





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					:






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=13  , __lowerCAmelCase=30  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=True  , __lowerCAmelCase=True  , __lowerCAmelCase=32  , __lowerCAmelCase=5  , __lowerCAmelCase=4  , __lowerCAmelCase=37  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=10  , __lowerCAmelCase=0.02  , __lowerCAmelCase=3  , __lowerCAmelCase=0.6  , __lowerCAmelCase=None  , ):
														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 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  ):
														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=__lowerCAmelCase  , initializer_range=self.initializer_range  , mask_ratio=self.mask_ratio  , )






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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			ViTMAEForPreTraining(__lowerCAmelCase  )
														model.to(__lowerCAmelCase  )
														model.eval()
														UpperCamelCase__				     =			model(__lowerCAmelCase  )
														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(__lowerCAmelCase  )
														model.to(__lowerCAmelCase  )
														model.eval()
														UpperCamelCase__				     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
														UpperCamelCase__				     =			model(__lowerCAmelCase  )
														UpperCamelCase__				     =			self.patch_size**2
														self.parent.assertEqual(result.logits.shape  , (self.batch_size, num_patches, expected_num_channels)  )







							def 				_lowerCamelCase		(						self  ):
														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					(     _a   ,		_a   ,		unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							snake_case     :      Optional[int]						     =       {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

							snake_case     :      Tuple						     =       False
							snake_case     :      Optional[int]						     =       False
							snake_case     :      int						     =       False
							snake_case     :      Dict						     =       False






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			ViTMAEModelTester(self  )
														UpperCamelCase__				     =			ConfigTester(self  , config_class=__lowerCAmelCase  , has_text_modality=__lowerCAmelCase  , hidden_size=37  )






							def 				_lowerCamelCase		(						self  ):
														self.config_tester.run_common_tests()






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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()

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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					UpperCamelCase__				     =			model_class(__lowerCAmelCase  )
																					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]  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														# make masks reproducible
														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(__lowerCAmelCase  )

														# 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(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																												# 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(__lowerCAmelCase  , 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  ):
														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  ):
														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  ):
														pass






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






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







							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					UpperCamelCase__				     =			ViTMAEModel.from_pretrained(__lowerCAmelCase  )
																					self.assertIsNotNone(__lowerCAmelCase  )






def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image

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






							@cached_property
							def 				_lowerCamelCase		(						self  ):
														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""  ) if is_vision_available() else None







							@slow
							def 				_lowerCamelCase		(						self  ):
														# make random mask reproducible across the PT and TF model
														np.random.seed(2  )

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

														UpperCamelCase__				     =			self.default_image_processor
														UpperCamelCase__				     =			prepare_img()
														UpperCamelCase__				     =			image_processor(images=__lowerCAmelCase  , return_tensors="""pt"""  ).to(__lowerCAmelCase  )

														# 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(**__lowerCAmelCase  , noise=torch.from_numpy(__lowerCAmelCase  ).to(device=__lowerCAmelCase  )  )

														# verify the logits
														UpperCamelCase__				     =			torch.Size((1, 196, 768)  )
														self.assertEqual(outputs.logits.shape  , __lowerCAmelCase  )

														UpperCamelCase__				     =			torch.tensor(
														    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  , expected_slice.to(__lowerCAmelCase  )  , atol=1E-4  )  )











import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    """files"""   ,			[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]   ,			)
def     _UpperCamelCase     (a__   :Any   ,			a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			tmp_path_factory.mktemp("""dset_infos_dir"""       )
							if "full:README.md" in files:
														with open(dataset_infos_dir / """README.md"""   ,			"""w"""       ) as f:
																					f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""       )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / """README.md"""   ,			"""w"""       ) as f:
																					f.write(""""""       )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / """dataset_infos.json"""   ,			"""w"""       ) as f:
																					f.write("""{\"default\": {\"dataset_size\": 42}}"""       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    """dataset_info"""   ,			[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train"""}]   ,			download_size=42   ,			),
    ]   ,			)
def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :DatasetInfo       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_info.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfo.from_directory(a__       )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(a__   ,			"""dataset_info.json"""       )       )





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			DatasetInfo(
							    description="""foo"""   ,			citation="""bar"""   ,			homepage="""https://foo.bar"""   ,			license="""CC0"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			post_processed={}   ,			supervised_keys=()   ,			task_templates=[]   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train""", """num_examples""": 42}]   ,			download_checksums={}   ,			download_size=1337   ,			post_processing_size=442   ,			dataset_size=1234   ,			size_in_bytes=1337 + 442 + 1234   ,			)
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert sorted(a__       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]   ,			(list, dict, int, str)       )
							UpperCamelCase__				     =			yaml.safe_dump(a__       )
							UpperCamelCase__				     =			yaml.safe_load(a__       )
							assert dataset_info_yaml_dict == reloaded





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			DatasetInfo()
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    """dataset_infos_dict"""   ,			[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}       ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train"""}]   ,			download_size=42   ,			)
            }       ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42       ),
                """v2""": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]   ,			)
def     _UpperCamelCase     (a__   :int   ,			a__   :DatasetInfosDict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_infos_dict.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														UpperCamelCase__				     =			config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														UpperCamelCase__				     =			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(a__   ,			"""README.md"""       )       )




