






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def     a_   (__A				,       __A      )				->						Dict:



					"""simple docstring"""
					assert isinstance(__A				,       __A      )
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"				,       [False, True]      )
def     a_   (__A				,       __A				,       __A      )				->						List[Any]:



					"""simple docstring"""
					__a					:						List[Any]      =					tmp_path / "cache"
					__a					:						Optional[Any]      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__a					:						Tuple      =					JsonDatasetReader(__A				,       cache_dir=__A				,       keep_in_memory=__A      ).read()
					_check_json_dataset(__A				,       __A      )







@pytest.mark.parametrize(
    "features"				,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]				,       )
def     a_   (__A				,       __A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						Dict      =					tmp_path / "cache"
					__a					:						Optional[Any]      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					__a					:						List[str]      =					features.copy() if features else default_expected_features
					__a					:						str      =					(
					    Features({feature: Value(__A      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__a					:						List[Any]      =					JsonDatasetReader(__A				,       features=__A				,       cache_dir=__A      ).read()
					_check_json_dataset(__A				,       __A      )







@pytest.mark.parametrize(
    "features"				,       [
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]				,       )
def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Any      =					tmp_path / "cache"
					__a					:						Dict      =					{"col_3": "float64", "col_1": "string", "col_2": "int64"}
					__a					:						str      =					features.copy() if features else default_expected_features
					__a					:						Optional[Any]      =					(
					    Features({feature: Value(__A      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__a					:						Dict      =					JsonDatasetReader(__A				,       features=__A				,       cache_dir=__A      ).read()
					assert isinstance(__A				,       __A      )
					assert dataset.num_rows == 2
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_3", "col_1", "col_2"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







def     a_   (__A				,       __A      )				->						Any:



					"""simple docstring"""
					# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
					__a					:						Tuple      =					{"col_2": "int64", "col_3": "float64", "col_1": "string"}
					__a					:						Optional[int]      =					features.copy()
					__a					:						str      =					(
					    Features({feature: Value(__A      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__a					:						Union[str, Any]      =					tmp_path / "cache"
					__a					:						Union[str, Any]      =					JsonDatasetReader(__A				,       features=__A				,       cache_dir=__A      ).read()
					assert isinstance(__A				,       __A      )
					assert dataset.num_rows == 2
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_2", "col_3", "col_1"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("split"				,       [None, NamedSplit("train"      ), "train", "test"]      )
def     a_   (__A				,       __A				,       __A      )				->						Tuple:



					"""simple docstring"""
					__a					:						Optional[int]      =					tmp_path / "cache"
					__a					:						int      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					__a					:						Optional[Any]      =					JsonDatasetReader(__A				,       cache_dir=__A				,       split=__A      ).read()
					_check_json_dataset(__A				,       __A      )
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"				,       [str, list]      )
def     a_   (__A				,       __A				,       __A      )				->						Any:



					"""simple docstring"""
					if issubclass(__A				,       __A      ):
										__a					:						Optional[Any]      =					jsonl_path
					elif issubclass(__A				,       __A      ):
										__a					:						List[Any]      =					[jsonl_path]
					__a					:						str      =					tmp_path / "cache"
					__a					:						str      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					__a					:						Tuple      =					JsonDatasetReader(__A				,       cache_dir=__A      ).read()
					_check_json_dataset(__A				,       __A      )







def     a_   (__A				,       __A				,       __A=("train",)      )				->						Optional[int]:



					"""simple docstring"""
					assert isinstance(__A				,       __A      )
					for split in splits:
										__a					:						Dict      =					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 3
										assert dataset.column_names == ["col_1", "col_2", "col_3"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"				,       [False, True]      )
def     a_   (__A				,       __A				,       __A      )				->						Tuple:



					"""simple docstring"""
					__a					:						Optional[int]      =					tmp_path / "cache"
					__a					:						Optional[Any]      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__a					:						int      =					JsonDatasetReader({"train": jsonl_path}				,       cache_dir=__A				,       keep_in_memory=__A      ).read()
					_check_json_datasetdict(__A				,       __A      )







