






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase			:					List[Any]         =	logging.getLogger(__name__)



@dataclass
class 							_UpperCamelCase			:
      snake_case_  =							field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
      snake_case_  =							field(
          default=a_							,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
      snake_case_  =							field(
          default=a_							,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
      snake_case_  =							field(
          default=a_							,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,		)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Whether tp freeze the encoder."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Whether  to freeze the embeddings."""}			)



@dataclass
class 							_UpperCamelCase			:
      snake_case_  =							field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}			)
      snake_case_  =							field(
          default="""summarization"""							,		metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}							,		)
      snake_case_  =							field(
          default=1024							,		metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }							,		)
      snake_case_  =							field(
          default=128							,		metadata={
              """help""": (
                  """The maximum total sequence length for target text after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }							,		)
      snake_case_  =							field(
          default=142							,		metadata={
              """help""": (
                  """The maximum total sequence length for validation target text after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded. """
                  """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
                  """during ``evaluate`` and ``predict``."""
              )
          }							,		)
      snake_case_  =							field(
          default=142							,		metadata={
              """help""": (
                  """The maximum total sequence length for test target text after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }							,		)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# training examples. -1 means use all."""}			)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# validation examples. -1 means use all."""}			)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# test examples. -1 means use all."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Source language id for translation."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Target language id for translation."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """# num_beams to use for evaluation."""}			)
      snake_case_  =							field(
          default=a_							,		metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}							,		)





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     logger.info(F"""***** {split} metrics *****"""       )
     for key in sorted(metrics.keys()       ):
          logger.info(F"""  {key} = {metrics[key]}"""       )
     save_json(__snake_case					, os.path.join(__snake_case					, F"""{split}_results.json"""       )       )





def       __lowerCAmelCase      (						):
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     __lowerCAmelCase								=					HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

     if len(sys.argv       ) == 2 and sys.argv[1].endswith(".json"       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					parser.parse_args_into_dataclasses()

     check_output_dir(__snake_case       )

     # Setup logging
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					, datefmt="%m/%d/%Y %H:%M:%S"					, level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					, )
     logger.warning(
         "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					, training_args.local_rank					, training_args.device					, training_args.n_gpu					, bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED       )					, training_args.fpaa					, )
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
     logger.info("Training/evaluation parameters %s"					, __snake_case       )

     # Set seed
     set_seed(training_args.seed       )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __lowerCAmelCase								=					AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path					, cache_dir=model_args.cache_dir					, )

     __lowerCAmelCase								=					("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
     for p in extra_model_params:
          if getattr(__snake_case					, __snake_case					, __snake_case       ):
               assert hasattr(__snake_case					, __snake_case       ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
               setattr(__snake_case					, __snake_case					, getattr(__snake_case					, __snake_case       )       )

     __lowerCAmelCase								=					AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					, cache_dir=model_args.cache_dir					, )
     __lowerCAmelCase								=					AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path					, from_tf=".ckpt" in model_args.model_name_or_path					, config=__snake_case					, cache_dir=model_args.cache_dir					, )

     # use task specific params
     use_task_specific_params(__snake_case					, data_args.task       )

     # set num_beams for evaluation
     if data_args.eval_beams is None:
          __lowerCAmelCase								=					model.config.num_beams

     # set decoder_start_token_id for MBart
     if model.config.decoder_start_token_id is None and isinstance(__snake_case					, (MBartTokenizer, MBartTokenizerFast)       ):
          assert (
              data_args.tgt_lang is not None and data_args.src_lang is not None
          ), "mBart requires --tgt_lang and --src_lang"
          if isinstance(__snake_case					, __snake_case       ):
               __lowerCAmelCase								=					tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               __lowerCAmelCase								=					tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

     if model_args.freeze_embeds:
          freeze_embeds(__snake_case       )
     if model_args.freeze_encoder:
          freeze_params(model.get_encoder()       )
          assert_all_frozen(model.get_encoder()       )

