

'''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 lowerCamelCase					:



			'''simple docstring'''






			def __init__( self			:		Any						,     lowerCAmelCase_			:		Any						,     lowerCAmelCase_			:		Any=13						,     lowerCAmelCase_			:		Tuple=30						,     lowerCAmelCase_			:		Optional[Any]=2						,     lowerCAmelCase_			:		Optional[int]=3						,     lowerCAmelCase_			:		Dict=True						,     lowerCAmelCase_			:		Optional[Any]=True						,     lowerCAmelCase_			:		Optional[Any]=32						,     lowerCAmelCase_			:		str=5						,     lowerCAmelCase_			:		Tuple=4						,     lowerCAmelCase_			:		List[Any]=37						,     lowerCAmelCase_			:		Dict="gelu"						,     lowerCAmelCase_			:		str=0.1						,     lowerCAmelCase_			:		Dict=0.1						,     lowerCAmelCase_			:		Dict=10						,     lowerCAmelCase_			:		Any=0.02						,     lowerCAmelCase_			:		Union[str, Any]=3						,     lowerCAmelCase_			:		Any=0.6						,     lowerCAmelCase_			:		int=None						,     )     ->						List[str]:





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


			def lowercase__		( self			:		int					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  List[Any]           =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

									A__	:  str           =self.get_config()

									return config, pixel_values, labels


			def lowercase__		( self			:		Dict					)     ->						str:





									'''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 lowercase__		( self			:		List[str]						,     lowerCAmelCase_			:		Any						,     lowerCAmelCase_			:		Tuple						,     lowerCAmelCase_			:		List[str]					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  List[str]           =ViTMAEModel(config=__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									A__	:  Union[str, Any]           =model(__lowerCamelCase					)
									self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)					)


			def lowercase__		( self			:		Optional[Any]						,     lowerCAmelCase_			:		List[Any]						,     lowerCAmelCase_			:		Any						,     lowerCAmelCase_			:		Tuple					)     ->						List[str]:





									'''simple docstring'''
									A__	:  str           =ViTMAEForPreTraining(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									A__	:  int           =model(__lowerCamelCase					)
									A__	:  Dict           =(self.image_size // self.patch_size) ** 2
									A__	:  Tuple           =self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, num_patches, expected_num_channels)					)

									# test greyscale images
									A__	:  Dict           =1
									A__	:  List[Any]           =ViTMAEForPreTraining(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									A__	:  int           =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
									A__	:  Optional[Any]           =model(__lowerCamelCase					)
									A__	:  Optional[Any]           =self.patch_size**2
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, num_patches, expected_num_channels)					)



			def lowercase__		( self			:		List[str]					)     ->						int:





									'''simple docstring'''
									A__	:  List[str]           =self.prepare_config_and_inputs()
									A__       ,		A__       ,		A__	:  Union[str, Any]           =config_and_inputs
									A__	:  int           ={"""pixel_values""": pixel_values}
									return config, inputs_dict



@require_torch
class lowerCamelCase					(    __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		unittest.TestCase		):



			'''simple docstring'''






			__snake_case             =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			__snake_case             =			{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

			__snake_case             =			False
			__snake_case             =			False
			__snake_case             =			False
			__snake_case             =			False


			def lowercase__		( self			:		Tuple					)     ->						Optional[Any]:





									'''simple docstring'''
									A__	:  Union[str, Any]           =ViTMAEModelTester(self					)
									A__	:  Tuple           =ConfigTester(self						,     config_class=__lowerCamelCase						,     has_text_modality=__lowerCamelCase						,     hidden_size=37					)


			def lowercase__		( self			:		Optional[Any]					)     ->						int:





									'''simple docstring'''
									self.config_tester.run_common_tests()


			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
			def lowercase__		( self			:		Dict					)     ->						Dict:





									'''simple docstring'''
									pass


			def lowercase__		( self			:		Optional[int]					)     ->						str:





