

"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 __magic_name__     =      ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

 __magic_name__     =      (
     ('''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(UpperCamelCase__						):
  os.makedirs(UpperCamelCase__						)

 __magic_name__     =      model.state_dict()

 def to_tf_var_name(__UpperCamelCase						):
  for patt, repl in iter(UpperCamelCase__						):
   __magic_name__     =      name.replace(UpperCamelCase__      ,   UpperCamelCase__						)
  return f'''bert/{name}'''

 def create_tf_var(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						):
  __magic_name__     =      tf.dtypes.as_dtype(tensor.dtype						)
  __magic_name__     =      tf.get_variable(dtype=UpperCamelCase__      ,   shape=tensor.shape      ,   name=UpperCamelCase__      ,   initializer=tf.zeros_initializer()						)
  session.run(tf.variables_initializer([tf_var]						)						)
  session.run(UpperCamelCase__						)
  return tf_var

 tf.reset_default_graph()
 with tf.Session() as session:
  for var_name in state_dict:
   __magic_name__     =      to_tf_var_name(UpperCamelCase__						)
   __magic_name__     =      state_dict[var_name].numpy()
   if any(x in var_name for x in tensors_to_transpose						):
    __magic_name__     =      torch_tensor.T
   __magic_name__     =      create_tf_var(tensor=UpperCamelCase__      ,   name=UpperCamelCase__      ,   session=UpperCamelCase__						)
   tf.keras.backend.set_value(UpperCamelCase__      ,   UpperCamelCase__						)
   __magic_name__     =      session.run(UpperCamelCase__						)
   print(f'''Successfully created {tf_name}: {np.allclose(UpperCamelCase__      ,   UpperCamelCase__						)}'''						)

  __magic_name__     =      tf.train.Saver(tf.trainable_variables()						)
  saver.save(UpperCamelCase__      ,   os.path.join(UpperCamelCase__      ,   model_name.replace('''-'''      ,   '''_'''						) + '''.ckpt'''						)						)





def 	lowercase      (   __UpperCamelCase=None						) ->      List[str]:
 __magic_name__     =      argparse.ArgumentParser()
 parser.add_argument('''--model_name'''      ,   type=UpperCamelCase__      ,   required=UpperCamelCase__      ,   help='''model name e.g. bert-base-uncased'''						)
 parser.add_argument(
     '''--cache_dir'''      ,   type=UpperCamelCase__      ,   default=UpperCamelCase__      ,   required=UpperCamelCase__      ,   help='''Directory containing pytorch model'''						)
 parser.add_argument('''--pytorch_model_path'''      ,   type=UpperCamelCase__      ,   required=UpperCamelCase__      ,   help='''/path/to/<pytorch-model-name>.bin'''						)
 parser.add_argument('''--tf_cache_dir'''      ,   type=UpperCamelCase__      ,   required=UpperCamelCase__      ,   help='''Directory in which to save tensorflow model'''						)
 __magic_name__     =      parser.parse_args(UpperCamelCase__						)

 __magic_name__     =      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=UpperCamelCase__      ,   ckpt_dir=args.tf_cache_dir      ,   model_name=args.model_name						)


if __name__ == "__main__":
 main()




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tempfile.mkdtemp()

      # fmt: off
      __magic_name__     =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

      __magic_name__     =      {
          '''do_resize''': True,
          '''size''': {'''height''': 18, '''width''': 18},
          '''do_normalize''': True,
          '''image_mean''': [0.5, 0.5, 0.5],
          '''image_std''': [0.5, 0.5, 0.5],
      }
      __magic_name__     =      os.path.join(self.tmpdirname ,						UpperCamelCase_       )
      with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''       ) as fp:
       json.dump(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return BertTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      shutil.rmtree(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta       )]

      __magic_name__     =      [Image.fromarray(np.moveaxis(UpperCamelCase_ ,						0 ,						-1       )       ) for x in image_inputs]

      return image_inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_image_processor()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      processor.save_pretrained(self.tmpdirname       )
      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      __magic_name__     =      self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''       )
      __magic_name__     =      self.get_image_processor(do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      __magic_name__     =      VisionTextDualEncoderProcessor.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 ,						(BertTokenizer, BertTokenizerFast)       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      image_processor(UpperCamelCase_ ,						return_tensors='''np'''       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''

      __magic_name__     =      processor(text=UpperCamelCase_       )

      __magic_name__     =      tokenizer(UpperCamelCase_       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

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

      # test if it raises when no input is passed
      with self.assertRaises(UpperCamelCase_       ):
       processor()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __magic_name__     =      processor.batch_decode(UpperCamelCase_       )
      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_       )

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						processor.model_input_names       )