     __lowerCAmelCase								=					SeqaSeqDataset

     # Get datasets
     __lowerCAmelCase								=					(
         dataset_class(
             __snake_case					, type_path="train"					, data_dir=data_args.data_dir					, n_obs=data_args.n_train					, max_target_length=data_args.max_target_length					, max_source_length=data_args.max_source_length					, prefix=model.config.prefix or ""					, )
         if training_args.do_train
         else None
     )
     __lowerCAmelCase								=					(
         dataset_class(
             __snake_case					, type_path="val"					, data_dir=data_args.data_dir					, n_obs=data_args.n_val					, max_target_length=data_args.val_max_target_length					, max_source_length=data_args.max_source_length					, prefix=model.config.prefix or ""					, )
         if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
         else None
     )
     __lowerCAmelCase								=					(
         dataset_class(
             __snake_case					, type_path="test"					, data_dir=data_args.data_dir					, n_obs=data_args.n_test					, max_target_length=data_args.test_max_target_length					, max_source_length=data_args.max_source_length					, prefix=model.config.prefix or ""					, )
         if training_args.do_predict
         else None
     )

     # Initialize our Trainer
     __lowerCAmelCase								=					(
         build_compute_metrics_fn(data_args.task					, __snake_case       ) if training_args.predict_with_generate else None
     )
     __lowerCAmelCase								=					SeqaSeqTrainer(
         model=__snake_case					, args=__snake_case					, data_args=__snake_case					, train_dataset=__snake_case					, eval_dataset=__snake_case					, data_collator=SeqaSeqDataCollator(
             __snake_case					, __snake_case					, model.config.decoder_start_token_id					, training_args.tpu_num_cores       )					, compute_metrics=__snake_case					, tokenizer=__snake_case					, )

     __lowerCAmelCase								=					{}
     # Training
     if training_args.do_train:
          logger.info("*** Train ***"       )

          __lowerCAmelCase								=					trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          __lowerCAmelCase								=					train_result.metrics
          __lowerCAmelCase								=					data_args.n_train

          trainer.save_model()  # this also saves the tokenizer

          if trainer.is_world_process_zero():
               handle_metrics("train"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

               # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir					, "trainer_state.json"       )       )

               # For convenience, we also re-save the tokenizer to the same directory,
               # so that you can share your model easily on huggingface.co/models =)
               tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
     if training_args.do_eval:
          logger.info("*** Evaluate ***"       )

          __lowerCAmelCase								=					trainer.evaluate(metric_key_prefix="val"       )
          __lowerCAmelCase								=					data_args.n_val
          __lowerCAmelCase								=					round(metrics["val_loss"]					, 4       )

          if trainer.is_world_process_zero():
               handle_metrics("val"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

     if training_args.do_predict:
          logger.info("*** Predict ***"       )

          __lowerCAmelCase								=					trainer.predict(test_dataset=__snake_case					, metric_key_prefix="test"       )
          __lowerCAmelCase								=					test_output.metrics
          __lowerCAmelCase								=					data_args.n_test

          if trainer.is_world_process_zero():
               __lowerCAmelCase								=					round(metrics["test_loss"]					, 4       )
               handle_metrics("test"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

               if training_args.predict_with_generate:
                    __lowerCAmelCase								=					tokenizer.batch_decode(
                        test_output.predictions					, skip_special_tokens=__snake_case					, clean_up_tokenization_spaces=__snake_case       )
                    __lowerCAmelCase								=					lmap(str.strip					, __snake_case       )
                    write_txt_file(__snake_case					, os.path.join(training_args.output_dir					, "test_generations.txt"       )       )

     if trainer.is_world_process_zero():
          save_json(__snake_case					, os.path.join(training_args.output_dir					, "all_results.json"       )       )

     return all_metrics





def       __lowerCAmelCase      (						__snake_case       ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()














from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCamelCase			:					Tuple         =	'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

lowerCamelCase			:					int         =	'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

lowerCamelCase			:					List[str]         =	'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,		_KWARGS_DESCRIPTION			)
class 							_UpperCamelCase			(datasets.Metric			):


      def  __UpperCAmelCase		(	self	)->	MetricInfo:
           return datasets.MetricInfo(
               description=_DESCRIPTION					,						citation=_CITATION					,						inputs_description=_KWARGS_DESCRIPTION					,						features=datasets.Features(
                   {
                       "predictions": datasets.Sequence(datasets.Value("string"					,						id="token"	)					,						id="sequence"	),
                       "references": datasets.Sequence(
                           datasets.Sequence(datasets.Value("string"					,						id="token"	)					,						id="sequence"	)					,						id="references"	),
                   }	)					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 4					,						)->	Dict[str, float]:
           return {
               "google_bleu": gleu_score.corpus_gleu(
                   list_of_references=__UpperCamelCase					,						hypotheses=__UpperCamelCase					,						min_len=__UpperCamelCase					,						max_len=__UpperCamelCase	)
           }






