


"""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 					a__			(				SCREAMING_SNAKE_CASE    :    Dict=None				,      SCREAMING_SNAKE_CASE    :    Optional[int]=None				):


  '''simple docstring'''







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


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


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

def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  try:
    int(SCREAMING_SNAKE_CASE				)
    return True
  except ValueError:
    return False
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







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


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							args
         lowerCAmelCase     :     Union[str, Any]								=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     int								=							plt.subplots()
         lowerCAmelCase     :     Optional[int]								=							"Time usage" if self.args.is_time else "Memory usage"
         lowerCAmelCase     :     Tuple								=							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()						):
           lowerCAmelCase     :     str								=							sorted(set(self.result_dict[model_name]["bsz"]						)						)
           lowerCAmelCase     :     Union[str, Any]								=							sorted(set(self.result_dict[model_name]["seq_len"]						)						)
           lowerCAmelCase     :     str								=							self.result_dict[model_name]["result"]

           ((lowerCAmelCase)						,  (lowerCAmelCase))     :     Union[str, Any]								=							(
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           lowerCAmelCase     :     str								=							(
               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:
               lowerCAmelCase     :     List[str]								=							np.asarray(
                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=snake_case__		,     )
             else:
               lowerCAmelCase     :     Tuple								=							np.asarray(
                   [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,     dtype=np.floataa		,     )

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

             lowerCAmelCase     :     Any								=							np.asarray(snake_case__		,     snake_case__						)[: len(snake_case__						)]
             plt.scatter(
                 snake_case__		,     snake_case__		,     label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
             plt.plot(snake_case__		,     snake_case__		,     "--"						)

           title_str += f""" {label_model_name} vs."""

         lowerCAmelCase     :     str								=							title_str[:-4]
         lowerCAmelCase     :     Optional[int]								=							"Time in s" if self.args.is_time else "Memory in MB"

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

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

def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							HfArgumentParser(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Tuple								=							parser.parse_args_into_dataclasses()[0]
  lowerCAmelCase     :     Tuple								=							Plot(args=SCREAMING_SNAKE_CASE				)
  plot.plot()


if __name__ == "__main__":
     main()









"""simple docstring"""







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase__      				=   logging.getLogger(__name__)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Tuple   ="token-classification"

       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         if type(snake_case__						) == dict:
           lowerCAmelCase     :     List[str]								=							Namespace(**snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							import_module("tasks"						)
         try:
           lowerCAmelCase     :     Dict								=							getattr(snake_case__		,     hparams.task_type						)
           lowerCAmelCase     :     TokenClassificationTask								=							token_classification_task_clazz()
         except AttributeError:
           raise ValueError(
               f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
               f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)
         lowerCAmelCase     :     Dict								=							self.token_classification_task.get_labels(hparams.labels						)
         lowerCAmelCase     :     str								=							CrossEntropyLoss().ignore_index
         super().__init__(snake_case__		,     len(self.labels						)		,     self.mode						)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         return self.model(**snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     Optional[int]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase     :     str								=							self(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.hparams
         for mode in ["train", "dev", "test"]:
           lowerCAmelCase     :     Optional[int]								=							self._feature_file(snake_case__						)
           if os.path.exists(snake_case__						) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"		,     snake_case__						)
             lowerCAmelCase     :     Dict								=							torch.load(snake_case__						)
           else:
             logger.info("Creating features from dataset file at %s"		,     args.data_dir						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.read_examples_from_file(args.data_dir		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.convert_examples_to_features(
                 snake_case__		,     self.labels		,     args.max_seq_length		,     self.tokenizer		,     cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)		,     cls_token=self.tokenizer.cls_token		,     cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0		,     sep_token=self.tokenizer.sep_token		,     sep_token_extra=snake_case__		,     pad_on_left=bool(self.config.model_type in ["xlnet"]						)		,     pad_token=self.tokenizer.pad_token_id		,     pad_token_segment_id=self.tokenizer.pad_token_type_id		,     pad_token_label_id=self.pad_token_label_id		,     )
             logger.info("Saving features into cached file %s"		,     snake_case__						)
             torch.save(snake_case__		,     snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self._feature_file(snake_case__						)
         logger.info("Loading features from cached file %s"		,     snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							torch.load(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.input_ids for f in features]		,     dtype=torch.long						)
         lowerCAmelCase     :     int								=							torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long						)
         if features[0].token_type_ids is not None:
           lowerCAmelCase     :     Tuple								=							torch.tensor([f.token_type_ids for f in features]		,     dtype=torch.long						)
         else:
           lowerCAmelCase     :     Optional[Any]								=							torch.tensor([0 for f in features]		,     dtype=torch.long						)
           # HACK(we will not use this anymore soon)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.label_ids for f in features]		,     dtype=torch.long						)
         return DataLoader(
             TensorDataset(snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)		,     batch_size=snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         """Compute validation""" ""
         lowerCAmelCase     :     str								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     List[Any]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase     :     Any								=							self(**snake_case__						)
         lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							outputs[:2]
         lowerCAmelCase     :     Dict								=							logits.detach().cpu().numpy()
         lowerCAmelCase     :     Optional[Any]								=							inputs["labels"].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							torch.stack([x["val_loss"] for x in outputs]						).mean()
         lowerCAmelCase     :     Optional[Any]								=							np.concatenate([x["pred"] for x in outputs]		,     axis=0						)
         lowerCAmelCase     :     List[Any]								=							np.argmax(snake_case__		,     axis=2						)
         lowerCAmelCase     :     Optional[int]								=							np.concatenate([x["target"] for x in outputs]		,     axis=0						)

