





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =tempfile.mkdtemp()

   lowerCamelCase__:				Union[str, Any]        =BlipImageProcessor()
   lowerCamelCase__:				Union[str, Any]        =GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")

   lowerCamelCase__:				Optional[int]        =BlipaProcessor(UpperCAmelCase_	,			UpperCAmelCase_)

   processor.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			**UpperCAmelCase_    :       Optional[Any])	->int:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).tokenizer

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			**UpperCAmelCase_    :       Optional[int])	->List[str]:



   '''simple docstring'''



   return AutoProcessor.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_).image_processor

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



   '''simple docstring'''



   shutil.rmtree(self.tmpdirname)

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =[np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta)]

   lowerCamelCase__:				Optional[int]        =[Image.fromarray(np.moveaxis(UpperCAmelCase_	,			0	,			-1)) for x in image_inputs]

   return image_inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =BlipaProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor())
   processor.save_pretrained(self.tmpdirname)

   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)")
   lowerCamelCase__:				List[Any]        =self.get_image_processor(do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   lowerCamelCase__:				Tuple        =BlipaProcessor.from_pretrained(
       self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=UpperCAmelCase_	,			padding_value=1.0)

   self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab())
   self.assertIsInstance(processor.tokenizer	,			UpperCAmelCase_)

   self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string())
   self.assertIsInstance(processor.image_processor	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.get_image_processor()
   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer()

   lowerCamelCase__:				Any        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =self.prepare_image_inputs()

   lowerCamelCase__:				List[str]        =image_processor(UpperCAmelCase_	,			return_tensors="np")
   lowerCamelCase__:				Union[str, Any]        =processor(images=UpperCAmelCase_	,			return_tensors="np")

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1E-2)

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



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.get_image_processor()
   lowerCamelCase__:				str        =self.get_tokenizer()

   lowerCamelCase__:				Optional[Any]        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        ="lower newer"

   lowerCamelCase__:				Union[str, Any]        =processor(text=UpperCAmelCase_)

   lowerCamelCase__:				str        =tokenizer(UpperCAmelCase_	,			return_token_type_ids=UpperCAmelCase_)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,			encoded_processor[key])

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



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =self.get_image_processor()
   lowerCamelCase__:				Dict        =self.get_tokenizer()

   lowerCamelCase__:				str        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Tuple        ="lower newer"
   lowerCamelCase__:				str        =self.prepare_image_inputs()

   lowerCamelCase__:				Tuple        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

   self.assertListEqual(list(inputs.keys())	,			["pixel_values", "input_ids", "attention_mask"])

   # test if it raises when no input is passed
   with pytest.raises(UpperCAmelCase_):
     processor()

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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_image_processor()
   lowerCamelCase__:				Union[str, Any]        =self.get_tokenizer()

   lowerCamelCase__:				Tuple        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				Dict        =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   lowerCamelCase__:				List[Any]        =processor.batch_decode(UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =tokenizer.batch_decode(UpperCAmelCase_)

   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.get_image_processor()
   lowerCamelCase__:				List[Any]        =self.get_tokenizer()

   lowerCamelCase__:				List[str]        =BlipaProcessor(tokenizer=UpperCAmelCase_	,			image_processor=UpperCAmelCase_)

   lowerCamelCase__:				List[str]        ="lower newer"
   lowerCamelCase__:				Optional[int]        =self.prepare_image_inputs()

   lowerCamelCase__:				Tuple        =processor(text=UpperCAmelCase_	,			images=UpperCAmelCase_)

   # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
   self.assertListEqual(list(inputs.keys())	,			["pixel_values", "input_ids", "attention_mask"])








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()

