





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        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()

        # fmt: off
        A__		   =		['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
        # fmt: on
        A__		   =		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]					)					)

        A__		   =		{
            '''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],
        }
        A__		   =		os.path.join(self.tmpdirname,__lowerCamelCase					)
        with open(self.image_processor_file,'''w''',encoding='''utf-8'''					) as fp:
               json.dump(__lowerCamelCase,__lowerCamelCase					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return BertTokenizer.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return ViTImageProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					)



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



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

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

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_tokenizer()
        A__		   =		self.get_image_processor()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        processor.save_pretrained(self.tmpdirname					)
        A__		   =		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,__lowerCamelCase					)



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

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

        A__		   =		VisionTextDualEncoderProcessor.from_pretrained(
            self.tmpdirname,bos_token='''(BOS)''',eos_token='''(EOS)''',do_normalize=__lowerCamelCase,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,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,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 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

        A__		   =		'''lower newer'''

        A__		   =		processor(text=__lowerCamelCase					)

        A__		   =		tokenizer(__lowerCamelCase					)

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



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

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

        A__		   =		processor(text=__lowerCamelCase,images=__lowerCamelCase					)

        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(__lowerCamelCase					):
               processor()



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

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

        A__		   =		processor.batch_decode(__lowerCamelCase					)
        A__		   =		tokenizer.batch_decode(__lowerCamelCase					)

        self.assertListEqual(__lowerCamelCase,__lowerCamelCase					)




 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()
        A__		   =		self.get_tokenizer()

        A__		   =		VisionTextDualEncoderProcessor(tokenizer=__lowerCamelCase,image_processor=__lowerCamelCase					)

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

        A__		   =		processor(text=__lowerCamelCase,images=__lowerCamelCase					)

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












import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_big_bird(UpperCamelCase__					,	UpperCamelCase__					,	is_trivia_qa=UpperCamelCase__    )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"    )
       model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						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(
           '--big_bird_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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.'
       )
       parser.add_argument(
           '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