@pytest.mark.parametrize(
    "features"				,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]				,       )
def     a_   (__A				,       __A				,       __A      )				->						Optional[Any]:



					"""simple docstring"""
					__a					:						int      =					tmp_path / "cache"
					__a					:						List[str]      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					__a					:						List[str]      =					features.copy() if features else default_expected_features
					__a					:						Union[str, Any]      =					(
					    Features({feature: Value(__A      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					__a					:						List[Any]      =					JsonDatasetReader({"train": jsonl_path}				,       features=__A				,       cache_dir=__A      ).read()
					_check_json_datasetdict(__A				,       __A      )







@pytest.mark.parametrize("split"				,       [None, NamedSplit("train"      ), "train", "test"]      )
def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					if split:
										__a					:						List[Any]      =					{split: jsonl_path}
					else:
										__a					:						int      =					"train"
										__a					:						str      =					{"train": jsonl_path, "test": jsonl_path}
					__a					:						List[Any]      =					tmp_path / "cache"
					__a					:						List[Any]      =					{"col_1": "string", "col_2": "int64", "col_3": "float64"}
					__a					:						Any      =					JsonDatasetReader(__A				,       cache_dir=__A      ).read()
					_check_json_datasetdict(__A				,       __A				,       splits=list(path.keys()      )      )
					assert all(dataset[split].split == split for split in path.keys()      )







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					return json.load(__A      )







def     a_   (__A      )				->						Optional[int]:



					"""simple docstring"""
					return [json.loads(__A      ) for line in buffer]




class 							snake_case_       :

						"""simple docstring"""



						@pytest.mark.parametrize("lines, load_json_function" ,					[(True, load_json_lines), (False, load_json)]   )
						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					List[str]   )    ->     List[Any]:




											'''simple docstring'''



											with io.BytesIO() as buffer:
																JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					lines=__UpperCAmelCase   ).write()
																buffer.seek(0   )
																__a					:						Any      =					load_json_function(__UpperCAmelCase   )
											assert isinstance(__UpperCAmelCase ,					__UpperCAmelCase   )
											assert isinstance(exported_content[0] ,					__UpperCAmelCase   )
											assert len(__UpperCAmelCase   ) == 10



						@pytest.mark.parametrize(
						    "orient, container, keys, len_at" ,					[
						        ("records", list, {"tokens", "labels", "answers", "id"}, None),
						        ("split", dict, {"columns", "data"}, "data"),
						        ("index", dict, set("0123456789"   ), None),
						        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
						        ("values", list, None, None),
						        ("table", dict, {"schema", "data"}, "data"),
						    ] ,					)
						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					Tuple   )    ->     Any:




											'''simple docstring'''



											with io.BytesIO() as buffer:
																JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					lines=__UpperCAmelCase ,					orient=__UpperCAmelCase   ).write()
																buffer.seek(0   )
																__a					:						List[str]      =					load_json(__UpperCAmelCase   )
											assert isinstance(__UpperCAmelCase ,					__UpperCAmelCase   )
											if keys:
																if container is dict:
																					assert exported_content.keys() == keys
																else:
																					assert exported_content[0].keys() == keys
											else:
																assert not hasattr(__UpperCAmelCase ,					"keys"   ) and not hasattr(exported_content[0] ,					"keys"   )
											if len_at:
																assert len(exported_content[len_at]   ) == 10
											else:
																assert len(__UpperCAmelCase   ) == 10



						@pytest.mark.parametrize("lines, load_json_function" ,					[(True, load_json_lines), (False, load_json)]   )
						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Optional[int]   )    ->     Optional[int]:




											'''simple docstring'''



											with io.BytesIO() as buffer:
																JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					lines=__UpperCAmelCase ,					num_proc=2   ).write()
																buffer.seek(0   )
																__a					:						int      =					load_json_function(__UpperCAmelCase   )
											assert isinstance(__UpperCAmelCase ,					__UpperCAmelCase   )
											assert isinstance(exported_content[0] ,					__UpperCAmelCase   )
											assert len(__UpperCAmelCase   ) == 10



