





'''simple docstring'''




import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase__       : List[str]         =       getLogger(__name__)


lowercase__       : List[Any]         =       '''cuda''' if torch.cuda.is_available() else '''cpu'''






def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					int = 8					,				__snake_case    :					str = DEFAULT_DEVICE					,				__snake_case    :					Union[str, Any]=False					,				__snake_case    :					Optional[int]="summarization"					,				__snake_case    :					Dict=None					,				**__snake_case    :					List[str]					,				)	->	Dict:
     __A		:					Any						=			Path(__snake_case     ).open('w'					,				encoding='utf-8'     )
     __A		:					List[str]						=			str(__snake_case     )
     __A		:					Union[str, Any]						=			AutoModelForSeqaSeqLM.from_pretrained(__snake_case     ).to(__snake_case     )
     if fpaa:
          __A		:					Tuple						=			model.half()

     __A		:					Dict						=			AutoTokenizer.from_pretrained(__snake_case     )
     logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'     )  # if this is wrong, check config.model_type.

     __A		:					List[str]						=			time.time()
     # update config with task specific params
     use_task_specific_params(__snake_case					,				__snake_case     )
     if prefix is None:
          __A		:					Dict						=			prefix or getattr(model.config					,				'prefix'					,				''     ) or ''
     for examples_chunk in tqdm(list(chunks(__snake_case					,				__snake_case     )     )     ):
          __A		:					Tuple						=			[prefix + text for text in examples_chunk]
          __A		:					List[Any]						=			tokenizer(__snake_case					,				return_tensors='pt'					,				truncation=__snake_case					,				padding='longest'     ).to(__snake_case     )
          __A		:					Any						=			model.generate(
              input_ids=batch.input_ids					,				attention_mask=batch.attention_mask					,				**__snake_case					,				)
          __A		:					Dict						=			tokenizer.batch_decode(__snake_case					,				skip_special_tokens=__snake_case					,				clean_up_tokenization_spaces=__snake_case     )
          for hypothesis in dec:
               fout.write(hypothesis + '\n'     )
               fout.flush()
     fout.close()
     __A		:					List[str]						=			int(time.time() - start_time     )  # seconds
     __A		:					Dict						=			len(__snake_case     )
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,				4     )}






def 			_lowerCAmelCase     (    )	->	Optional[int]:
     return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )






def 			_lowerCAmelCase     (    __snake_case    :					Tuple=True     )	->	int:
     __A		:					Any						=			argparse.ArgumentParser()
     parser.add_argument('model_name'					,				type=__snake_case					,				help='like facebook/bart-large-cnn,t5-base, etc.'     )
     parser.add_argument('input_path'					,				type=__snake_case					,				help='like cnn_dm/test.source'     )
     parser.add_argument('save_path'					,				type=__snake_case					,				help='where to save summaries'     )
     parser.add_argument('--reference_path'					,				type=__snake_case					,				required=__snake_case					,				help='like cnn_dm/test.target'     )
     parser.add_argument('--score_path'					,				type=__snake_case					,				required=__snake_case					,				default='metrics.json'					,				help='where to save metrics'     )
     parser.add_argument('--device'					,				type=__snake_case					,				required=__snake_case					,				default=__snake_case					,				help='cuda, cuda:1, cpu etc.'     )
     parser.add_argument(
         '--prefix'					,				type=__snake_case					,				required=__snake_case					,				default=__snake_case					,				help='will be added to the begininng of src examples'     )
     parser.add_argument('--task'					,				type=__snake_case					,				default='summarization'					,				help='used for task_specific_params + metrics'     )
     parser.add_argument('--bs'					,				type=__snake_case					,				default=8					,				required=__snake_case					,				help='batch size'     )
     parser.add_argument(
         '--n_obs'					,				type=__snake_case					,				default=-1					,				required=__snake_case					,				help='How many observations. Defaults to all.'     )
     parser.add_argument('--fp16'					,				action='store_true'     )
     parser.add_argument('--dump-args'					,				action='store_true'					,				help='print the custom hparams with the results'     )
     parser.add_argument(
         '--info'					,				nargs='?'					,				type=__snake_case					,				const=datetime_now()					,				help=(
             'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
             ' lang=en-ru. If no value is passed, the current datetime string will be used.'
         )					,				)
     # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
     __A					,__A		:					Optional[Any]						=			parser.parse_known_args()
     __A		:					List[str]						=			parse_numeric_n_bool_cl_kwargs(__snake_case     )
     if parsed_args and verbose:
          print(f'parsed the following generate kwargs: {parsed_args}'     )
     __A		:					List[Any]						=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
     if args.n_obs > 0:
          __A		:					Optional[Any]						=			examples[: args.n_obs]
     Path(args.save_path     ).parent.mkdir(exist_ok=__snake_case     )

     if args.reference_path is None and Path(args.score_path     ).exists():
          warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'     )

     if args.device == "cpu" and args.fpaa:
          # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
          raise ValueError('Can\'t mix --fp16 and --device cpu'     )

     __A		:					Union[str, Any]						=			generate_summaries_or_translations(
         __snake_case					,				args.save_path					,				args.model_name					,				batch_size=args.bs					,				device=args.device					,				fpaa=args.fpaa					,				task=args.task					,				prefix=args.prefix					,				**__snake_case					,				)

     if args.reference_path is None:
          return {}

     # Compute scores
     __A		:					Dict						=			calculate_bleu if 'translation' in args.task else calculate_rouge
     __A		:					Dict						=			[x.rstrip() for x in open(args.save_path     ).readlines()]
     __A		:					List[str]						=			[x.rstrip() for x in open(args.reference_path     ).readlines()][: len(__snake_case     )]
     __A		:					dict						=			score_fn(__snake_case					,				__snake_case     )
     scores.update(__snake_case     )

     if args.dump_args:
          scores.update(__snake_case     )
     if args.info:
          __A		:					Union[str, Any]						=			args.info

     if verbose:
          print(__snake_case     )

     if args.score_path is not None:
          json.dump(__snake_case					,				open(args.score_path					,				'w'     )     )

     return scores


if __name__ == "__main__":
  # Usage for MT:
  # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
  run_generate(verbose=True)






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   __snake_case    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



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




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					List[Any]        =      (
               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:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

              ((A_)   ,    (A_))       :					Optional[Any]        =      (
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )

              A_       :					List[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."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "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   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
