import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, 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   snake_case_       (		__A       ):






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






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Optional[Dataset] = None	,						lowercase_				:     Any=None	,						lowercase_				:     Optional[List[str]] = None	,						lowercase_				:     str = "eval"	,						**lowercase_				:     Any	,						)					->						Dict[str, float]:
       lowercase__ :   List[Any]    					=				gen_kwargs.copy()
       lowercase__ :   Optional[int]    					=				(
           gen_kwargs["max_length"] if gen_kwargs.get("max_length"  ) is not None else self.args.generation_max_length
       )
       lowercase__ :   List[str]    					=				(
           gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"  ) is not None else self.args.generation_num_beams
       )
       lowercase__ :   Tuple    					=				gen_kwargs

       lowercase__ :   Tuple    					=				self.eval_dataset if eval_dataset is None else eval_dataset
       lowercase__ :   str    					=				self.get_eval_dataloader(lowercase_  )
       lowercase__ :   Any    					=				self.eval_examples if eval_examples is None else eval_examples

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

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()  ):
               if not key.startswith(F'''{metric_key_prefix}_'''  ):
                   lowercase__ :   List[str]    					=				metrics.pop(lowercase_  )

           metrics.update(output.metrics  )
       else:
           lowercase__ :   Union[str, Any]    					=				output.metrics

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

       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()  )

       lowercase__ :   List[str]    					=				self.callback_handler.on_evaluate(self.args	,						self.state	,						self.control	,						lowercase_  )
       return metrics







   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     str	,						lowercase_				:     List[Any]	,						lowercase_				:     Optional[int]=None	,						lowercase_				:     str = "test"	,						**lowercase_				:     Any  )					->						Tuple:
       lowercase__ :   Any    					=				gen_kwargs.copy()

       lowercase__ :   Tuple    					=				self.get_test_dataloader(lowercase_  )

       # Temporarily disable metric computation, we will do it in the loop here.
       lowercase__ :   List[Any]    					=				self.compute_metrics
       lowercase__ :   Optional[Any]    					=				None
       lowercase__ :   Any    					=				time.time()
       lowercase__ :   Optional[int]    					=				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
           lowercase__ :   Dict    					=				eval_loop(
               lowercase_	,						description="Prediction"	,						prediction_loss_only=True if compute_metrics is None else None	,						ignore_keys=lowercase_	,						metric_key_prefix=lowercase_	,						)
       finally:
           lowercase__ :   Optional[int]    					=				compute_metrics

       lowercase__ :   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(
               lowercase_	,						lowercase_	,						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

       lowercase__ :   int    					=				self.post_process_function(lowercase_	,						lowercase_	,						lowercase_	,						"predict"  )
       lowercase__ :   List[Any]    					=				self.compute_metrics(lowercase_  )

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








"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  parser.add_argument('--dialogpt_path', default='.', type=str)
  _lowerCAmelCase    :str     =					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase    :Tuple     =					os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
    _lowerCAmelCase    :int     =					f"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )


