


"""simple docstring"""







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__ (lowerCAmelCase_     ):




     '''simple docstring'''


     __SCREAMING_SNAKE_CASE		     =       filter(lambda lowerCAmelCase_     : p.requires_grad						,		model.parameters()     )
     __SCREAMING_SNAKE_CASE		     =       sum([np.prod(p.size()     ) for p in model_parameters]     )
     return params


a__       :							Tuple        =  logging.getLogger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




     '''simple docstring'''


     if metric == "rouge2":
          __SCREAMING_SNAKE_CASE		     =       "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          __SCREAMING_SNAKE_CASE		     =       "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "em":
          __SCREAMING_SNAKE_CASE		     =       "{val_avg_em:.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."     )

     __SCREAMING_SNAKE_CASE		     =       ModelCheckpoint(
         dirpath=lowerCAmelCase_						,		filename=lowerCAmelCase_						,		monitor=f"""val_{metric}"""						,		mode="max"						,		save_top_k=3						,		every_n_epochs=1						,		)
     return checkpoint_callback




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




     '''simple docstring'''


     return EarlyStopping(
         monitor=f"""val_{metric}"""						,		mode="min" if "loss" in metric else "max"						,		patience=lowerCAmelCase_						,		verbose=lowerCAmelCase_						,		)




class 				UpperCamelCase_			(				pl.Callback):

 """simple docstring"""





 def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Union[str, Any]     ) ->			str:
      __SCREAMING_SNAKE_CASE		     =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
      pl_module.logger.log_metrics(UpperCAmelCase__     )




 @rank_zero_only
 def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       pl.Trainer    ,    UpperCAmelCase__    :       pl.LightningModule    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Union[str, Any]=True     ) ->			None:
      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
      __SCREAMING_SNAKE_CASE		     =       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
      __SCREAMING_SNAKE_CASE		     =       Path(pl_module.hparams.output_dir     )
      if type_path == "test":
           __SCREAMING_SNAKE_CASE		     =       od / "test_results.txt"
           __SCREAMING_SNAKE_CASE		     =       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.
           __SCREAMING_SNAKE_CASE		     =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
           __SCREAMING_SNAKE_CASE		     =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
           results_file.parent.mkdir(exist_ok=UpperCAmelCase__     )
           generations_file.parent.mkdir(exist_ok=UpperCAmelCase__     )
      with open(UpperCAmelCase__    ,    "a+"     ) as writer:
           for key in sorted(UpperCAmelCase__     ):
                if key in ["log", "progress_bar", "preds"]:
                     continue
                __SCREAMING_SNAKE_CASE		     =       metrics[key]
                if isinstance(UpperCAmelCase__    ,    torch.Tensor     ):
                     __SCREAMING_SNAKE_CASE		     =       val.item()
                __SCREAMING_SNAKE_CASE		     =       F"""{key}: {val:.6f}\n"""
                writer.write(UpperCAmelCase__     )

      if not save_generations:
           return

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




 @rank_zero_only
 def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Tuple     ) ->			Optional[Any]:
      try:
           __SCREAMING_SNAKE_CASE		     =       pl_module.model.model.num_parameters()
      except AttributeError:
           __SCREAMING_SNAKE_CASE		     =       pl_module.model.num_parameters()

      __SCREAMING_SNAKE_CASE		     =       count_trainable_parameters(UpperCAmelCase__     )
      # 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 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       pl.Trainer    ,    UpperCAmelCase__    :       pl.LightningModule     ) ->			Any:
      save_json(pl_module.metrics    ,    pl_module.metrics_save_path     )
      return self._write_logs(UpperCAmelCase__    ,    UpperCAmelCase__    ,    "test"     )




 @rank_zero_only
 def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       pl.Trainer    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Optional[int]:
      save_json(pl_module.metrics    ,    pl_module.metrics_save_path     )
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")




"""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__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


					return field(default_factory=lambda: default						,		metadata=lowerCAmelCase_     )




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										int(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										float(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       args
						__SCREAMING_SNAKE_CASE		     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

						with open(self.args.csv_file    ,    newline=""     ) as csv_file:
											__SCREAMING_SNAKE_CASE		     =       csv.DictReader(UpperCAmelCase__     )
											for row in reader:
																__SCREAMING_SNAKE_CASE		     =       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
																					__SCREAMING_SNAKE_CASE		     =       int(row["result"]     )
																elif can_convert_to_float(row["result"]     ):
																					# value is not None
																					__SCREAMING_SNAKE_CASE		     =       float(row["result"]     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_SNAKE_CASE		     =       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()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

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

											__SCREAMING_SNAKE_CASE		     =       (
											    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:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=UpperCAmelCase__    ,    )
																else:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,    dtype=np.floataa    ,    )

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

																__SCREAMING_SNAKE_CASE		     =       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."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_SNAKE_CASE		     =       "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__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
