





'''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,
  )











def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


if __name__ == "__main__":
					print(F'''{solution() = }''')



