

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

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								__lowerCamelCase							:							Union[str, Any]   			=					(image_size // patch_size) ** 2
								__lowerCamelCase							:							int   			=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
							def        lowerCAmelCase	(						self						:     Dict):
								__lowerCamelCase							:							Any   			=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								__lowerCamelCase							:							str   			=					self.get_config()

								return config, pixel_values, labels
							def        lowerCAmelCase	(						self						:     Tuple):
								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=A_  ,initializer_range=self.initializer_range  ,mask_ratio=self.mask_ratio  ,)
							def        lowerCAmelCase	(						self						:     Any  ,SCREAMING_SNAKE_CASE__						:     Union[str, Any]  ,SCREAMING_SNAKE_CASE__						:     Dict  ,SCREAMING_SNAKE_CASE__						:     List[Any]):
								__lowerCamelCase							:							Any   			=					ViTMAEModel(config=A_)
								model.to(A_)
								model.eval()
								__lowerCamelCase							:							Optional[Any]   			=					model(A_)
								self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size))
							def        lowerCAmelCase	(						self						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     List[str]  ,SCREAMING_SNAKE_CASE__						:     str  ,SCREAMING_SNAKE_CASE__						:     str):
								__lowerCamelCase							:							Tuple   			=					ViTMAEForPreTraining(A_)
								model.to(A_)
								model.eval()
								__lowerCamelCase							:							List[str]   			=					model(A_)
								__lowerCamelCase							:							Any   			=					(self.image_size // self.patch_size) ** 2
								__lowerCamelCase							:							List[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
								__lowerCamelCase							:							str   			=					1
								__lowerCamelCase							:							Union[str, Any]   			=					ViTMAEForPreTraining(A_)
								model.to(A_)
								model.eval()
								__lowerCamelCase							:							Union[str, Any]   			=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								__lowerCamelCase							:							Union[str, Any]   			=					model(A_)
								__lowerCamelCase							:							List[str]   			=					self.patch_size**2
								self.parent.assertEqual(result.logits.shape  ,(self.batch_size, num_patches, expected_num_channels))


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






@require_torch
class 			A_						(			__snake_case     ,				__snake_case     ,				unittest.TestCase							):
							_UpperCAmelCase       :    Any		 = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							_UpperCAmelCase       :    int		 = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

							_UpperCAmelCase       :    Optional[Any]		 = False
							_UpperCAmelCase       :    Any		 = False
							_UpperCAmelCase       :    Optional[int]		 = False
							_UpperCAmelCase       :    Dict		 = False
							def        lowerCAmelCase	(						self						:     str):
								__lowerCamelCase							:							Union[str, Any]   			=					ViTMAEModelTester(self)
								__lowerCamelCase							:							Any   			=					ConfigTester(self  ,config_class=A_  ,has_text_modality=A_  ,hidden_size=3_7)
							def        lowerCAmelCase	(						self						:     Tuple):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='ViTMAE does not use inputs_embeds')
							def        lowerCAmelCase	(						self						:     List[str]):
								pass
							def        lowerCAmelCase	(						self						:     List[Any]):
								__lowerCamelCase							:							List[Any]   			=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase							:							Any   			=					model_class(A_)
									self.assertIsInstance(model.get_input_embeddings()  ,(nn.Module))
									__lowerCamelCase							:							Any   			=					model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A_  ,nn.Linear))
							def        lowerCAmelCase	(						self						:     Tuple):
								__lowerCamelCase							:							Optional[int]   			=					self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase							:							Optional[int]   			=					["pixel_values"]
									self.assertListEqual(arg_names[:1]  ,A_)
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								__lowerCamelCase							:							List[str]   			=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A_)
							def        lowerCAmelCase	(						self						:     str):
								__lowerCamelCase							:							Optional[int]   			=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*A_)
							def        lowerCAmelCase	(						self						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     List[Any]  ,SCREAMING_SNAKE_CASE__						:     Optional[int]  ,SCREAMING_SNAKE_CASE__						:     str):
								np.random.seed(2)

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

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

								super().check_pt_tf_models(A_  ,A_  ,A_)
							def        lowerCAmelCase	(						self						:     Dict):
								__lowerCamelCase							:							Tuple   			=					self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase							:							Any   			=					outputs[0].cpu().numpy()
									__lowerCamelCase							:							str   			=					0

									with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(A_)
										__lowerCamelCase							:							Optional[int]   			=					model_class.from_pretrained(A_)
										model.to(A_)
										# make random mask reproducible
										torch.manual_seed(2)
										with torch.no_grad():
											__lowerCamelCase							:							Tuple   			=					model(**self._prepare_for_class(A_  ,A_))

										# Make sure we don't have nans
										__lowerCamelCase							:							Dict   			=					after_outputs[0].cpu().numpy()
										__lowerCamelCase							:							int   			=					0
										__lowerCamelCase							:							str   			=					np.amax(np.abs(out_a - out_a))
										self.assertLessEqual(A_  ,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        lowerCAmelCase	(						self						:     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        lowerCAmelCase	(						self						:     Tuple):
								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        lowerCAmelCase	(						self						:     Dict):
								pass
							@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
							def        lowerCAmelCase	(						self						:     Any):
								pass
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def        lowerCAmelCase	(						self						:     Union[str, Any]):
								pass


							@slow
							def        lowerCAmelCase	(						self						:     Tuple):




								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase							:							List[str]   			=					ViTMAEModel.from_pretrained(A_)
									self.assertIsNotNone(A_)






def   SCREAMING_SNAKE_CASE__			(    )    ->	Optional[int]:
	__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        lowerCAmelCase	(						self						:     Optional[Any]):
								return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None


							@slow
							def        lowerCAmelCase	(						self						:     List[Any]):
								np.random.seed(2)

								__lowerCamelCase							:							Union[str, Any]   			=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(A_)

								__lowerCamelCase							:							Any   			=					self.default_image_processor
								__lowerCamelCase							:							Optional[Any]   			=					prepare_img()
								__lowerCamelCase							:							int   			=					image_processor(images=A_  ,return_tensors='pt').to(A_)

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

								# forward pass
								with torch.no_grad():
									__lowerCamelCase							:							Optional[Any]   			=					model(**A_  ,noise=torch.from_numpy(A_).to(device=A_))

								# verify the logits
								__lowerCamelCase							:							str   			=					torch.Size((1, 1_9_6, 7_6_8))
								self.assertEqual(outputs.logits.shape  ,A_)

								__lowerCamelCase							:							str   			=					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(A_)  ,atol=1E-4))











__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