						@pytest.mark.parametrize(
						    "orient, container, keys, len_at" ,					[
						        ("records", list, {"tokens", "labels", "answers", "id"}, None),
						        ("split", dict, {"columns", "data"}, "data"),
						        ("index", dict, set("0123456789"   ), None),
						        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
						        ("values", list, None, None),
						        ("table", dict, {"schema", "data"}, "data"),
						    ] ,					)
						def        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Union[str, Any]   )    ->     List[Any]:




											'''simple docstring'''



											with io.BytesIO() as buffer:
																JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					lines=__UpperCAmelCase ,					orient=__UpperCAmelCase ,					num_proc=2   ).write()
																buffer.seek(0   )
																__a					:						List[str]      =					load_json(__UpperCAmelCase   )
											assert isinstance(__UpperCAmelCase ,					__UpperCAmelCase   )
											if keys:
																if container is dict:
																					assert exported_content.keys() == keys
																else:
																					assert exported_content[0].keys() == keys
											else:
																assert not hasattr(__UpperCAmelCase ,					"keys"   ) and not hasattr(exported_content[0] ,					"keys"   )
											if len_at:
																assert len(exported_content[len_at]   ) == 10
											else:
																assert len(__UpperCAmelCase   ) == 10



						def        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					int   )    ->     Any:




											'''simple docstring'''



											with pytest.raises(__UpperCAmelCase   ):
																with io.BytesIO() as buffer:
																					JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					num_proc=0   )



						@pytest.mark.parametrize("compression, extension" ,					[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]   )
						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[Any] ,					__UpperCAmelCase:					List[Any] ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					List[str]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					tmp_path_factory.mktemp("data"   ) / f'test.json.{extension}'
											__a					:						str      =					str(shared_datadir / f'test_file.json.{extension}'   )
											JsonDatasetWriter(__UpperCAmelCase ,					__UpperCAmelCase ,					compression=__UpperCAmelCase   ).write()

											with fsspec.open(__UpperCAmelCase ,					"rb" ,					compression="infer"   ) as f:
																__a					:						Optional[Any]      =					f.read()
											with fsspec.open(__UpperCAmelCase ,					"rb" ,					compression="infer"   ) as f:
																__a					:						int      =					f.read()
											assert exported_content == original_content













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 							snake_case_       :

						"""simple docstring"""



						def __init__(self:					Optional[int] ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					List[Any]=13 ,					__UpperCAmelCase:					Any=30 ,					__UpperCAmelCase:					List[str]=2 ,					__UpperCAmelCase:					Optional[int]=3 ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					str=True ,					__UpperCAmelCase:					int=32 ,					__UpperCAmelCase:					Tuple=5 ,					__UpperCAmelCase:					Optional[Any]=4 ,					__UpperCAmelCase:					Dict=37 ,					__UpperCAmelCase:					List[Any]="gelu" ,					__UpperCAmelCase:					List[Any]=0.1 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					Optional[Any]=10 ,					__UpperCAmelCase:					str=0.02 ,					__UpperCAmelCase:					Any=3 ,					__UpperCAmelCase:					Tuple=0.6 ,					__UpperCAmelCase:					str=None ,					)    ->     List[Any]:




											'''simple docstring'''



											__a					:						int      =					parent
											__a					:						Any      =					batch_size
											__a					:						int      =					image_size
											__a					:						Tuple      =					patch_size
											__a					:						str      =					num_channels
											__a					:						List[Any]      =					is_training
											__a					:						Tuple      =					use_labels
											__a					:						str      =					hidden_size
											__a					:						List[Any]      =					num_hidden_layers
											__a					:						str      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						str      =					hidden_act
											__a					:						Union[str, Any]      =					hidden_dropout_prob
											__a					:						Optional[int]      =					attention_probs_dropout_prob
											__a					:						Tuple      =					type_sequence_label_size
											__a					:						Any      =					initializer_range
											__a					:						Dict      =					mask_ratio
											__a					:						Union[str, Any]      =					scope

