

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['image_processor', 'tokenizer']
			__lowerCamelCase       				=				'ViTImageProcessor'
			__lowerCamelCase       				=				('CLIPTokenizer', 'CLIPTokenizerFast')


			def __init__(			self					,					lowercase=None					,					lowercase=None					,					**lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."					,					lowercase					,					)
											A__						=	kwargs.pop("feature_extractor"				)

							A__						=	image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."				)
							if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."				)

							super().__init__(lowercase					,					lowercase				)


			def __call__(			self					,					lowercase=None					,					lowercase=None					,					lowercase=None					,					lowercase=None					,					**lowercase				)     ->  Any:
							'''simple docstring'''






							if text is None and visual_prompt is None and images is None:
											raise ValueError("You have to specify either text, visual prompt or images."				)

							if text is not None and visual_prompt is not None:
											raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

							if text is not None:
											A__						=	self.tokenizer(lowercase					,					return_tensors=lowercase					,					**lowercase				)

							if visual_prompt is not None:
											A__						=	self.image_processor(lowercase					,					return_tensors=lowercase					,					**lowercase				)

							if images is not None:
											A__						=	self.image_processor(lowercase					,					return_tensors=lowercase					,					**lowercase				)

							if visual_prompt is not None and images is not None:
											A__						=	{
											    "pixel_values": image_features.pixel_values,
											    "conditional_pixel_values": prompt_features.pixel_values,
											}
											return encoding
							elif text is not None and images is not None:
											A__						=	image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							elif visual_prompt is not None:
											A__						=	{
											    "conditional_pixel_values": prompt_features.pixel_values,
											}
											return encoding
							else:
											return BatchEncoding(data=dict(**lowercase				)					,					tensor_type=lowercase				)


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  List[str]:
							'''simple docstring'''






							return self.tokenizer.batch_decode(*lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					*lowercase					,					**lowercase				)     ->  Union[str, Any]:
							'''simple docstring'''






							return self.tokenizer.decode(*lowercase					,					**lowercase				)


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,					lowercase					,					)
							return self.image_processor_class


			@property
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,					lowercase					,					)
							return self.image_processor








'''simple docstring'''




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							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   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}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				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.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				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,
  )




