


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      ,						snake_case      ,						snake_case=13      ,						snake_case=30      ,						snake_case=2      ,						snake_case=3      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						snake_case=3      ,						snake_case=0.6      ,						snake_case=None      ,						):

						'''simple docstring'''


						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 				snake_case__				(							self     ):

						'''simple docstring'''


						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 				snake_case__				(							self     ):

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


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







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






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

			_UpperCamelCase		:					List[str]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					Optional[Any]    =		False
			_UpperCamelCase		:					int    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       ViTMAEModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




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

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						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(snake_case     )

						# 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(snake_case      ,						snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

												# 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(snake_case      ,						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     ):

						'''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 				snake_case__				(							self     ):

						'''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 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




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

						'''simple docstring'''


						pass




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

						'''simple docstring'''


						pass







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       ViTMAEModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )



def        UpperCamelCase_(				)->		Optional[int]:
			UpperCamelCase__					=       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     ):

						'''simple docstring'''


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







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						np.random.seed(2     )

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

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_img()
						UpperCamelCase__					=       image_processor(images=snake_case      ,						return_tensors="pt"     ).to(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)
						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(**snake_case      ,						noise=torch.from_numpy(snake_case     ).to(device=snake_case     )     )

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

						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(snake_case     )      ,						atol=1E-4     )     )






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


