
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       __A:

					def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[Any]=1_3   ,					__UpperCamelCase					:     List[str]=3_0   ,					__UpperCamelCase					:     Dict=2   ,					__UpperCamelCase					:     Optional[Any]=3   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     Tuple=True   ,					__UpperCamelCase					:     Union[str, Any]=3_2   ,					__UpperCamelCase					:     List[Any]=5   ,					__UpperCamelCase					:     Any=4   ,					__UpperCamelCase					:     List[str]=3_7   ,					__UpperCamelCase					:     str="gelu"   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Optional[Any]=0.1   ,					__UpperCamelCase					:     Optional[int]=1_0   ,					__UpperCamelCase					:     Any=0.02   ,					__UpperCamelCase					:     Union[str, Any]=3   ,					__UpperCamelCase					:     List[Any]=0.6   ,					__UpperCamelCase					:     Any=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					image_size
												lowerCamelCase_							  =					patch_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					type_sequence_label_size
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					mask_ratio
												lowerCamelCase_							  =					scope

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

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												lowerCamelCase_							  =					self.get_config()

												return config, pixel_values, labels

					def    lowercase__      (	self					:     List[str]   ):
												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=__UpperCamelCase   ,					initializer_range=self.initializer_range   ,					mask_ratio=self.mask_ratio   ,					)

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

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

												# test greyscale images
												lowerCamelCase_							  =					1
												lowerCamelCase_							  =					ViTMAEForPreTraining(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												lowerCamelCase_							  =					self.patch_size**2
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, num_patches, expected_num_channels)   )



					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_torch
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE			    =    {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					ViTMAEModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					has_text_modality=__UpperCamelCase   ,					hidden_size=3_7   )

					def    lowercase__      (	self					:     Optional[int]   ):
												self.config_tester.run_common_tests()

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

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_							  =					["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]   ,					__UpperCamelCase   )

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

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase   )

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

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

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

												super().check_pt_tf_models(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

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

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

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



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



def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
							return image





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

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



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

												lowerCamelCase_							  =					ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""   ).to(__UpperCamelCase   )

												lowerCamelCase_							  =					self.default_image_processor
												lowerCamelCase_							  =					prepare_img()
												lowerCamelCase_							  =					image_processor(images=__UpperCamelCase   ,					return_tensors="""pt"""   ).to(__UpperCamelCase   )

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

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

												# verify the logits
												lowerCamelCase_							  =					torch.Size((1, 1_9_6, 7_6_8)   )
												self.assertEqual(outputs.logits.shape   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					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(__UpperCamelCase   )   ,					atol=1E-4   )   )







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float(moles / volume       ) * nfactor       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (volume)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((pressure * volume) / (0.0_8_2_1 * moles)       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





