
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   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					Tuple       ):
       # 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
       snake_case			:  Any     							= TapasConfig.from_json_file(__lowerCamelCase       )
       # set absolute/relative position embeddings parameter
       snake_case			:  Dict     							= reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              snake_case			:  int     							= TapasForQuestionAnswering(config=__lowerCamelCase       )
       elif task == "WTQ":
              # run_task_main.py hparams
              snake_case			:  Tuple     							= 4
              snake_case			:  Optional[int]     							= True
              # hparam_utils.py hparams
              snake_case			:  Union[str, Any]     							= 0.66_4694
              snake_case			:  Any     							= 0.20_7951
              snake_case			:  Any     							= 0.12_1194
              snake_case			:  List[Any]     							= True
              snake_case			:  Tuple     							= True
              snake_case			:  int     							= False
              snake_case			:  Any     							= 0.035_2513

              snake_case			:  str     							= TapasForQuestionAnswering(config=__lowerCamelCase       )
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              snake_case			:  Any     							= 4
              snake_case			:  List[Any]     							= False
              # hparam_utils.py hparams
              snake_case			:  Dict     							= 36.4519
              snake_case			:  List[Any]     							= 0.90_3421
              snake_case			:  Optional[Any]     							= 222.088
              snake_case			:  List[Any]     							= True
              snake_case			:  Optional[int]     							= True
              snake_case			:  Optional[Any]     							= True
              snake_case			:  Tuple     							= 0.76_3141

              snake_case			:  Optional[int]     							= TapasForQuestionAnswering(config=__lowerCamelCase       )
       elif task == "TABFACT":
              snake_case			:  Any     							= TapasForSequenceClassification(config=__lowerCamelCase       )
       elif task == "MLM":
              snake_case			:  Optional[Any]     							= TapasForMaskedLM(config=__lowerCamelCase       )
       elif task == "INTERMEDIATE_PRETRAINING":
              snake_case			:  Optional[Any]     							= TapasModel(config=__lowerCamelCase       )
       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(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

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

       # Save tokenizer files
       print(f"""Save tokenizer files to {pytorch_dump_path}"""       )
       snake_case			:  List[str]     							= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,   model_max_length=512       )
       tokenizer.save_pretrained(__lowerCamelCase       )

       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,
   )







from math import factorial


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					float       ):
       if successes > trials:
              raise ValueError("successes must be lower or equal to trials"       )
       if trials < 0 or successes < 0:
              raise ValueError("the function is defined for non-negative integers"       )
       if not isinstance(__lowerCamelCase							,   __lowerCamelCase       ) or not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              raise ValueError("the function is defined for non-negative integers"       )
       if not 0 < prob < 1:
              raise ValueError("prob has to be in range of 1 - 0"       )
       snake_case			:  Tuple     							= (prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case			:  str     							= float(factorial(__lowerCamelCase       )       )
       coefficient /= factorial(__lowerCamelCase       ) * factorial(trials - successes       )
       return probability * coefficient


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print("""Probability of 2 successes out of 4 trails""")
   print("""with probability of 0.75 is:""", end=""" """)
   print(binomial_distribution(2, 4, 0.75))





