





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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =parent
   lowerCamelCase__:				str        =batch_size
   lowerCamelCase__:				Tuple        =image_size
   lowerCamelCase__:				List[str]        =patch_size
   lowerCamelCase__:				int        =num_channels
   lowerCamelCase__:				Optional[int]        =is_training
   lowerCamelCase__:				List[str]        =use_labels
   lowerCamelCase__:				Any        =hidden_size
   lowerCamelCase__:				Dict        =num_hidden_layers
   lowerCamelCase__:				str        =num_attention_heads
   lowerCamelCase__:				Optional[int]        =intermediate_size
   lowerCamelCase__:				Optional[Any]        =hidden_act
   lowerCamelCase__:				List[str]        =hidden_dropout_prob
   lowerCamelCase__:				List[str]        =attention_probs_dropout_prob
   lowerCamelCase__:				Any        =type_sequence_label_size
   lowerCamelCase__:				Tuple        =initializer_range
   lowerCamelCase__:				Tuple        =mask_ratio
   lowerCamelCase__:				List[str]        =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 							SCREAMING_SNAKE_CASE_ (self    :       str)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

   lowerCamelCase__:				List[str]        =self.get_config()

   return config, pixel_values, labels

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Union[str, Any]:



   '''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 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       Tuple)	->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[int])	->str:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =ViTMAEForPreTraining(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:				Dict        =model(UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =(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__:				int        =1
   lowerCamelCase__:				Dict        =ViTMAEForPreTraining(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:				Union[str, Any]        =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowerCamelCase__:				List[Any]        =model(UpperCAmelCase_)
   lowerCamelCase__:				int        =self.patch_size**2
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, num_patches, expected_num_channels))







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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.prepare_config_and_inputs()
   lowerCamelCase__:				Optional[int]        =config_and_inputs
   lowerCamelCase__:				int        ={'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class        _SCREAMING_SNAKE_CASE						(     __UpperCamelCase   ,							__UpperCamelCase   ,							unittest.TestCase		):







 '''simple docstring'''
 lowercase_		=      (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 lowercase_		=      {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

 lowercase_		=      False
 lowercase_		=      False
 lowercase_		=      False
 lowercase_		=      False

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



   '''simple docstring'''



   lowerCamelCase__:				str        =ViTMAEModelTester(self)
   lowerCamelCase__:				Optional[int]        =ConfigTester(self	,			config_class=UpperCAmelCase_	,			has_text_modality=UpperCAmelCase_	,			hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="ViTMAE does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->List[str]:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:				List[str]        =model_class(UpperCAmelCase_)
     self.assertIsInstance(model.get_input_embeddings()	,			(nn.Module))
     lowerCamelCase__:				int        =model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(UpperCAmelCase_	,			nn.Linear))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->str:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.model_tester.prepare_config_and_inputs_for_common()

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

     lowerCamelCase__:				int        =['pixel_values']
     self.assertListEqual(arg_names[:1]	,			UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Union[str, Any]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->Any:



   '''simple docstring'''



   np.random.seed(2)

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

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

   super().check_pt_tf_models(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:				int        =model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.eval()
     # make random mask reproducible
     torch.manual_seed(2)
     with torch.no_grad():
       lowerCamelCase__:				Any        =model(**self._prepare_for_class(UpperCAmelCase_	,			UpperCAmelCase_))

     lowerCamelCase__:				Tuple        =outputs[0].cpu().numpy()
     lowerCamelCase__:				List[str]        =0

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

       # Make sure we don't have nans
       lowerCamelCase__:				str        =after_outputs[0].cpu().numpy()
       lowerCamelCase__:				Optional[Any]        =0
       lowerCamelCase__:				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\n    to get deterministic results.")
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Tuple:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->int:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->str:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   pass







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:				int        =ViTMAEModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)





def 					lowerCAmelCase_			(	)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Optional[int]        =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






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







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->str:



   '''simple docstring'''



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







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Union[str, Any]:



