



'''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 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : List[Any]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : List[Any]=1_3							,     lowerCAmelCase__     : Tuple=3_0							,     lowerCAmelCase__     : Tuple=2							,     lowerCAmelCase__     : str=3							,     lowerCAmelCase__     : str=True							,     lowerCAmelCase__     : int=True							,     lowerCAmelCase__     : Any=3_2							,     lowerCAmelCase__     : Any=5							,     lowerCAmelCase__     : List[str]=4							,     lowerCAmelCase__     : Optional[Any]=3_7							,     lowerCAmelCase__     : List[str]="gelu"							,     lowerCAmelCase__     : Union[str, Any]=0.1							,     lowerCAmelCase__     : Dict=0.1							,     lowerCAmelCase__     : str=1_0							,     lowerCAmelCase__     : List[Any]=0.02							,     lowerCAmelCase__     : Tuple=3							,     lowerCAmelCase__     : Tuple=0.6							,     lowerCAmelCase__     : Any=None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		parent
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		batch_size
					__SCREAMING_SNAKE_CASE       :		Any           =		image_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		patch_size
					__SCREAMING_SNAKE_CASE       :		Any           =		num_channels
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		is_training
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		use_labels
					__SCREAMING_SNAKE_CASE       :		List[str]           =		hidden_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		num_hidden_layers
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		num_attention_heads
					__SCREAMING_SNAKE_CASE       :		str           =		intermediate_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		hidden_act
					__SCREAMING_SNAKE_CASE       :		int           =		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		type_sequence_label_size
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		Any           =		mask_ratio
					__SCREAMING_SNAKE_CASE       :		Any           =		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       :		Union[str, Any]           =		(image_size // patch_size) ** 2
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		int(math.ceil((1 - mask_ratio) * (num_patches + 1)						)						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__					(							self     : Optional[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=lowerCAmelCase__							,     initializer_range=self.initializer_range							,     mask_ratio=self.mask_ratio							,     )

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		ViTMAEModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)						)

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Any							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		ViTMAEForPreTraining(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(self.image_size // self.patch_size) ** 2
					__SCREAMING_SNAKE_CASE       :		str           =		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       :		Union[str, Any]           =		1
					__SCREAMING_SNAKE_CASE       :		List[str]           =		ViTMAEForPreTraining(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		model(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		self.patch_size**2
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, num_patches, expected_num_channels)						)



			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






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






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


			'''simple docstring'''

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

			_A      :		List[Any]			    =		False
			_A      :		Tuple			    =		False
			_A      :		Any			    =		False
			_A      :		Union[str, Any]			    =		False

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		ViTMAEModelTester(self						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		ConfigTester(self							,     config_class=lowerCAmelCase__							,     has_text_modality=lowerCAmelCase__							,     hidden_size=3_7						)

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					self.config_tester.run_common_tests()

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





					"""simple docstring"""






					pass

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		self.model_tester.prepare_config_and_inputs_for_common()

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

			def UpperCamelCase__					(							self     : Any						):





					"""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       :		List[Any]           =		model_class(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		str           =		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE       :		Tuple           =		[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE       :		Tuple           =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Tuple						):





					"""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       :		List[Any]           =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.from_numpy(lowerCAmelCase__						)

					# Add `noise` argument.
					# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		pt_noise

					super().check_pt_tf_models(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		self.model_tester.prepare_config_and_inputs_for_common()

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

							__SCREAMING_SNAKE_CASE       :		Dict           =		outputs[0].cpu().numpy()
							__SCREAMING_SNAKE_CASE       :		str           =		0

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

									# Make sure we don't have nans
									__SCREAMING_SNAKE_CASE       :		int           =		after_outputs[0].cpu().numpy()
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		np.amax(np.abs(out_a - out_a						)						)
									self.assertLessEqual(lowerCAmelCase__							,     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     : Optional[Any]						):





					"""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 UpperCamelCase__					(							self     : Union[str, Any]						):





					"""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 UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					pass

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





					"""simple docstring"""






					pass

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





					"""simple docstring"""






					pass



			@slow
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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


def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image






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


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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



			@slow
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					np.random.seed(2						)

					__SCREAMING_SNAKE_CASE       :		str           =		ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""						).to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.default_image_processor
					__SCREAMING_SNAKE_CASE       :		Tuple           =		prepare_img()
					__SCREAMING_SNAKE_CASE       :		int           =		image_processor(images=lowerCAmelCase__							,     return_tensors="""pt"""						).to(lowerCAmelCase__						)

					# 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       :		Optional[int]           =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		np.random.uniform(size=(1, num_patches)						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(**lowerCAmelCase__							,     noise=torch.from_numpy(lowerCAmelCase__						).to(device=lowerCAmelCase__						)						)

					# verify the logits
					__SCREAMING_SNAKE_CASE       :		Dict           =		torch.Size((1, 1_9_6, 7_6_8)						)
					self.assertEqual(outputs.logits.shape							,     lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch.tensor(
					    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]						)

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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__       :	Any    = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__       :	Optional[Any]    = [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						UpperCamelCase__       :	str    = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)