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



						def        UpperCAmelCase__					(self:					Any   )    ->     str:




											'''simple docstring'''



											__a					:						Dict      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

											__a					:						Optional[Any]      =					None
											if self.use_labels:
																__a					:						int      =					ids_tensor([self.batch_size] ,					self.type_sequence_label_size   )

											__a					:						Optional[int]      =					self.get_config()

											return config, pixel_values, labels



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Optional[int]:




											'''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=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					mask_ratio=self.mask_ratio ,					)



						def        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					List[str]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					ViTMAEModel(config=__UpperCAmelCase   )
											model.to(__UpperCAmelCase   )
											model.eval()
											__a					:						int      =					model(__UpperCAmelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)   )



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					Dict   )    ->     Dict:




											'''simple docstring'''



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



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




											'''simple docstring'''



											__a					:						List[Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						Union[str, Any]      =					{"pixel_values": pixel_values}
											return config, inputs_dict




@require_torch
class 							snake_case_       (    __UpperCamelCase		,							__UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						snake_case__        =      {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

						snake_case__        =      False
						snake_case__        =      False
						snake_case__        =      False
						snake_case__        =      False



						def        UpperCAmelCase__					(self:					int   )    ->     Dict:




											'''simple docstring'''



											__a					:						List[str]      =					ViTMAEModelTester(self   )
											__a					:						str      =					ConfigTester(self ,					config_class=__UpperCAmelCase ,					has_text_modality=__UpperCAmelCase ,					hidden_size=37   )



						def        UpperCAmelCase__					(self:					Tuple   )    ->     Dict:




											'''simple docstring'''



											self.config_tester.run_common_tests()



						@unittest.skip(reason="ViTMAE does not use inputs_embeds"   )
						def        UpperCAmelCase__					(self:					str   )    ->     Optional[Any]:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											__a      ,			__a					:						Optional[Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

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



						def        UpperCAmelCase__					(self:					Dict   )    ->     Tuple:




											'''simple docstring'''



											__a      ,			__a					:						Union[str, Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

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

																__a					:						int      =					["pixel_values"]
																self.assertListEqual(arg_names[:1] ,					__UpperCAmelCase   )



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




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						List[Any]      =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					str   )    ->     Dict:




											'''simple docstring'''



											np.random.seed(2   )

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

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

											super().check_pt_tf_models(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					str   )    ->     Dict:




											'''simple docstring'''



											__a      ,			__a					:						Dict      =					self.model_tester.prepare_config_and_inputs_for_common()

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

																__a					:						Dict      =					outputs[0].cpu().numpy()
																__a					:						str      =					0

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

																					# Make sure we don't have nans
																					__a					:						Dict      =					after_outputs[0].cpu().numpy()
																					__a					:						str      =					0
																					__a					:						str      =					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        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Any:




											'''simple docstring'''



											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        UpperCAmelCase__					(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\n    to get deterministic results."   )
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     List[str]:




											'''simple docstring'''



											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]   )    ->     Optional[Any]:




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											pass



						@slow
						def        UpperCAmelCase__					(self:					Tuple   )    ->     int:




											'''simple docstring'''



											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__a					:						Tuple      =					ViTMAEModel.from_pretrained(__UpperCAmelCase   )
																self.assertIsNotNone(__UpperCAmelCase   )







def     a_   ()				->						Optional[Any]:



					"""simple docstring"""
					__a					:						List[str]      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
					return image




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

						"""simple docstring"""



						@cached_property
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



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



						@slow
						def        UpperCAmelCase__					(self:					str   )    ->     Union[str, Any]:




											'''simple docstring'''



											np.random.seed(2   )

											__a					:						int      =					ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"   ).to(__UpperCAmelCase   )

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

											# forward pass
											with torch.no_grad():
																__a					:						Union[str, Any]      =					model(**__UpperCAmelCase ,					noise=torch.from_numpy(__UpperCAmelCase   ).to(device=__UpperCAmelCase   )   )

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

											__a					:						Any      =					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(__UpperCAmelCase   ) ,					atol=1E-4   )   )





