



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


__lowerCamelCase         =			getLogger(__name__)


__lowerCamelCase         =			'''cuda''' if torch.cuda.is_available() else '''cpu'''







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = 8					,  __snake_case = DEFAULT_DEVICE					,  __snake_case=False					,  __snake_case="summarization"					,  __snake_case=None					,  **__snake_case					,  )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		Path(__snake_case    ).open("w"					,  encoding="utf-8"    )
      UpperCAmelCase_  :	Optional[Any]				      =		str(__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		AutoModelForSeqaSeqLM.from_pretrained(__snake_case    ).to(__snake_case    )
      if fpaa:
            UpperCAmelCase_  :	Dict				      =		model.half()

      UpperCAmelCase_  :	Optional[Any]				      =		AutoTokenizer.from_pretrained(__snake_case    )
      logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""    )  # if this is wrong, check config.model_type.

      UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      # update config with task specific params
      use_task_specific_params(__snake_case					,  __snake_case    )
      if prefix is None:
            UpperCAmelCase_  :	int				      =		prefix or getattr(model.config					,  "prefix"					,  ""    ) or ""
      for examples_chunk in tqdm(list(chunks(__snake_case					,  __snake_case    )    )    ):
            UpperCAmelCase_  :	int				      =		[prefix + text for text in examples_chunk]
            UpperCAmelCase_  :	List[str]				      =		tokenizer(__snake_case					,  return_tensors="pt"					,  truncation=__snake_case					,  padding="longest"    ).to(__snake_case    )
            UpperCAmelCase_  :	Optional[Any]				      =		model.generate(
                input_ids=batch.input_ids					,  attention_mask=batch.attention_mask					,  **__snake_case					,  )
            UpperCAmelCase_  :	List[Any]				      =		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()
      UpperCAmelCase_  :	str				      =		int(time.time() - start_time    )  # seconds
      UpperCAmelCase_  :	Optional[Any]				      =		len(__snake_case    )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,  4    )}







def    _snake_case (       )     ->     Optional[Any]:






      '''simple docstring'''







      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"    )







def    _snake_case (       __snake_case=True    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	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
      UpperCAmelCase_  :	int				      =		parser.parse_known_args()
      UpperCAmelCase_  :	List[Any]				      =		parse_numeric_n_bool_cl_kwargs(__snake_case    )
      if parsed_args and verbose:
            print(F"""parsed the following generate kwargs: {parsed_args}"""    )
      UpperCAmelCase_  :	Dict				      =		[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
      if args.n_obs > 0:
            UpperCAmelCase_  :	Optional[int]				      =		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"    )

      UpperCAmelCase_  :	Optional[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
      UpperCAmelCase_  :	List[Any]				      =		calculate_bleu if "translation" in args.task else calculate_rouge
      UpperCAmelCase_  :	Optional[Any]				      =		[x.rstrip() for x in open(args.save_path    ).readlines()]
      UpperCAmelCase_  :	List[Any]				      =		[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(__snake_case    )]
      UpperCAmelCase_  :	dict				      =		score_fn(__snake_case					,  __snake_case    )
      scores.update(__snake_case    )

      if args.dump_args:
            scores.update(__snake_case    )
      if args.info:
            UpperCAmelCase_  :	List[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 os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase         =			False
__lowerCamelCase         =			False







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		parser.add_parser("train"     ,help="CLI tool to train a model on a task.")

             train_parser.add_argument(
                 "--train_data"     ,type=lowercase     ,required=lowercase     ,help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."     ,)
             train_parser.add_argument(
                 "--column_label"     ,type=lowercase     ,default=0     ,help="Column of the dataset csv file with example labels.")
             train_parser.add_argument(
                 "--column_text"     ,type=lowercase     ,default=1     ,help="Column of the dataset csv file with example texts.")
             train_parser.add_argument(
                 "--column_id"     ,type=lowercase     ,default=2     ,help="Column of the dataset csv file with example ids.")
             train_parser.add_argument(
                 "--skip_first_row"     ,action="store_true"     ,help="Skip the first row of the csv file (headers).")

             train_parser.add_argument("--validation_data"     ,type=lowercase     ,default=""     ,help="path to validation dataset.")
             train_parser.add_argument(
                 "--validation_split"     ,type=lowercase     ,default=0.1     ,help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."     ,)

             train_parser.add_argument("--output"     ,type=lowercase     ,default="./"     ,help="path to saved the trained model.")

             train_parser.add_argument(
                 "--task"     ,type=lowercase     ,default="text_classification"     ,help="Task to train the model on.")
             train_parser.add_argument(
                 "--model"     ,type=lowercase     ,default="bert-base-uncased"     ,help="Model's name or path to stored model.")
             train_parser.add_argument("--train_batch_size"     ,type=lowercase     ,default=32     ,help="Batch size for training.")
             train_parser.add_argument("--valid_batch_size"     ,type=lowercase     ,default=64     ,help="Batch size for validation.")
             train_parser.add_argument("--learning_rate"     ,type=lowercase     ,default=3E-5     ,help="Learning rate.")
             train_parser.add_argument("--adam_epsilon"     ,type=lowercase     ,default=1E-08     ,help="Epsilon for Adam optimizer.")
             train_parser.set_defaults(func=lowercase)



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		TextClassificationPipeline.from_pretrained(args.model)
             elif args.task == "token_classification":
                   raise NotImplementedError
             elif args.task == "question_answering":
                   raise NotImplementedError

             self.logger.info(F"""Loading dataset from {args.train_data}""")
             UpperCAmelCase_  :	str				      =		Processor.create_from_csv(
                 args.train_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)
             UpperCAmelCase_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	Union[str, Any]				      =		Processor.create_from_csv(
                       args.validation_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





             if self.framework == "tf":
                   return self.run_tf()
             return self.run_torch()



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """simple docstring"""





             self.pipeline.fit(
                 self.train_dataset     ,validation_data=self.valid_dataset     ,validation_split=self.validation_split     ,learning_rate=self.learning_rate     ,adam_epsilon=self.adam_epsilon     ,train_batch_size=self.train_batch_size     ,valid_batch_size=self.valid_batch_size     ,)

             # Save trained pipeline
             self.pipeline.save_pretrained(self.output)
