


"""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							, _a							, _a=13							, _a=30							, _a=2							, _a=3							, _a=True							, _a=True							, _a=32							, _a=5							, _a=4							, _a=37							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=10							, _a=0.02							, _a=3							, _a=0.6							, _a=None							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			is_training
				__a           =			use_labels
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			type_sequence_label_size
				__a           =			initializer_range
				__a           =			mask_ratio
				__a           =			scope

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

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

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

				__a           =			self.get_config()

				return config, pixel_values, labels

		def 			__UpperCAmelCase       (  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=_a							, initializer_range=self.initializer_range							, mask_ratio=self.mask_ratio							, )

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

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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()
				__a      ,     __a      ,     __a           =			config_and_inputs
				__a           =			{'''pixel_values''': pixel_values}
				return config, inputs_dict







@require_torch
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



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

		__UpperCAmelCase		:			List[str]										=				False
		__UpperCAmelCase		:			Tuple										=				False
		__UpperCAmelCase		:			int										=				False
		__UpperCAmelCase		:			Any										=				False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ViTMAEModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, has_text_modality=_a							, hidden_size=37      )

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

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

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

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

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

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

						__a           =			['''pixel_values''']
						self.assertListEqual(arg_names[:1]							, _a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				# make masks reproducible
				np.random.seed(2      )

				__a           =			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2      )
				__a           =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
				__a           =			torch.from_numpy(_a      )

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

				super().check_pt_tf_models(_a							, _a							, _a      )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

								# Make sure we don't have nans
								__a           =			after_outputs[0].cpu().numpy()
								__a           =			0
								__a           =			np.amax(np.abs(out_a - out_a      )      )
								self.assertLessEqual(_a							, 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 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  self      ):
				pass

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

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





		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			ViTMAEModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )







def        lowercase							(		) ->      Optional[int]:
		__a           =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
		return image




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


		'''simple docstring'''



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





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

				__a           =			ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''      ).to(_a      )

				__a           =			self.default_image_processor
				__a           =			prepare_img()
				__a           =			image_processor(images=_a							, return_tensors='''pt'''      ).to(_a      )

				# 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           =			ViTMAEConfig()
				__a           =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
				__a           =			np.random.uniform(size=(1, num_patches)      )

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

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

				__a           =			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(_a      )							, atol=1E-4      )      )










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     list[str]			) ->      str:
		__a           =			''''''
		for word_or_phrase in separated:
				if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
						raise Exception('''join() accepts only strings to be joined'''			)
				joined += word_or_phrase + separator
		return joined.strip(lowerCAmelCase__			)


if __name__ == "__main__":
				from doctest import testmod

				testmod()






