



'''simple docstring'''


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,
)


lowercase							       =			logging.getLogger(__name__)







@dataclass
class 	UpperCAmelCase	:


 '''simple docstring'''




 __magic_name__					: str  =      field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Pretrained config name or path if not the same as model_name"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
 __magic_name__					: Optional[str]  =      field(
     default=__a					,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,	)
 __magic_name__					: bool  =      field(default=__a					,	metadata={"help": "Whether tp freeze the encoder."})
 __magic_name__					: bool  =      field(default=__a					,	metadata={"help": "Whether  to freeze the embeddings."})







@dataclass
class 	UpperCAmelCase	:


 '''simple docstring'''




 __magic_name__					: str  =      field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."})
 __magic_name__					: Optional[str]  =      field(
     default="summarization"					,	metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}					,	)
 __magic_name__					: Optional[int]  =      field(
     default=1_024					,	metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }					,	)
 __magic_name__					: Optional[int]  =      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."
         )
     }					,	)
 __magic_name__					: Optional[int]  =      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``."
         )
     }					,	)
 __magic_name__					: Optional[int]  =      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."
         )
     }					,	)
 __magic_name__					: Optional[int]  =      field(default=-1					,	metadata={"help": "# training examples. -1 means use all."})
 __magic_name__					: Optional[int]  =      field(default=-1					,	metadata={"help": "# validation examples. -1 means use all."})
 __magic_name__					: Optional[int]  =      field(default=-1					,	metadata={"help": "# test examples. -1 means use all."})
 __magic_name__					: Optional[str]  =      field(default=__a					,	metadata={"help": "Source language id for translation."})
 __magic_name__					: Optional[str]  =      field(default=__a					,	metadata={"help": "Target language id for translation."})
 __magic_name__					: Optional[int]  =      field(default=__a					,	metadata={"help": "# num_beams to use for evaluation."})
 __magic_name__					: bool  =      field(
     default=__a					,	metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}					,	)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=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.
          a_		,						a_		,						a_       		=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          a_		,						a_		,						a_       		=parser.parse_args_into_dataclasses()

     check_output_dir(lowercase__       )

     # 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"       ,	lowercase__       )

     # 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.

     a_       		=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       ,	)

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

     a_       		=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       ,	)
     a_       		=AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path       ,	from_tf=".ckpt" in model_args.model_name_or_path       ,	config=lowercase__       ,	cache_dir=model_args.cache_dir       ,	)

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

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

     # set decoder_start_token_id for MBart
     if model.config.decoder_start_token_id is None and isinstance(lowercase__       ,	(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(lowercase__       ,	lowercase__       ):
               a_       		=tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               a_       		=tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

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

     a_       		=SeqaSeqDataset

     # Get datasets
     a_       		=(
         dataset_class(
             lowercase__       ,	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
     )
     a_       		=(
         dataset_class(
             lowercase__       ,	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
     )
     a_       		=(
         dataset_class(
             lowercase__       ,	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
     a_       		=(
         build_compute_metrics_fn(data_args.task       ,	lowercase__       ) if training_args.predict_with_generate else None
     )
     a_       		=SeqaSeqTrainer(
         model=lowercase__       ,	args=lowercase__       ,	data_args=lowercase__       ,	train_dataset=lowercase__       ,	eval_dataset=lowercase__       ,	data_collator=SeqaSeqDataCollator(
             lowercase__       ,	lowercase__       ,	model.config.decoder_start_token_id       ,	training_args.tpu_num_cores       )       ,	compute_metrics=lowercase__       ,	tokenizer=lowercase__       ,	)

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

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

          trainer.save_model()  # this also saves the tokenizer

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

               # 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 ***"       )

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

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

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

          a_       		=trainer.predict(test_dataset=lowercase__       ,	metric_key_prefix="test"       )
          a_       		=test_output.metrics
          a_       		=data_args.n_test

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

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

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

     return all_metrics






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     main()


if __name__ == "__main__":
  main()







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if b == 0:
          return (1, 0)
     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	a % b       )
     a_       		=a // b
     return (y, x - k * y)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	lowercase__       )
     a_       		=na * na
     a_       		=ra * x * na + ra * y * na
     return (n % m + m) % m






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     ((a_)		,						(a_))       		=extended_euclid(lowercase__       ,	lowercase__       )
     if b < 0:
          a_       		=(b % n + n) % n
     return b






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_		,						a_       		=invert_modulo(lowercase__       ,	lowercase__       ), invert_modulo(lowercase__       ,	lowercase__       )
     a_       		=na * na
     a_       		=ra * x * na + ra * y * na
     return (n % m + m) % m


if __name__ == "__main__":
  from doctest import testmod

  testmod(name='''chinese_remainder_theorem''', verbose=True)
  testmod(name='''chinese_remainder_theorem2''', verbose=True)
  testmod(name='''invert_modulo''', verbose=True)
  testmod(name='''extended_euclid''', verbose=True)


