





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor



class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def __init__(self    :       List[str]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       int=7	,			UpperCAmelCase_    :       str=3	,			UpperCAmelCase_    :       List[str]=18	,			UpperCAmelCase_    :       Optional[int]=30	,			UpperCAmelCase_    :       Tuple=400	,			UpperCAmelCase_    :       Tuple=True	,			UpperCAmelCase_    :       Any=None	,			UpperCAmelCase_    :       int=True	,			UpperCAmelCase_    :       Optional[Any]=None	,			)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =size if size is not None else {"shortest_edge": 20}
   lowerCamelCase__:				Dict        =crop_size if crop_size is not None else {"height": 18, "width": 18}
   lowerCamelCase__:				List[str]        =parent
   lowerCamelCase__:				List[str]        =batch_size
   lowerCamelCase__:				str        =num_channels
   lowerCamelCase__:				str        =image_size
   lowerCamelCase__:				int        =min_resolution
   lowerCamelCase__:				int        =max_resolution
   lowerCamelCase__:				Dict        =do_resize
   lowerCamelCase__:				Dict        =size
   lowerCamelCase__:				List[str]        =do_center_crop
   lowerCamelCase__:				int        =crop_size







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



   '''simple docstring'''



   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
   }



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







 '''simple docstring'''
 lowercase_		=      MobileNetVaImageProcessor if is_vision_available() else None

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =MobileNetVaImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->List[Any]:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(UpperCAmelCase_	,			"do_resize"))
   self.assertTrue(hasattr(UpperCAmelCase_	,			"size"))
   self.assertTrue(hasattr(UpperCAmelCase_	,			"do_center_crop"))
   self.assertTrue(hasattr(UpperCAmelCase_	,			"crop_size"))

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.image_processing_class.from_dict(self.image_processor_dict)
   self.assertEqual(image_processor.size	,			{"shortest_edge": 20})
   self.assertEqual(image_processor.crop_size	,			{"height": 18, "width": 18})

   lowerCamelCase__:				Tuple        =self.image_processing_class.from_dict(self.image_processor_dict	,			size=42	,			crop_size=84)
   self.assertEqual(image_processor.size	,			{"shortest_edge": 42})
   self.assertEqual(image_processor.crop_size	,			{"height": 84, "width": 84})

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



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:				Any        =prepare_image_inputs(self.image_processor_tester	,			equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_	,			Image.Image)

   # Test not batched input
   lowerCamelCase__:				Dict        =image_processing(image_inputs[0]	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)

   # Test batched
   lowerCamelCase__:				Dict        =image_processing(UpperCAmelCase_	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)

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



   '''simple docstring'''



   lowerCamelCase__:				str        =self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:				Dict        =prepare_image_inputs(self.image_processor_tester	,			equal_resolution=UpperCAmelCase_	,			numpify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_	,			np.ndarray)

   # Test not batched input
   lowerCamelCase__:				Union[str, Any]        =image_processing(image_inputs[0]	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)

   # Test batched
   lowerCamelCase__:				List[Any]        =image_processing(UpperCAmelCase_	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)







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



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:				Tuple        =prepare_image_inputs(self.image_processor_tester	,			equal_resolution=UpperCAmelCase_	,			torchify=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_	,			torch.Tensor)

   # Test not batched input
   lowerCamelCase__:				Tuple        =image_processing(image_inputs[0]	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)

   # Test batched
   lowerCamelCase__:				Tuple        =image_processing(UpperCAmelCase_	,			return_tensors="pt").pixel_values
   self.assertEqual(
       encoded_images.shape	,			(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )	,			)








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()

