


'''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,
)


a_  :				Optional[Any]						= logging.getLogger(__name__)





@dataclass
class      snake_case    :





  """simple docstring"""




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





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
  _lowerCamelCase		    =  field(
      default="summarization"			,						metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}			,						)
  _lowerCamelCase		    =  field(
      default=10_24			,						metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_28			,						metadata={
          "help": (
              "The maximum total sequence length for target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						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``."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						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."
          )
      }			,						)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# training examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# validation examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# test examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Source language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Target language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "# num_beams to use for evaluation."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,						)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 logger.info(F'''***** {split} metrics *****'''		)
 for key in sorted(metrics.keys()		):
  logger.info(F'''  {key} = {metrics[key]}'''		)
 save_json(UpperCAmelCase_					,				os.path.join(UpperCAmelCase_					,				F'''{split}_results.json'''		)		)





def __snake_case					(      ):
 # 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(UpperCAmelCase_		)

 # 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"					,				UpperCAmelCase_		)

 # 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(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		):
   assert hasattr(UpperCAmelCase_					,				UpperCAmelCase_		), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
   setattr(UpperCAmelCase_					,				UpperCAmelCase_					,				getattr(UpperCAmelCase_					,				UpperCAmelCase_		)		)

 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=UpperCAmelCase_					,				cache_dir=model_args.cache_dir					,				)

 # use task specific params
 use_task_specific_params(UpperCAmelCase_					,				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(UpperCAmelCase_					,				(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(UpperCAmelCase_					,				UpperCAmelCase_		):
   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(UpperCAmelCase_		)
 if model_args.freeze_encoder:
  freeze_params(model.get_encoder()		)
  assert_all_frozen(model.get_encoder()		)

 lowerCamelCase_      =  SeqaSeqDataset

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

 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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

   # 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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

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

  lowerCamelCase_      =  trainer.predict(test_dataset=UpperCAmelCase_					,				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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

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

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

 return all_metrics





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()











"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=-1							)					->       Tuple:
   # in NER datasets, the last column is usually reserved for NER label
   _a  :       Optional[int]             =	label_idx


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 1:
           labels.append(splits[self.label_idx].replace("""\n"""							,						""""""							)							)
         else:
           # Examples could have no label for mode = "test"
           labels.append("""O"""							)
     if words:
       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	0
   for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
       writer.write(UpperCAmelCase__							)
       if not preds_list[example_id]:
         example_id += 1
     elif preds_list[example_id]:
       _a  :       int             =	line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
       writer.write(UpperCAmelCase__							)
     else:
       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,						line.split()[0]							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""O"""] + labels
     return labels
   else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Union[str, Any]							)					->       List[str]:
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""O"""] + labels
     return labels
   else:
     return [
         "O",
         "B-ADVP",
         "B-INTJ",
         "B-LST",
         "B-PRT",
         "B-NP",
         "B-SBAR",
         "B-VP",
         "B-ADJP",
         "B-CONJP",
         "B-PP",
         "I-ADVP",
         "I-INTJ",
         "I-LST",
         "I-PRT",
         "I-NP",
         "I-SBAR",
         "I-VP",
         "I-ADJP",
         "I-CONJP",
         "I-PP",
     ]







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     for sentence in parse_incr(UpperCAmelCase__							):
       _a  :       List[Any]             =	[]
       _a  :       Any             =	[]
       for token in sentence:
         words.append(token["""form"""]							)
         labels.append(token["""upos"""]							)
       assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)
       if words:
         examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
         guid_index += 1
   return examples


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       return f.read().splitlines()
   else:
     return [
         "ADJ",
         "ADP",
         "ADV",
         "AUX",
         "CCONJ",
         "DET",
         "INTJ",
         "NOUN",
         "NUM",
         "PART",
         "PRON",
         "PROPN",
         "PUNCT",
         "SCONJ",
         "SYM",
         "VERB",
         "X",
     ]

