





import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def __init__(    self			:		List[Any] ,  *lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any=None ,  lowerCAmelCase_			:		int=None ,  **lowerCAmelCase_			:		str					)							->						Tuple:
     super().__init__(*lowerCAmelCase_ ,  **lowerCAmelCase_					)
     __lowerCAmelCase							     =     eval_examples
     __lowerCAmelCase							     =     post_process_function






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		Tuple=None ,  lowerCAmelCase_			:		Tuple=None ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		str = "eval"					)							->						List[str]:
     __lowerCAmelCase							     =     self.eval_dataset if eval_dataset is None else eval_dataset
     __lowerCAmelCase							     =     self.get_eval_dataloader(lowerCAmelCase_					)
     __lowerCAmelCase							     =     self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     __lowerCAmelCase							     =     self.compute_metrics
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     __lowerCAmelCase							     =     time.time()
     try:
        __lowerCAmelCase							     =     eval_loop(
            lowerCAmelCase_ ,  description='Evaluation' ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=lowerCAmelCase_ ,  metric_key_prefix=lowerCAmelCase_ ,  )
     finally:
        __lowerCAmelCase							     =     compute_metrics
     __lowerCAmelCase							     =     self.args.eval_batch_size * self.args.world_size
     if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
        start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
     output.metrics.update(
         speed_metrics(
             lowerCAmelCase_ ,  lowerCAmelCase_ ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size					) ,  )					)
     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        __lowerCAmelCase							     =     self.post_process_function(lowerCAmelCase_ ,  lowerCAmelCase_ ,  output.predictions					)
        __lowerCAmelCase							     =     self.compute_metrics(lowerCAmelCase_					)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()					):
           if not key.startswith(f"""{metric_key_prefix}_"""					):
              __lowerCAmelCase							     =     metrics.pop(lowerCAmelCase_					)
        metrics.update(output.metrics					)
     else:
        __lowerCAmelCase							     =     output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(lowerCAmelCase_					)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()					)

     __lowerCAmelCase							     =     self.callback_handler.on_evaluate(self.args ,  self.state ,  self.control ,  lowerCAmelCase_					)
     return metrics






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Tuple=None ,  lowerCAmelCase_			:		str = "test"					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.get_test_dataloader(lowerCAmelCase_					)

     # Temporarily disable metric computation, we will do it in the loop here.
     __lowerCAmelCase							     =     self.compute_metrics
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     __lowerCAmelCase							     =     time.time()
     try:
        __lowerCAmelCase							     =     eval_loop(
            lowerCAmelCase_ ,  description='Prediction' ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=lowerCAmelCase_ ,  metric_key_prefix=lowerCAmelCase_ ,  )
     finally:
        __lowerCAmelCase							     =     compute_metrics
     __lowerCAmelCase							     =     self.args.eval_batch_size * self.args.world_size
     if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
        start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
     output.metrics.update(
         speed_metrics(
             lowerCAmelCase_ ,  lowerCAmelCase_ ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size					) ,  )					)

     if self.post_process_function is None or self.compute_metrics is None:
        return output

     __lowerCAmelCase							     =     self.post_process_function(lowerCAmelCase_ ,  lowerCAmelCase_ ,  output.predictions ,  'predict'					)
     __lowerCAmelCase							     =     self.compute_metrics(lowerCAmelCase_					)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()					):
        if not key.startswith(f"""{metric_key_prefix}_"""					):
           __lowerCAmelCase							     =     metrics.pop(lowerCAmelCase_					)
     metrics.update(output.metrics					)
     return PredictionOutput(predictions=predictions.predictions ,  label_ids=predictions.label_ids ,  metrics=lowerCAmelCase_					)








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_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     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
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

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

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

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     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}"""     )
   __lowerCAmelCase							     =     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__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[Any]						   =  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,
     )

