
'''simple docstring'''
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

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


a_											=						logging.getLogger(__name__)

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_				(	snake_case__					):





 def __init__(    self ,       *lowercase_ ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     super().__init__(*lowercase_ ,       **lowercase_)
     snake_case_ :    List[str]     			=						eval_examples
     snake_case_ :    str     			=						post_process_function
     snake_case_ :    int     			=						quant_trainer_args
     snake_case_ :    Optional[int]     			=						1_28  # default number of calibration samples





 def        snake_case__		(    self ,       lowercase_=None):
     if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("Trainer: calibration requires an calib_dataset.")
     snake_case_ :    Optional[Any]     			=						calib_dataset if calib_dataset is not None else self.calib_dataset

     snake_case_ :    str     			=						self._remove_unused_columns(lowercase_ ,       description="Calibration")

     return DataLoader(
         lowercase_ ,       batch_size=self.args.eval_batch_size ,       collate_fn=self.data_collator ,       drop_last=self.args.dataloader_drop_last ,       num_workers=self.args.dataloader_num_workers ,       pin_memory=self.args.dataloader_pin_memory ,       shuffle=lowercase_ ,       )





 def        snake_case__		(    self ,       lowercase_=None):
     snake_case_ :    Dict     			=						self.train_dataset if calib_dataset is None else calib_dataset
     snake_case_ :    str     			=						self.get_calib_dataloader(lowercase_)

     snake_case_ :    Any     			=						self.model
     quant_trainer.configure_model(lowercase_ ,       self.quant_trainer_args ,       calib=lowercase_)
     model.eval()
     quant_trainer.enable_calibration(lowercase_)

     logger.info("***** Running calibration *****")
     logger.info(F'  Num examples = {self.calib_num}')
     logger.info(F'  Batch size = {calib_dataloader.batch_size}')

     for step, inputs in enumerate(lowercase_):
         # Prediction step
         snake_case_			, snake_case_			, snake_case_ :    Optional[Any]     			=						self.prediction_step(lowercase_ ,       lowercase_ ,       prediction_loss_only=lowercase_)
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
             break

     quant_trainer.finish_calibration(lowercase_ ,       self.quant_trainer_args)
     snake_case_ :    Optional[int]     			=						model





 def        snake_case__		(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_ = "eval"):
     snake_case_ :    List[str]     			=						self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_ :    Any     			=						self.get_eval_dataloader(lowercase_)
     snake_case_ :    int     			=						self.eval_examples if eval_examples is None else eval_examples

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

     if self.post_process_function is not None and self.compute_metrics is not None:
         snake_case_ :    Tuple     			=						self.post_process_function(lowercase_ ,       lowercase_ ,       output.predictions)
         snake_case_ :    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}_'):
                 snake_case_ :    Dict     			=						metrics.pop(lowercase_)

         self.log(lowercase_)
     else:
         snake_case_ :    List[Any]     			=						{}

     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())

     snake_case_ :    int     			=						self.callback_handler.on_evaluate(self.args ,       self.state ,       self.control ,       lowercase_)
     return metrics





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_=None ,       lowercase_ = "test"):
     snake_case_ :    Dict     			=						self.get_test_dataloader(lowercase_)

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

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

     snake_case_ :    List[str]     			=						self.post_process_function(lowercase_ ,       lowercase_ ,       output.predictions ,       "predict")
     snake_case_ :    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}_'):
             snake_case_ :    int     			=						metrics.pop(lowercase_)

     return PredictionOutput(predictions=predictions.predictions ,       label_ids=predictions.label_ids ,       metrics=lowercase_)





 def        snake_case__		(    self ,       lowercase_="./"):
     snake_case_ :    int     			=						self.eval_dataset
     snake_case_ :    Any     			=						self.get_eval_dataloader(lowercase_)

     snake_case_ :    int     			=						next(iter(lowercase_))

     # saving device - to make it consistent
     snake_case_ :    Union[str, Any]     			=						torch.device("cuda" if torch.cuda.is_available() else "cpu")

     # convert to tuple
     snake_case_ :    str     			=						tuple(v.to(lowercase_) for k, v in batch.items())

     logger.info("Converting model to be onnx compatible")
     from pytorch_quantization.nn import TensorQuantizer

     snake_case_ :    Union[str, Any]     			=						True

     snake_case_ :    Optional[Any]     			=						self.model.to(lowercase_)

     model.eval()
     model.float()

     snake_case_ :    Optional[Any]     			=						model.module if hasattr(lowercase_ ,       "module") else model
     quant_trainer.configure_model(lowercase_ ,       self.quant_trainer_args)

     snake_case_ :    List[Any]     			=						os.path.join(lowercase_ ,       "model.onnx")
     logger.info(F'exporting model to {output_model_file}')

     snake_case_ :    Dict     			=						{0: "batch_size", 1: "seq_len"}

     torch.onnx.export(
         lowercase_ ,       lowercase_ ,       lowercase_ ,       export_params=lowercase_ ,       opset_version=13 ,       do_constant_folding=lowercase_ ,       input_names=["input_ids", "attention_mask", "token_type_ids"] ,       output_names=["output_start_logits", "output_end_logits"] ,       dynamic_axes={
             "input_ids": axes,
             "attention_mask": axes,
             "token_type_ids": axes,
             "output_start_logits": axes,
             "output_end_logits": axes,
         } ,       verbose=lowercase_ ,       )
     logger.info("onnx export finished")






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


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




