

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


lowerCAmelCase__       = 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 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: str			,						*lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: List[Any]=None			,						lowerCAmelCase__		: Optional[int]=None			,						lowerCAmelCase__		: List[str]=None			,						**lowerCAmelCase__		: List[str]    )    ->      Optional[Any]:
   super().__init__(*lowerCAmelCase__			,						**lowerCAmelCase__    )
   UpperCAmelCase					      =  eval_examples
   UpperCAmelCase					      =  post_process_function
   UpperCAmelCase					      =  quant_trainer_args
   UpperCAmelCase					      =  1_2_8  # default number of calibration samples


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: Dict=None    )    ->      Optional[int]:
   if calib_dataset is None and self.calib_dataset is None:
     raise ValueError("Trainer: calibration requires an calib_dataset."    )
   UpperCAmelCase					      =  calib_dataset if calib_dataset is not None else self.calib_dataset

   UpperCAmelCase					      =  self._remove_unused_columns(lowerCAmelCase__			,						description="Calibration"    )

   return DataLoader(
       lowerCAmelCase__			,						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=lowerCAmelCase__			,						)


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: Any=None    )    ->      int:
   UpperCAmelCase					      =  self.train_dataset if calib_dataset is None else calib_dataset
   UpperCAmelCase					      =  self.get_calib_dataloader(lowerCAmelCase__    )

   UpperCAmelCase					      =  self.model
   quant_trainer.configure_model(lowerCAmelCase__			,						self.quant_trainer_args			,						calib=lowerCAmelCase__    )
   model.eval()
   quant_trainer.enable_calibration(lowerCAmelCase__    )

   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(lowerCAmelCase__    ):
     # Prediction step
     UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  self.prediction_step(lowerCAmelCase__			,						lowerCAmelCase__			,						prediction_loss_only=lowerCAmelCase__    )
     if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
       break

   quant_trainer.finish_calibration(lowerCAmelCase__			,						self.quant_trainer_args    )
   UpperCAmelCase					      =  model


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: str = "eval"    )    ->      List[Any]:
   UpperCAmelCase					      =  self.eval_dataset if eval_dataset is None else eval_dataset
   UpperCAmelCase					      =  self.get_eval_dataloader(lowerCAmelCase__    )
   UpperCAmelCase					      =  self.eval_examples if eval_examples is None else eval_examples

   # Temporarily disable metric computation, we will do it in the loop here.
   UpperCAmelCase					      =  self.compute_metrics
   UpperCAmelCase					      =  None
   UpperCAmelCase					      =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   try:
     UpperCAmelCase					      =  eval_loop(
         lowerCAmelCase__			,						description="Evaluation"			,						prediction_loss_only=True if compute_metrics is None else None			,						ignore_keys=lowerCAmelCase__			,						)
   finally:
     UpperCAmelCase					      =  compute_metrics

   if self.post_process_function is not None and self.compute_metrics is not None:
     UpperCAmelCase					      =  self.post_process_function(lowerCAmelCase__			,						lowerCAmelCase__			,						output.predictions    )
     UpperCAmelCase					      =  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}_"    ):
         UpperCAmelCase					      =  metrics.pop(lowerCAmelCase__    )

     self.log(lowerCAmelCase__    )
   else:
     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()    )

   UpperCAmelCase					      =  self.callback_handler.on_evaluate(self.args			,						self.state			,						self.control			,						lowerCAmelCase__    )
   return metrics


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: str = "test"    )    ->      Any:
   UpperCAmelCase					      =  self.get_test_dataloader(lowerCAmelCase__    )

   # Temporarily disable metric computation, we will do it in the loop here.
   UpperCAmelCase					      =  self.compute_metrics
   UpperCAmelCase					      =  None
   UpperCAmelCase					      =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   try:
     UpperCAmelCase					      =  eval_loop(
         lowerCAmelCase__			,						description="Prediction"			,						prediction_loss_only=True if compute_metrics is None else None			,						ignore_keys=lowerCAmelCase__			,						)
   finally:
     UpperCAmelCase					      =  compute_metrics

