




"""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					:	List[Any]    ,   lowercase__					:	str    ,   lowercase__					:	Optional[int]=1_3    ,   lowercase__					:	str=3_0    ,   lowercase__					:	Union[str, Any]=2    ,   lowercase__					:	Optional[int]=3    ,   lowercase__					:	Dict=True    ,   lowercase__					:	Dict=True    ,   lowercase__					:	Any=3_2    ,   lowercase__					:	Any=5    ,   lowercase__					:	str=4    ,   lowercase__					:	Dict=3_7    ,   lowercase__					:	Tuple="gelu"    ,   lowercase__					:	Dict=0.1    ,   lowercase__					:	int=0.1    ,   lowercase__					:	int=1_0    ,   lowercase__					:	Any=0.0_2    ,   lowercase__					:	Tuple=3    ,   lowercase__					:	Optional[Any]=0.6    ,   lowercase__					:	Optional[int]=None    ,   ):
						__lowercase				:		Dict												=				parent
						__lowercase				:		Dict												=				batch_size
						__lowercase				:		Optional[int]												=				image_size
						__lowercase				:		Union[str, Any]												=				patch_size
						__lowercase				:		Dict												=				num_channels
						__lowercase				:		Optional[int]												=				is_training
						__lowercase				:		Dict												=				use_labels
						__lowercase				:		Any												=				hidden_size
						__lowercase				:		str												=				num_hidden_layers
						__lowercase				:		int												=				num_attention_heads
						__lowercase				:		Optional[Any]												=				intermediate_size
						__lowercase				:		Dict												=				hidden_act
						__lowercase				:		int												=				hidden_dropout_prob
						__lowercase				:		Dict												=				attention_probs_dropout_prob
						__lowercase				:		Optional[Any]												=				type_sequence_label_size
						__lowercase				:		str												=				initializer_range
						__lowercase				:		Optional[int]												=				mask_ratio
						__lowercase				:		Tuple												=				scope

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



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		Tuple												=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						__lowercase				:		Union[str, Any]												=				self.get_config()

						return config, pixel_values, labels



				def       snake_case    (     self					:	Dict						):
						return ViTMAEConfig(
						    image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   is_decoder=lowercase__    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	Any    ,   lowercase__					:	str    ,   lowercase__					:	Dict						):
						__lowercase				:		Optional[int]												=				ViTMAEModel(config=lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		Union[str, Any]												=				model(lowercase__						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)						)



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	str    ,   lowercase__					:	int    ,   lowercase__					:	Optional[int]						):
						__lowercase				:		int												=				ViTMAEForPreTraining(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		Optional[int]												=				model(lowercase__						)
						__lowercase				:		Optional[int]												=				(self.image_size // self.patch_size) ** 2
						__lowercase				:		Dict												=				self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)						)

						# test greyscale images
						__lowercase				:		Union[str, Any]												=				1
						__lowercase				:		Optional[Any]												=				ViTMAEForPreTraining(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						__lowercase				:		List[str]												=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
						__lowercase				:		Optional[Any]												=				model(lowercase__						)
						__lowercase				:		List[Any]												=				self.patch_size**2
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)						)



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





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


				"""simple docstring"""





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

				__UpperCAmelCase :     str       =      False
				__UpperCAmelCase :     Dict       =      False
				__UpperCAmelCase :     str       =      False
				__UpperCAmelCase :     List[Any]       =      False



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		List[str]												=				ViTMAEModelTester(self						)
						__lowercase				:		List[Any]												=				ConfigTester(self    ,   config_class=lowercase__    ,   has_text_modality=lowercase__    ,   hidden_size=3_7						)



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



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



				def       snake_case    (     self					:	Tuple						):
						__lowercase						,__lowercase				:		List[Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

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



				def       snake_case    (     self					:	int						):
						__lowercase						,__lowercase				:		Optional[Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase				:		Any												=				["pixel_values"]
								self.assertListEqual(arg_names[:1]    ,   lowercase__						)



				def       snake_case    (     self					:	Optional[Any]						):
						__lowercase				:		Any												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase__						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Optional[int]												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	str    ,   lowercase__					:	Optional[Any]    ,   lowercase__					:	int						):
						# make masks reproducible
						np.random.seed(2						)

						__lowercase				:		Any												=				int((pt_model.config.image_size // pt_model.config.patch_size) ** 2						)
						__lowercase				:		Tuple												=				np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
						__lowercase				:		Optional[Any]												=				torch.from_numpy(lowercase__						)

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

						super().check_pt_tf_models(lowercase__    ,   lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[str]						):
						__lowercase						,__lowercase				:		Tuple												=				self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase				:		Dict												=				outputs[0].cpu().numpy()
								__lowercase				:		Optional[int]												=				0

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

										# Make sure we don't have nans
										__lowercase				:		int												=				after_outputs[0].cpu().numpy()
										__lowercase				:		Dict												=				0
										__lowercase				:		Tuple												=				np.amax(np.abs(out_a - out_a						)						)
										self.assertLessEqual(lowercase__    ,   1e-5						)



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



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



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



				@slow
				def       snake_case    (     self					:	Optional[Any]						):
						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase				:		Any												=				ViTMAEModel.from_pretrained(lowercase__						)
								self.assertIsNotNone(lowercase__						)





def 						snake_case__       (					)			->Tuple:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image





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


				"""simple docstring"""





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



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

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

						__lowercase				:		str												=				self.default_image_processor
						__lowercase				:		Any												=				prepare_img()
						__lowercase				:		List[Any]												=				image_processor(images=lowercase__    ,   return_tensors="pt"						).to(lowercase__						)

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						__lowercase				:		Tuple												=				ViTMAEConfig()
						__lowercase				:		Tuple												=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
						__lowercase				:		Dict												=				np.random.uniform(size=(1, num_patches)						)

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

						# verify the logits
						__lowercase				:		Tuple												=				torch.Size((1, 1_9_6, 7_6_8)						)
						self.assertEqual(outputs.logits.shape    ,   lowercase__						)

						__lowercase				:		List[str]												=				torch.tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]						)

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






"""simple docstring"""







from collections import deque

from .hash_table import HashTable




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   *lowercase__					:	Any    ,   **lowercase__					:	Union[str, Any]						):
						super().__init__(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	Dict						):
						__lowercase				:		Dict												=				deque([]						) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.values[key]



				def       snake_case    (     self					:	str						):
						return (
						    sum(self.charge_factor - len(lowercase__						) for slot in self.values						)
						    / self.size_table
						    * self.charge_factor
						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str=None						):
						if not (
						    len(self.values[key]						) == self.charge_factor and self.values.count(lowercase__						) == 0
						):
								return key
						return super()._collision_resolution(lowercase__    ,   lowercase__						)