         lowerCAmelCase     :     str								=							dict(enumerate(self.labels						)						)
         lowerCAmelCase     :     Optional[int]								=							[[] for _ in range(out_label_ids.shape[0]						)]
         lowerCAmelCase     :     Dict								=							[[] for _ in range(out_label_ids.shape[0]						)]

         for i in range(out_label_ids.shape[0]						):
           for j in range(out_label_ids.shape[1]						):
             if out_label_ids[i, j] != self.pad_token_label_id:
               out_label_list[i].append(label_map[out_label_ids[i][j]]						)
               preds_list[i].append(label_map[preds[i][j]]						)

         lowerCAmelCase     :     Dict								=							{
             "val_loss": val_loss_mean,
             "accuracy_score": accuracy_score(snake_case__		,     snake_case__						),
             "precision": precision_score(snake_case__		,     snake_case__						),
             "recall": recall_score(snake_case__		,     snake_case__						),
             "f1": fa_score(snake_case__		,     snake_case__						),
         }

         lowerCAmelCase     :     int								=							dict(results.items()						)
         lowerCAmelCase     :     Tuple								=							results
         return ret, preds_list, out_label_list

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._eval_end(snake_case__						)
         lowerCAmelCase     :     int								=							ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							self._eval_end(snake_case__						)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase     :     str								=							ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       @staticmethod
       def        lowercase__							(						snake_case__		,     snake_case__						):






         """simple docstring"""





         BaseTransformer.add_model_specific_args(snake_case__		,     snake_case__						)
         parser.add_argument(
             "--task_type"		,     default="NER"		,     type=snake_case__		,     help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
         parser.add_argument(
             "--max_seq_length"		,     default=128		,     type=snake_case__		,     help=(
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )		,     )

         parser.add_argument(
             "--labels"		,     default=""		,     type=snake_case__		,     help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,     )
         parser.add_argument(
             "--gpus"		,     default=0		,     type=snake_case__		,     help="The number of GPUs allocated for this, it is by default 0 meaning none"		,     )

         parser.add_argument(
             "--overwrite_cache"		,     action="store_true"		,     help="Overwrite the cached training and evaluation sets"						)

         return parser


if __name__ == "__main__":
     lowerCAmelCase__      				=   argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowerCAmelCase__      				=   NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowerCAmelCase__      				=   parser.parse_args()
     lowerCAmelCase__      				=   NERTransformer(args)
     lowerCAmelCase__      				=   generic_train(model, args)

     if args.do_predict:
          # See https://github.com/huggingface/transformers/issues/3159
          # pl use this default format to create a checkpoint:
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
          # /pytorch_lightning/callbacks/model_checkpoint.py#L322
          lowerCAmelCase__      				=   sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
          lowerCAmelCase__      				=   model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)





