelCase__      :			int 						= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
														lowerCamelCase__      :			Any 						= np.random.uniform(size=(1, num_patches)  )

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

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

														lowerCamelCase__      :			str 						= 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(UpperCamelCase__  )    ,							atol=1e-4  )  )

