





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   ):
       # Initialise PyTorch model.
       # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
       # TapasConfig to False.

       # initialize configuration from json file
       _lowerCamelCase				:  int						=		TapasConfig.from_json_file(lowercase_   )
       # set absolute/relative position embeddings parameter
       _lowerCamelCase				:  List[Any]						=		reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              _lowerCamelCase				:  int						=		TapasForQuestionAnswering(config=lowercase_   )
       elif task == "WTQ":
              # run_task_main.py hparams
              _lowerCamelCase				:  Optional[int]						=		4
              _lowerCamelCase				:  Tuple						=		True
              # hparam_utils.py hparams
              _lowerCamelCase				:  Tuple						=		0.6_6_4_6_9_4
              _lowerCamelCase				:  List[Any]						=		0.2_0_7_9_5_1
              _lowerCamelCase				:  Dict						=		0.1_2_1_1_9_4
              _lowerCamelCase				:  Union[str, Any]						=		True
              _lowerCamelCase				:  Optional[Any]						=		True
              _lowerCamelCase				:  Tuple						=		False
              _lowerCamelCase				:  Any						=		0.0_3_5_2_5_1_3

              _lowerCamelCase				:  List[str]						=		TapasForQuestionAnswering(config=lowercase_   )
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              _lowerCamelCase				:  Tuple						=		4
              _lowerCamelCase				:  Optional[int]						=		False
              # hparam_utils.py hparams
              _lowerCamelCase				:  List[Any]						=		3_6.4_5_1_9
              _lowerCamelCase				:  int						=		0.9_0_3_4_2_1
              _lowerCamelCase				:  Dict						=		2_2_2.0_8_8
              _lowerCamelCase				:  Union[str, Any]						=		True
              _lowerCamelCase				:  Any						=		True
              _lowerCamelCase				:  Optional[Any]						=		True
              _lowerCamelCase				:  str						=		0.7_6_3_1_4_1

              _lowerCamelCase				:  Optional[Any]						=		TapasForQuestionAnswering(config=lowercase_   )
       elif task == "TABFACT":
              _lowerCamelCase				:  Optional[Any]						=		TapasForSequenceClassification(config=lowercase_   )
       elif task == "MLM":
              _lowerCamelCase				:  Optional[Any]						=		TapasForMaskedLM(config=lowercase_   )
       elif task == "INTERMEDIATE_PRETRAINING":
              _lowerCamelCase				:  Optional[int]						=		TapasModel(config=lowercase_   )
       else:
              raise ValueError(F"""Task {task} not supported."""   )

       print(F"""Building PyTorch model from configuration: {config}"""   )
       # Load weights from tf checkpoint
       load_tf_weights_in_tapas(lowercase_			,     lowercase_			,     lowercase_   )

       # Save pytorch-model (weights and configuration)
       print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowercase_   )

       # Save tokenizer files
       print(F"""Save tokenizer files to {pytorch_dump_path}"""   )
       _lowerCamelCase				:  Dict						=		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''			,     model_max_length=5_12   )
       tokenizer.save_pretrained(lowercase_   )

       print('''Used relative position embeddings:'''			,     model.config.reset_position_index_per_cell   )


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
       )
       parser.add_argument(
           '--reset_position_index_per_cell',
           default=False,
           action='store_true',
           help='Whether to use relative position embeddings or not. Defaults to True.',
       )
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--tapas_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained TAPAS 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.'
       )
       _lowerCamelCase      							=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.task,
           args.reset_position_index_per_cell,
           args.tf_checkpoint_path,
           args.tapas_config_file,
           args.pytorch_dump_path,
       )








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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )







@require_vision
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		tempfile.mkdtemp()

             _lowerCamelCase				:  List[str]						=		BlipImageProcessor()
             _lowerCamelCase				:  Union[str, Any]						=		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
             _lowerCamelCase				:  Any						=		BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')

             _lowerCamelCase				:  Any						=		InstructBlipProcessor(a__   ,				a__   ,				a__)

             processor.save_pretrained(self.tmpdirname)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).qformer_tokenizer


      def 					__snake_case      (		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 					__snake_case      (		self):



             """simple docstring"""





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

             _lowerCamelCase				:  List[str]						=		[Image.fromarray(np.moveaxis(a__   ,				0   ,				-1)) for x in image_inputs]

             return image_inputs


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   ,				qformer_tokenizer=self.get_qformer_tokenizer()   ,				)
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)''')
             _lowerCamelCase				:  str						=		self.get_image_processor(do_normalize=a__   ,				padding_value=1.0)

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor.from_pretrained(
                 self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=a__   ,				padding_value=1.0)

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

             self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string())
             self.assertIsInstance(processor.image_processor   ,				a__)
             self.assertIsInstance(processor.qformer_tokenizer   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Any						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Dict						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[str]						=		image_processor(a__   ,				return_tensors='''np''')
             _lowerCamelCase				:  List[Any]						=		processor(images=a__   ,				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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''

             _lowerCamelCase				:  Any						=		processor(text=a__)

             _lowerCamelCase				:  Optional[int]						=		tokenizer(a__   ,				return_token_type_ids=a__)
             _lowerCamelCase				:  Optional[Any]						=		qformer_tokenizer(a__   ,				return_token_type_ids=a__)

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

             for key in encoded_tokens_qformer.keys():
                    self.assertListEqual(encoded_tokens_qformer[key]   ,				encoded_processor['''qformer_''' + key])


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Optional[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  List[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''
             _lowerCamelCase				:  Tuple						=		self.prepare_image_inputs()

             _lowerCamelCase				:  Tuple						=		processor(text=a__   ,				images=a__)

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

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.get_image_processor()
             _lowerCamelCase				:  List[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  List[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase				:  Tuple						=		processor.batch_decode(a__)
             _lowerCamelCase				:  str						=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__   ,				a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  str						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  str						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  str						=		'''lower newer'''
             _lowerCamelCase				:  str						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[Any]						=		processor(text=a__   ,				images=a__)

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

