




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__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def __init__(    self     ,  *UpperCamelCase__     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Tuple:
           super().__init__(*UpperCamelCase__     ,  **UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					eval_examples
           lowerCamelCase   :      Union[str, Any]			 =					post_process_function







    def     _lowercase     (    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__ = "eval"     )     ->      Optional[int]:
           lowerCamelCase   :      Optional[int]			 =					self.eval_dataset if eval_dataset is None else eval_dataset
           lowerCamelCase   :      str			 =					self.get_eval_dataloader(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           lowerCamelCase   :      List[str]			 =					self.compute_metrics
           lowerCamelCase   :      List[str]			 =					None
           lowerCamelCase   :      Optional[Any]			 =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           lowerCamelCase   :      Dict			 =					time.time()
           try:
                  lowerCamelCase   :      Union[str, Any]			 =					eval_loop(
                      UpperCamelCase__     ,  description="Evaluation"     ,  prediction_loss_only=True if compute_metrics is None else None     ,  ignore_keys=UpperCamelCase__     ,  metric_key_prefix=UpperCamelCase__     ,  )
           finally:
                  lowerCamelCase   :      Tuple			 =					compute_metrics
           lowerCamelCase   :      Union[str, Any]			 =					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(
                   UpperCamelCase__     ,  UpperCamelCase__     ,  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   :      int			 =					self.post_process_function(UpperCamelCase__     ,  UpperCamelCase__     ,  output.predictions     )
                  lowerCamelCase   :      int			 =					self.compute_metrics(UpperCamelCase__     )

                  # Prefix all keys with metric_key_prefix + '_'
                  for key in list(metrics.keys()     ):
                         if not key.startswith(F'''{metric_key_prefix}_'''     ):
                                lowerCamelCase   :      List[str]			 =					metrics.pop(UpperCamelCase__     )
                  metrics.update(output.metrics     )
           else:
                  lowerCamelCase   :      Optional[Any]			 =					output.metrics

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

           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   :      Tuple			 =					self.callback_handler.on_evaluate(self.args     ,  self.state     ,  self.control     ,  UpperCamelCase__     )
           return metrics







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  UpperCamelCase__ = "test"     )     ->      Union[str, Any]:
           lowerCamelCase   :      Any			 =					self.get_test_dataloader(UpperCamelCase__     )

           # Temporarily disable metric computation, we will do it in the loop here.
           lowerCamelCase   :      Tuple			 =					self.compute_metrics
           lowerCamelCase   :      Dict			 =					None
           lowerCamelCase   :      int			 =					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           lowerCamelCase   :      Optional[int]			 =					time.time()
           try:
                  lowerCamelCase   :      Optional[int]			 =					eval_loop(
                      UpperCamelCase__     ,  description="Prediction"     ,  prediction_loss_only=True if compute_metrics is None else None     ,  ignore_keys=UpperCamelCase__     ,  metric_key_prefix=UpperCamelCase__     ,  )
           finally:
                  lowerCamelCase   :      Dict			 =					compute_metrics
           lowerCamelCase   :      int			 =					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(
                   UpperCamelCase__     ,  UpperCamelCase__     ,  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   :      Any			 =					self.post_process_function(UpperCamelCase__     ,  UpperCamelCase__     ,  output.predictions     ,  "predict"     )
           lowerCamelCase   :      Union[str, Any]			 =					self.compute_metrics(UpperCamelCase__     )

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()     ):
                  if not key.startswith(F'''{metric_key_prefix}_'''     ):
                         lowerCamelCase   :      List[str]			 =					metrics.pop(UpperCamelCase__     )
           metrics.update(output.metrics     )
           return PredictionOutput(predictions=predictions.predictions     ,  label_ids=predictions.label_ids     ,  metrics=UpperCamelCase__     )












import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained T5 model. \nThis 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__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






