


"""simple docstring"""




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str=None      ,					SCREAMING_SNAKE_CASE__      : str=None    ):
     return field(default_factory=lambda: default      ,					metadata=SCREAMING_SNAKE_CASE__    )






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		List[Any]              =  field(
        metadata={"help": "The csv file to plot."}       ,  )
    UpperCAmelCase__						:		Optional[Any]              =  field(
        default=A_       ,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}       ,  )
    UpperCAmelCase__						:		List[str]              =  field(
        default=A_       ,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}       ,  )
    UpperCAmelCase__						:		List[Any]              =  field(
        default=A_       ,  metadata={"help": "Disable logarithmic scale when plotting"}       ,  )
    UpperCAmelCase__						:		Optional[int]              =  field(
        default=A_       ,  metadata={
            "help": "Whether the csv file has training results or inference results. Defaults to inference results."
        }       ,  )
    UpperCAmelCase__						:		Dict              =  field(
        default=A_       ,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}       ,  )
    UpperCAmelCase__						:		int              =  list_field(
        default=A_       ,  metadata={"help": "List of model names that are used instead of the ones in the csv file."}		)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     try:
          int(SCREAMING_SNAKE_CASE__    )
          return True
     except ValueError:
          return False

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any    ):
     try:
          float(SCREAMING_SNAKE_CASE__    )
          return True
     except ValueError:
          return False






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_   )  ->	Tuple:
         __UpperCamelCase   							=args
         __UpperCamelCase   							=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

         with open(self.args.csv_file ,       newline=''   ) as csv_file:
              __UpperCamelCase   							=csv.DictReader(__UpperCAmelCase   )
              for row in reader:
                   __UpperCamelCase   							=row['model']
                   self.result_dict[model_name]["bsz"].append(int(row['batch_size']   )   )
                   self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']   )   )
                   if can_convert_to_int(row['result']   ):
                        # value is not None
                        __UpperCamelCase   							=int(row['result']   )
                   elif can_convert_to_float(row['result']   ):
                        # value is not None
                        __UpperCamelCase   							=float(row['result']   )






    def _a (      self   )  ->	int:
         __UpperCamelCase							,				__UpperCamelCase   							=plt.subplots()
         __UpperCamelCase   							='Time usage' if self.args.is_time else 'Memory usage'
         __UpperCamelCase   							=title_str + ' for training' if self.args.is_train else title_str + ' for inference'

         if not self.args.no_log_scale:
              # set logarithm scales
              ax.set_xscale('log'   )
              ax.set_yscale('log'   )

         for axis in [ax.xaxis, ax.yaxis]:
              axis.set_major_formatter(ScalarFormatter()   )

         for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
              __UpperCamelCase   							=sorted(set(self.result_dict[model_name]['bsz']   )   )
              __UpperCamelCase   							=sorted(set(self.result_dict[model_name]['seq_len']   )   )
              __UpperCamelCase   							=self.result_dict[model_name]['result']

              ((__UpperCamelCase)							,				(__UpperCamelCase))   							=(
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              __UpperCamelCase   							=(
                  model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
              )

              for inner_loop_value in inner_loop_array:
                   if self.args.plot_along_batch:
                        __UpperCamelCase   							=np.asarray(
                            [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,       dtype=__UpperCAmelCase ,       )
                   else:
                        __UpperCamelCase   							=np.asarray(
                            [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,       dtype=np.floataa ,       )

                   ((__UpperCamelCase)							,				(__UpperCamelCase))   							=(
                       ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                   )

                   __UpperCamelCase   							=np.asarray(__UpperCAmelCase ,       __UpperCAmelCase   )[: len(__UpperCAmelCase   )]
                   plt.scatter(
                       __UpperCAmelCase ,       __UpperCAmelCase ,       label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'   )
                   plt.plot(__UpperCAmelCase ,       __UpperCAmelCase ,       '--'   )

              title_str += f' {label_model_name} vs.'

         __UpperCamelCase   							=title_str[:-4]
         __UpperCamelCase   							='Time in s' if self.args.is_time else 'Memory in MB'

         # plot
         plt.title(__UpperCAmelCase   )
         plt.xlabel(__UpperCAmelCase   )
         plt.ylabel(__UpperCAmelCase   )
         plt.legend()

         if self.args.figure_png_file is not None:
              plt.savefig(self.args.figure_png_file   )
         else:
              plt.show()

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							=HfArgumentParser(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=parser.parse_args_into_dataclasses()[0]
     __UpperCamelCase   							=Plot(args=SCREAMING_SNAKE_CASE__    )
     plot.plot()


if __name__ == "__main__":
   main()









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     return EarlyStopping(
         monitor=F'val_{metric}'      ,					mode='min' if 'loss' in metric else 'max'      ,					patience=SCREAMING_SNAKE_CASE__      ,					verbose=SCREAMING_SNAKE_CASE__      ,					)






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
         pl_module.logger.log_metrics(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}   )
         # Log results
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=od / 'test_generations.txt'
         else:
              # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
              # If people want this it will be easy enough to add back.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
              results_file.parent.mkdir(exist_ok=A_   )
              generations_file.parent.mkdir(exist_ok=A_   )
         with open(A_ ,       'a+'   ) as writer:
              for key in sorted(A_   ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

         if "preds" in metrics:
              __UpperCamelCase   							='\n'.join(metrics['preds']   )
              generations_file.open('w+'   ).write(A_   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=count_trainable_parameters(A_   )
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