   '''simple docstring'''



   np.random.seed(2)

   lowerCamelCase__:				List[Any]        =ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(UpperCAmelCase_)

   lowerCamelCase__:				Dict        =self.default_image_processor
   lowerCamelCase__:				Dict        =prepare_img()
   lowerCamelCase__:				Optional[int]        =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)
   lowerCamelCase__:				Optional[int]        =ViTMAEConfig()
   lowerCamelCase__:				List[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(**UpperCAmelCase_	,			noise=torch.from_numpy(UpperCAmelCase_).to(device=UpperCAmelCase_))

   # verify the logits
   lowerCamelCase__:				Union[str, Any]        =torch.Size((1, 196, 768))
   self.assertEqual(outputs.logits.shape	,			UpperCAmelCase_)

   lowerCamelCase__:				Tuple        =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))








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				int        =("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  lowerCamelCase__:				Any        =(
      ("layer.", "layer_"),
      ("word_embeddings.weight", "word_embeddings"),
      ("position_embeddings.weight", "position_embeddings"),
      ("token_type_embeddings.weight", "token_type_embeddings"),
      (".", "/"),
      ("LayerNorm/weight", "LayerNorm/gamma"),
      ("LayerNorm/bias", "LayerNorm/beta"),
      ("weight", "kernel"),
  )

  if not os.path.isdir(__a		):
    os.makedirs(__a		)

  lowerCamelCase__:				Tuple        =model.state_dict()

  def to_tf_var_name(__a		):
    for patt, repl in iter(__a		):
      lowerCamelCase__:				Tuple        =name.replace(__a     ,   __a		)
    return F"""bert/{name}"""

  def create_tf_var(__a     ,   __a     ,   __a		):
    lowerCamelCase__:				Union[str, Any]        =tf.dtypes.as_dtype(tensor.dtype		)
    lowerCamelCase__:				Any        =tf.get_variable(dtype=__a     ,   shape=tensor.shape     ,   name=__a     ,   initializer=tf.zeros_initializer()		)
    session.run(tf.variables_initializer([tf_var]		)		)
    session.run(__a		)
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowerCamelCase__:				Tuple        =to_tf_var_name(__a		)
      lowerCamelCase__:				str        =state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        lowerCamelCase__:				List[Any]        =torch_tensor.T
      lowerCamelCase__:				Optional[int]        =create_tf_var(tensor=__a     ,   name=__a     ,   session=__a		)
      tf.keras.backend.set_value(__a     ,   __a		)
      lowerCamelCase__:				Union[str, Any]        =session.run(__a		)
      print(F"""Successfully created {tf_name}: {np.allclose(__a     ,   __a		)}"""		)

    lowerCamelCase__:				List[Any]        =tf.train.Saver(tf.trainable_variables()		)
    saver.save(__a     ,   os.path.join(__a     ,   model_name.replace("-"     ,   "_"		) + ".ckpt"		)		)





def 					lowerCAmelCase_			(	__a=None		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =argparse.ArgumentParser()
  parser.add_argument("--model_name"     ,   type=__a     ,   required=__a     ,   help="model name e.g. bert-base-uncased"		)
  parser.add_argument(
      "--cache_dir"     ,   type=__a     ,   default=__a     ,   required=__a     ,   help="Directory containing pytorch model"		)
  parser.add_argument("--pytorch_model_path"     ,   type=__a     ,   required=__a     ,   help="/path/to/<pytorch-model-name>.bin"		)
  parser.add_argument("--tf_cache_dir"     ,   type=__a     ,   required=__a     ,   help="Directory in which to save tensorflow model"		)
  lowerCamelCase__:				Optional[Any]        =parser.parse_args(__a		)

  lowerCamelCase__:				Dict        =BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name     ,   state_dict=torch.load(args.pytorch_model_path		)     ,   cache_dir=args.cache_dir     ,   )

  convert_pytorch_checkpoint_to_tf(model=__a     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name		)


if __name__ == "__main__":
     main()

