
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 							snake_case_ :





			def __init__(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase=13      ,						__lowerCAmelCase=30      ,						__lowerCAmelCase=2      ,						__lowerCAmelCase=3      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=32      ,						__lowerCAmelCase=5      ,						__lowerCAmelCase=4      ,						__lowerCAmelCase=37      ,						__lowerCAmelCase="gelu"      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=10      ,						__lowerCAmelCase=0.02      ,						__lowerCAmelCase=3      ,						__lowerCAmelCase=0.6      ,						__lowerCAmelCase=None      ,						):
					SCREAMING_SNAKE_CASE_					:	str		  =			parent
					SCREAMING_SNAKE_CASE_					:	str		  =			batch_size
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			image_size
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			patch_size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			num_channels
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			is_training
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			use_labels
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			hidden_size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			num_hidden_layers
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			num_attention_heads
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			intermediate_size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			hidden_act
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			hidden_dropout_prob
					SCREAMING_SNAKE_CASE_					:	str		  =			attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE_					:	Any		  =			type_sequence_label_size
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			initializer_range
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			mask_ratio
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			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_					:	int		  =			(image_size // patch_size) ** 2
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)      )      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

					SCREAMING_SNAKE_CASE_					:	str		  =			self.get_config()

					return config, pixel_values, labels





			def 						__A						(		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=__lowerCAmelCase      ,						initializer_range=self.initializer_range      ,						mask_ratio=self.mask_ratio      ,						)





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			ViTMAEModel(config=__lowerCAmelCase      )
					model.to(__lowerCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE_					:	Any		  =			model(__lowerCAmelCase      )
					self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)      )





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			ViTMAEForPreTraining(__lowerCAmelCase      )
					model.to(__lowerCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			model(__lowerCAmelCase      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			(self.image_size // self.patch_size) ** 2
					SCREAMING_SNAKE_CASE_					:	int		  =			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_					:	Dict		  =			ViTMAEForPreTraining(__lowerCAmelCase      )
					model.to(__lowerCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			model(__lowerCAmelCase      )
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			self.patch_size**2
					self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, num_patches, expected_num_channels)      )






			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	List[str]		  =			config_and_inputs
					SCREAMING_SNAKE_CASE_					:	int		  =			{'pixel_values': pixel_values}
					return config, inputs_dict

@require_torch
class 							snake_case_ (				lowercase_   ,  lowercase_   ,  unittest.TestCase    ):
			__lowerCamelCase							:     Dict						=			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			__lowerCamelCase							:     Union[str, Any]						=			{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

			__lowerCamelCase							:     List[Any]						=			False
			__lowerCamelCase							:     Union[str, Any]						=			False
			__lowerCamelCase							:     List[Any]						=			False
			__lowerCamelCase							:     Union[str, Any]						=			False





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			ViTMAEModelTester(self      )
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			ConfigTester(self      ,						config_class=__lowerCAmelCase      ,						has_text_modality=__lowerCAmelCase      ,						hidden_size=37      )





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





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





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_					:	int		  =			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 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()

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

							SCREAMING_SNAKE_CASE_					:	Any		  =			['pixel_values']
							self.assertListEqual(arg_names[:1]      ,						__lowerCAmelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Any		  =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase      )





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					# make masks reproducible
					np.random.seed(2      )

					SCREAMING_SNAKE_CASE_					:	Any		  =			int((pt_model.config.image_size // pt_model.config.patch_size) ** 2      )
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			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_					:	Union[str, Any]		  =			pt_noise

					super().check_pt_tf_models(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	str		  =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

									# Make sure we don't have nans
									SCREAMING_SNAKE_CASE_					:	str		  =			after_outputs[0].cpu().numpy()
									SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			0
									SCREAMING_SNAKE_CASE_					:	List[str]		  =			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\n    to get deterministic results.'      )
			def 						__A						(		self      ):
					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 						__A						(		self      ):
					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 						__A						(		self      ):
					pass





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





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






			@slow
			def 						__A						(		self      ):
					for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			ViTMAEModel.from_pretrained(__lowerCAmelCase      )
							self.assertIsNotNone(__lowerCAmelCase      )



def   __SCREAMING_SNAKE_CASE (		)		-> Optional[int]:
		SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image

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





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






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

					SCREAMING_SNAKE_CASE_					:	int		  =			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'      ).to(__lowerCAmelCase      )

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			self.default_image_processor
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			prepare_img()
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			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_					:	Optional[Any]		  =			ViTMAEConfig()
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2      )
					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			np.random.uniform(size=(1, num_patches)      )

					# forward pass
					with torch.no_grad():
							SCREAMING_SNAKE_CASE_					:	Dict		  =			model(**__lowerCAmelCase      ,						noise=torch.from_numpy(__lowerCAmelCase      ).to(device=__lowerCAmelCase      )      )

					# verify the logits
					SCREAMING_SNAKE_CASE_					:	str		  =			torch.Size((1, 196, 768)      )
					self.assertEqual(outputs.logits.shape      ,						__lowerCAmelCase      )

					SCREAMING_SNAKE_CASE_					:	str		  =			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      )      )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