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

   UpperCAmelCase					      =  self.post_process_function(lowerCAmelCase__			,						lowerCAmelCase__			,						output.predictions			,						"predict"    )
   UpperCAmelCase					      =  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}_"    ):
       UpperCAmelCase					      =  metrics.pop(lowerCAmelCase__    )

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


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: Optional[int]="./"    )    ->      Optional[int]:
   UpperCAmelCase					      =  self.eval_dataset
   UpperCAmelCase					      =  self.get_eval_dataloader(lowerCAmelCase__    )

   UpperCAmelCase					      =  next(iter(lowerCAmelCase__    )    )

   # saving device - to make it consistent
   UpperCAmelCase					      =  torch.device("cuda" if torch.cuda.is_available() else "cpu"    )

   # convert to tuple
   UpperCAmelCase					      =  tuple(v.to(lowerCAmelCase__    ) for k, v in batch.items()    )

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

   UpperCAmelCase					      =  True

   UpperCAmelCase					      =  self.model.to(lowerCAmelCase__    )

   model.eval()
   model.float()

   UpperCAmelCase					      =  model.module if hasattr(lowerCAmelCase__			,						"module"    ) else model
   quant_trainer.configure_model(lowerCAmelCase__			,						self.quant_trainer_args    )

   UpperCAmelCase					      =  os.path.join(lowerCAmelCase__			,						"model.onnx"    )
   logger.info(f"exporting model to {output_model_file}"    )

   UpperCAmelCase					      =  {0: "batch_size", 1: "seq_len"}

   torch.onnx.export(
       lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						export_params=lowerCAmelCase__			,						opset_version=1_3			,						do_constant_folding=lowerCAmelCase__			,						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=lowerCAmelCase__			,						)
   logger.info("onnx export finished"    )






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = 1  # (0 is vertical, 1 is horizontal)
def      _lowerCAmelCase(	):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dataset(__A   ,       __A			)
  print("Processing..."			)
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  update_image_and_anno(__A   ,       __A   ,       __A			)

  for index, image in enumerate(__A			):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    UpperCAmelCase					      =  random_chars(32			)
    UpperCAmelCase					      =  paths[index].split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    UpperCAmelCase					      =  F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
    cva.imwrite(F"/{file_root}.jpg"   ,       __A   ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
    print(F"Success {index+1}/{len(__A			)} with {file_name}"			)
    UpperCAmelCase					      =  []
    for anno in new_annos[index]:
      UpperCAmelCase					      =  F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
      annos_list.append(__A			)
    with open(F"/{file_root}.txt"   ,       "w"			) as outfile:
      outfile.write("\n".join(line for line in annos_list			)			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for label_file in glob.glob(os.path.join(__A   ,       "*.txt"			)			):
    UpperCAmelCase					      =  label_file.split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    with open(__A			) as in_file:
      UpperCAmelCase					      =  in_file.readlines()
    UpperCAmelCase					      =  os.path.join(__A   ,       F"{label_name}.jpg"			)

    UpperCAmelCase					      =  []
    for obj_list in obj_lists:
      UpperCAmelCase					      =  obj_list.rstrip("\n"			).split(" "			)
      boxes.append(
          [
              int(obj[0]			),
              float(obj[1]			),
              float(obj[2]			),
              float(obj[3]			),
              float(obj[4]			),
          ]			)
    if not boxes:
      continue
    img_paths.append(__A			)
    labels.append(__A			)
  return img_paths, labels
def      _lowerCAmelCase(	__A   ,       __A   ,       __A = 1			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for idx in range(len(__A			)			):
    UpperCAmelCase					      =  []
    UpperCAmelCase					      =  img_list[idx]
    path_list.append(__A			)
    UpperCAmelCase					      =  anno_list[idx]
    UpperCAmelCase					      =  cva.imread(__A			)
    if flip_type == 1:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
    elif flip_type == 0:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
    new_annos_lists.append(__A			)
    new_imgs_list.append(__A			)
  return new_imgs_list, new_annos_lists, path_list
def      _lowerCAmelCase(	__A = 32			):
  assert number_char > 1, "The number of character should greater than 1"
  UpperCAmelCase					      =  ascii_lowercase + digits
  return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
      main()
      print("DONE ✅")



