


'''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    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=13    ,   UpperCamelCase_=30    ,   UpperCamelCase_=2    ,   UpperCamelCase_=3    ,   UpperCamelCase_=True    ,   UpperCamelCase_=True    ,   UpperCamelCase_=32    ,   UpperCamelCase_=5    ,   UpperCamelCase_=4    ,   UpperCamelCase_=37    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=10    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=3    ,   UpperCamelCase_=0.6    ,   UpperCamelCase_=None    ,   ):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :int												= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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

							return config, pixel_values, labels




		def     lowerCAmelCase__				(		self							):

							'''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=UpperCamelCase_    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= ViTMAEForPreTraining(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :Optional[Any]												= model(UpperCamelCase_							)
							UpperCamelCase__   :str												= (self.image_size // self.patch_size) ** 2
							UpperCamelCase__   :List[Any]												= self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.prepare_config_and_inputs()
							UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :int												= config_and_inputs
							UpperCamelCase__   :str												= {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class    lowercase	(							A__	,		A__	,		unittest.TestCase					):

		"""simple docstring"""

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

		_a     =				False
		_a     =				False
		_a     =				False
		_a     =				False




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= ViTMAEModelTester(self							)
							UpperCamelCase__   :Optional[int]												= ConfigTester(self    ,   config_class=UpperCamelCase_    ,   has_text_modality=UpperCamelCase_    ,   hidden_size=37							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							self.config_tester.run_common_tests()




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

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Any												= self.model_tester.prepare_config_and_inputs_for_common()

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase__   :Dict												= ['''pixel_values''']
												self.assertListEqual(arg_names[:1]    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							np.random.seed(2							)

							UpperCamelCase__   :Optional[int]												= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
							UpperCamelCase__   :List[Any]												= np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
							UpperCamelCase__   :int												= torch.from_numpy(UpperCamelCase_							)

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

							super().check_pt_tf_models(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :List[str]												= self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase__   :Optional[Any]												= outputs[0].cpu().numpy()
												UpperCamelCase__   :Union[str, Any]												= 0

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

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

							'''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     lowerCAmelCase__				(		self							):

							'''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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__   :Any												= ViTMAEModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)



def 			a      (			)  ->	Optional[int]:



					'''simple docstring'''






					UpperCamelCase__   :int												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
					return image







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

		"""simple docstring"""

		@cached_property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							np.random.seed(2							)

							UpperCamelCase__   :Tuple												= ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''							).to(UpperCamelCase_							)

							UpperCamelCase__   :Tuple												= self.default_image_processor
							UpperCamelCase__   :Dict												= prepare_img()
							UpperCamelCase__   :Tuple												= image_processor(images=UpperCamelCase_    ,   return_tensors='''pt'''							).to(UpperCamelCase_							)

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

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

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

							UpperCamelCase__   :Dict												= 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(UpperCamelCase_							)    ,   atol=1e-4							)							)

"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   _A			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


