"""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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  a__:     str										=	TapasConfig.from_json_file(UpperCamelCase__   )
  # set absolute/relative position embeddings parameter
  a__:     Tuple										=	reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a__:     Optional[Any]										=	TapasForQuestionAnswering(config=UpperCamelCase__   )
  elif task == "WTQ":
    # run_task_main.py hparams
    a__:     Optional[int]										=	4
    a__:     Any										=	True
    # hparam_utils.py hparams
    a__:     Optional[int]										=	0.664_694
    a__:     List[str]										=	0.207_951
    a__:     List[str]										=	0.121_194
    a__:     int										=	True
    a__:     Dict										=	True
    a__:     List[str]										=	False
    a__:     Union[str, Any]										=	0.0_352_513

    a__:     Union[str, Any]										=	TapasForQuestionAnswering(config=UpperCamelCase__   )
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a__:     Union[str, Any]										=	4
    a__:     Optional[Any]										=	False
    # hparam_utils.py hparams
    a__:     int										=	36.4_519
    a__:     str										=	0.903_421
    a__:     Optional[Any]										=	222.088
    a__:     Dict										=	True
    a__:     Union[str, Any]										=	True
    a__:     Union[str, Any]										=	True
    a__:     List[str]										=	0.763_141

    a__:     str										=	TapasForQuestionAnswering(config=UpperCamelCase__   )
  elif task == "TABFACT":
    a__:     Tuple										=	TapasForSequenceClassification(config=UpperCamelCase__   )
  elif task == "MLM":
    a__:     int										=	TapasForMaskedLM(config=UpperCamelCase__   )
  elif task == "INTERMEDIATE_PRETRAINING":
    a__:     Tuple										=	TapasModel(config=UpperCamelCase__   )
  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(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__   )

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

  # Save tokenizer files
  print(F'Save tokenizer files to {pytorch_dump_path}'   )
  a__:     int										=	TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'		,     model_max_length=512   )
  tokenizer.save_pretrained(UpperCamelCase__   )

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


if __name__ == "__main__":
 lowercase__					       =	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.'
 )
 lowercase__					       =	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 argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any=1024      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase       ,						__lowerCamelCase		       =    [], []

				__lowerCamelCase		       =    list(zip(UpperCamelCase__ ,     UpperCamelCase__      )      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    sorted_examples[0]

				def is_too_big(UpperCamelCase__				:      List[str]      ):
								return tok(UpperCamelCase__ ,     return_tensors='pt'      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								__lowerCamelCase		       =    new_src + ' ' + src
								__lowerCamelCase		       =    new_tgt + ' ' + tgt
								if is_too_big(UpperCamelCase__      ) or is_too_big(UpperCamelCase__      ):  # cant fit, finalize example
												finished_src.append(UpperCamelCase__      )
												finished_tgt.append(UpperCamelCase__      )
												__lowerCamelCase       ,						__lowerCamelCase		       =    src, tgt
								else:  # can fit, keep adding
												__lowerCamelCase       ,						__lowerCamelCase		       =    cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(UpperCamelCase__      )
								finished_tgt.append(UpperCamelCase__      )
				return finished_src, finished_tgt







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      Path ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    Path(UpperCamelCase__      )
				save_path.mkdir(exist_ok=UpperCamelCase__      )
				for split in ["train"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase		       =    [x.rstrip() for x in Path(UpperCamelCase__      ).open().readlines()]
								__lowerCamelCase       ,						__lowerCamelCase		       =    pack_examples(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""packed {split} split from {len(UpperCamelCase__      )} examples -> {len(UpperCamelCase__      )}."""      )
								Path(save_path / F"""{split}.source"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
								Path(save_path / F"""{split}.target"""      ).open('w'      ).write('\n'.join(UpperCamelCase__      )      )
				for split in ["val", "test"]:
								__lowerCamelCase       ,						__lowerCamelCase		       =    data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.source"""      )
								shutil.copyfile(UpperCamelCase__ ,     save_path / F"""{split}.target"""      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser()
				parser.add_argument('--tok_name' ,     type=UpperCamelCase__ ,     help='like facebook/bart-large-cnn,t5-base, etc.'      )
				parser.add_argument('--max_seq_len' ,     type=UpperCamelCase__ ,     default=128      )
				parser.add_argument('--data_dir' ,     type=UpperCamelCase__      )
				parser.add_argument('--save_path' ,     type=UpperCamelCase__      )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(UpperCamelCase__ ,     Path(args.data_dir      ) ,     args.max_seq_len ,     args.save_path      )


if __name__ == "__main__":
					packer_cli()


