


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						:		str		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]=13		, _SCREAMING_SNAKE_CASE						:		Optional[int]=30		, _SCREAMING_SNAKE_CASE						:		str=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=3		, _SCREAMING_SNAKE_CASE						:		int=True		, _SCREAMING_SNAKE_CASE						:		Tuple=True		, _SCREAMING_SNAKE_CASE						:		Dict=32		, _SCREAMING_SNAKE_CASE						:		Optional[int]=5		, _SCREAMING_SNAKE_CASE						:		Optional[int]=4		, _SCREAMING_SNAKE_CASE						:		Optional[int]=37		, _SCREAMING_SNAKE_CASE						:		Tuple="gelu"		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		int=0.1		, _SCREAMING_SNAKE_CASE						:		Dict=10		, _SCREAMING_SNAKE_CASE						:		int=0.02		, _SCREAMING_SNAKE_CASE						:		Any=3		, _SCREAMING_SNAKE_CASE						:		Dict=0.6		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, )->	int:
			lowerCAmelCase__       :      Optional[int]   					= parent
			lowerCAmelCase__       :      Tuple   					= batch_size
			lowerCAmelCase__       :      List[str]   					= image_size
			lowerCAmelCase__       :      Dict   					= patch_size
			lowerCAmelCase__       :      Any   					= num_channels
			lowerCAmelCase__       :      List[str]   					= is_training
			lowerCAmelCase__       :      List[Any]   					= use_labels
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Optional[Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[str]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Any   					= hidden_dropout_prob
			lowerCAmelCase__       :      List[Any]   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      int   					= type_sequence_label_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= mask_ratio
			lowerCAmelCase__       :      Dict   					= scope

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




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	str:
			lowerCAmelCase__       :      List[str]   					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

			lowerCAmelCase__       :      List[Any]   					= None
			if self.use_labels:
					lowerCAmelCase__       :      str   					= ids_tensor([self.batch_size]		, self.type_sequence_label_size		)

			lowerCAmelCase__       :      Optional[Any]   					= self.get_config()

			return config, pixel_values, labels




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Any:
			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=_SCREAMING_SNAKE_CASE		, initializer_range=self.initializer_range		, mask_ratio=self.mask_ratio		, )




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	Optional[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= ViTMAEModel(config=_SCREAMING_SNAKE_CASE		)
			model.to(_SCREAMING_SNAKE_CASE		)
			model.eval()
			lowerCAmelCase__       :      Optional[int]   					= model(_SCREAMING_SNAKE_CASE		)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)		)




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




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      int   					= self.prepare_config_and_inputs()
			lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= config_and_inputs
			lowerCAmelCase__       :      Optional[Any]   					= {'''pixel_values''': pixel_values}
			return config, inputs_dict





@require_torch
class  _a							(    _SCREAMING_SNAKE_CASE					,						_SCREAMING_SNAKE_CASE					,						unittest.TestCase):
	_a  :  List[str]				   =							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_a  :  Optional[int]				   =							{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_a  :  List[str]				   =							False
	_a  :  Optional[int]				   =							False
	_a  :  int				   =							False
	_a  :  Any				   =							False




	def 							UpperCAmelCase__( self						:		Tuple		)->	int:
			lowerCAmelCase__       :      Tuple   					= ViTMAEModelTester(self		)
			lowerCAmelCase__       :      int   					= ConfigTester(self		, config_class=_SCREAMING_SNAKE_CASE		, has_text_modality=_SCREAMING_SNAKE_CASE		, hidden_size=37		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Dict:
			self.config_tester.run_common_tests()




	@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''		)
	def 							UpperCAmelCase__( self						:		str		)->	List[str]:
			pass




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Any:
			lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCAmelCase__       :      int   					= model_class(_SCREAMING_SNAKE_CASE		)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)		)
					lowerCAmelCase__       :      Optional[Any]   					= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE		, nn.Linear		)		)




	def 							UpperCAmelCase__( self						:		Tuple		)->	Optional[int]:
			lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCAmelCase__       :      Optional[int]   					= ['''pixel_values''']
					self.assertListEqual(arg_names[:1]		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	List[str]:
			lowerCAmelCase__       :      Union[str, Any]   					= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Optional[int]:
			lowerCAmelCase__       :      Optional[Any]   					= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		int		)->	Any:
			# make masks reproducible
			np.random.seed(2		)

			lowerCAmelCase__       :      Union[str, Any]   					= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
			lowerCAmelCase__       :      Dict   					= np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
			lowerCAmelCase__       :      Optional[int]   					= torch.from_numpy(_SCREAMING_SNAKE_CASE		)

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

			super().check_pt_tf_models(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Optional[int]		)->	Dict:
			lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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




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




	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	str:
			pass




	@slow
	def 							UpperCAmelCase__( self						:		str		)->	Union[str, Any]:
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__       :      List[Any]   					= ViTMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image





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




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




	@slow
	def 							UpperCAmelCase__( self						:		str		)->	List[str]:
			# make random mask reproducible across the PT and TF model
			np.random.seed(2		)

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

			lowerCAmelCase__       :      Tuple   					= self.default_image_processor
			lowerCAmelCase__       :      List[str]   					= prepare_img()
			lowerCAmelCase__       :      Optional[Any]   					= image_processor(images=_SCREAMING_SNAKE_CASE		, return_tensors='''pt'''		).to(_SCREAMING_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)
			lowerCAmelCase__       :      Tuple   					= ViTMAEConfig()
			lowerCAmelCase__       :      List[Any]   					= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
			lowerCAmelCase__       :      List[Any]   					= np.random.uniform(size=(1, num_patches)		)

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

			# verify the logits
			lowerCAmelCase__       :      Dict   					= torch.Size((1, 196, 768)		)
			self.assertEqual(outputs.logits.shape		, _SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[Any]   					= 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(_SCREAMING_SNAKE_CASE		)		, atol=1E-4		)		)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}





class  _a							(    _lowercase):
	_a  :  List[Any]				   =							'''dpr'''




	def __init__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[str]=3_0522		, _SCREAMING_SNAKE_CASE						:		Optional[int]=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=512		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		int = 0		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	Optional[int]:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Union[str, Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      int   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      List[Any]   					= type_vocab_size
			lowerCAmelCase__       :      Optional[int]   					= initializer_range
			lowerCAmelCase__       :      List[str]   					= layer_norm_eps
			lowerCAmelCase__       :      Dict   					= projection_dim
			lowerCAmelCase__       :      int   					= position_embedding_type