									'''simple docstring'''
									A__       ,		A__	:  int           =self.model_tester.prepare_config_and_inputs_for_common()

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


			def lowercase__		( self			:		List[Any]					)     ->						str:





									'''simple docstring'''
									A__       ,		A__	:  List[Any]           =self.model_tester.prepare_config_and_inputs_for_common()

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

															A__	:  Union[str, Any]           =["""pixel_values"""]
															self.assertListEqual(arg_names[:1]						,     __lowerCamelCase					)


			def lowercase__		( self			:		int					)     ->						List[str]:





									'''simple docstring'''
									A__	:  Optional[Any]           =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase					)


			def lowercase__		( self			:		Tuple					)     ->						Union[str, Any]:





									'''simple docstring'''
									A__	:  Any           =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase					)


			def lowercase__		( self			:		List[str]						,     lowerCAmelCase_			:		str						,     lowerCAmelCase_			:		Optional[int]						,     lowerCAmelCase_			:		Union[str, Any]					)     ->						int:





									'''simple docstring'''
									np.random.seed(2					)

									A__	:  int           =int((pt_model.config.image_size // pt_model.config.patch_size) ** 2					)
									A__	:  Any           =np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
									A__	:  Dict           =torch.from_numpy(__lowerCamelCase					)

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

									super().check_pt_tf_models(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase					)


			def lowercase__		( self			:		List[Any]					)     ->						str:





									'''simple docstring'''
									A__       ,		A__	:  Optional[Any]           =self.model_tester.prepare_config_and_inputs_for_common()

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

															A__	:  int           =outputs[0].cpu().numpy()
															A__	:  Union[str, Any]           =0

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

																					# Make sure we don't have nans
																					A__	:  List[str]           =after_outputs[0].cpu().numpy()
																					A__	:  List[str]           =0
																					A__	:  Dict           =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 lowercase__		( self			:		Tuple					)     ->						List[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 lowercase__		( self			:		Any					)     ->						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 lowercase__		( self			:		Union[str, Any]					)     ->						int:





									'''simple docstring'''
									pass


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





									'''simple docstring'''
									pass


			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
			def lowercase__		( self			:		Union[str, Any]					)     ->						int:





									'''simple docstring'''
									pass



			@slow
			def lowercase__		( self			:		Dict					)     ->						Optional[int]:





									'''simple docstring'''
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															A__	:  List[str]           =ViTMAEModel.from_pretrained(__lowerCamelCase					)
															self.assertIsNotNone(__lowerCamelCase					)


def 		__lowerCamelCase			(  )			->				List[Any]:






						"""simple docstring"""





						A__	:  List[str]           =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image



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



			'''simple docstring'''






			@cached_property
			def lowercase__		( self			:		Optional[int]					)     ->						List[Any]:





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



			@slow
			def lowercase__		( self			:		Any					)     ->						List[Any]:





									'''simple docstring'''
									np.random.seed(2					)

									A__	:  List[str]           =ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					).to(__lowerCamelCase					)

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

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

									# verify the logits
									A__	:  Optional[Any]           =torch.Size((1, 1_96, 7_68)					)
									self.assertEqual(outputs.logits.shape						,     __lowerCamelCase					)

									A__	:  int           =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(__lowerCamelCase					)						,     atol=1e-4					)					)






"""simple docstring"""






import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
     import jax.numpy as jnp

     from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):



   @slow
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      AutoTokenizer.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      '''The dog is cute and lives in the garden house'''
     _snake_case   	=      jnp.array([tokenizer.encode(__lowerCamelCase				)]				)

     _snake_case   	=      (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
     _snake_case   	=      jnp.array(
         [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]				)

     _snake_case   	=      model(__lowerCamelCase				)['''last_hidden_state''']
     self.assertEqual(output.shape							,       __lowerCamelCase				)
     # compare the actual values for a slice of last dim
     self.assertTrue(jnp.allclose(output[:, :, -1]							,       __lowerCamelCase							,       atol=1E-3				)				)





