
"""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 			lowerCAmelCase_		:


				"""simple docstring"""



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



											"""simple docstring"""




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

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



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

											SCREAMING_SNAKE_CASE__		: Any     =     self.get_config()

											return config, pixel_values, labels



				def 			__magic_name__					(self					)      ->	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=SCREAMING_SNAKE_CASE__     , initializer_range=self.initializer_range     , mask_ratio=self.mask_ratio     , )



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     ViTMAEModel(config=SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model(SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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






@require_torch
class 			lowerCAmelCase_		(a__				,      a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				__UpperCamelCase   :		int       =     {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

				__UpperCamelCase   :		Dict       =     False
				__UpperCamelCase   :		Tuple       =     False
				__UpperCamelCase   :		Optional[Any]       =     False
				__UpperCamelCase   :		List[Any]       =     False



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     ViTMAEModelTester(self					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ConfigTester(self     , config_class=SCREAMING_SNAKE_CASE__     , has_text_modality=SCREAMING_SNAKE_CASE__     , hidden_size=37					)



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											self.config_tester.run_common_tests()



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



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[Any]     =     self.model_tester.prepare_config_and_inputs_for_common()

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



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



											"""simple docstring"""




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

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

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



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



											"""simple docstring"""




											np.random.seed(2					)

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

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

											super().check_pt_tf_models(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE__		: int     =     outputs[0].cpu().numpy()
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     0

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

																									# Make sure we don't have nans
																									SCREAMING_SNAKE_CASE__		: str     =     after_outputs[0].cpu().numpy()
																									SCREAMING_SNAKE_CASE__		: Dict     =     0
																									SCREAMING_SNAKE_CASE__		: Dict     =     np.amax(np.abs(out_a - out_a					)					)
																									self.assertLessEqual(SCREAMING_SNAKE_CASE__     , 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 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



				@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""					)
				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     ViTMAEModel.from_pretrained(SCREAMING_SNAKE_CASE__					)
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__					)






def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Tuple     =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							return image



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


				"""simple docstring"""



				@cached_property
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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



				@slow
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											np.random.seed(2					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					).to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     self.default_image_processor
											SCREAMING_SNAKE_CASE__		: Any     =     prepare_img()
											SCREAMING_SNAKE_CASE__		: Any     =     image_processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# 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)
											SCREAMING_SNAKE_CASE__		: Tuple     =     ViTMAEConfig()
											SCREAMING_SNAKE_CASE__		: int     =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.random.uniform(size=(1, num_patches)					)

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

											# verify the logits
											SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.Size((1, 1_96, 7_68)					)
											self.assertEqual(outputs.logits.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: List[str]     =     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(SCREAMING_SNAKE_CASE__					)     , atol=1E-4					)					)






"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast



@dataclass
class 			lowerCAmelCase_		(datasets.BuilderConfig						):


				"""simple docstring"""



				__UpperCamelCase   :		Optional[datasets.Features]       =     None






class 			lowerCAmelCase_		(datasets.ArrowBasedBuilder						):


				"""simple docstring"""



				__UpperCamelCase   :		Union[str, Any]       =     PandasConfig



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



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											if not self.config.data_files:
																		raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dl_manager.download_and_extract(self.config.data_files					)
											if isinstance(SCREAMING_SNAKE_CASE__     , (str, list, tuple)					):
																		SCREAMING_SNAKE_CASE__		: Tuple     =     data_files
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: str     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN     , gen_kwargs={"""files""": files}					)]
											SCREAMING_SNAKE_CASE__		: int     =     []
											for split_name, files in data_files.items():
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: List[str]     =     [files]
																		# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
																		SCREAMING_SNAKE_CASE__		: Dict     =     [dl_manager.iter_files(SCREAMING_SNAKE_CASE__					) for file in files]
																		splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__     , gen_kwargs={"""files""": files}					)					)
											return splits



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		# more expensive cast to support nested features with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		SCREAMING_SNAKE_CASE__		: Tuple     =     table_cast(SCREAMING_SNAKE_CASE__     , self.config.features.arrow_schema					)
											return pa_table



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__					)					):
																		with open(SCREAMING_SNAKE_CASE__     , """rb"""					) as f:
																									SCREAMING_SNAKE_CASE__		: List[str]     =     pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__					)					)
																									yield i, self._cast_table(SCREAMING_SNAKE_CASE__					)




