



"""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,
)


_lowerCAmelCase							:   int				=				logging.getLogger(__name__)






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







@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  str						=					field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(
          default='summarization'			,     metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=1_0_2_4			,     metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=1_2_8			,     metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=1_4_2			,     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``.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=1_4_2			,     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.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(default=-1			,     metadata={'help': '# training examples. -1 means use all.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(default=-1			,     metadata={'help': '# validation examples. -1 means use all.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(default=-1			,     metadata={'help': '# test examples. -1 means use all.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(default=_UpperCamelCase			,     metadata={'help': 'Source language id for translation.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[str]						=					field(default=_UpperCamelCase			,     metadata={'help': 'Target language id for translation.'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(default=_UpperCamelCase			,     metadata={'help': '# num_beams to use for evaluation.'}		)
      __SCREAMING_SNAKE_CASE		:  bool						=					field(
          default=_UpperCamelCase			,     metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}			,     )







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  int:





     '''simple docstring'''

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

def 		__snake_case		( )    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       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.
          _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       parser.parse_args_into_dataclasses()

     check_output_dir(SCREAMING_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"		,    SCREAMING_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.

     _UpperCAmelCase     :      Union[str, Any]														=       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		,    )

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

     _UpperCAmelCase     :      Any														=       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		,    )
     _UpperCAmelCase     :      Optional[int]														=       AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path		,    from_tf=".ckpt" in model_args.model_name_or_path		,    config=SCREAMING_SNAKE_CASE__		,    cache_dir=model_args.cache_dir		,    )

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

     # set num_beams for evaluation
     if data_args.eval_beams is None:
          _UpperCAmelCase     :      Optional[Any]														=       model.config.num_beams

     # set decoder_start_token_id for MBart
     if model.config.decoder_start_token_id is None and isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      Optional[int]														=       tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               _UpperCAmelCase     :      Optional[Any]														=       tokenizer.convert_tokens_to_ids(data_args.tgt_lang							)

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

     _UpperCAmelCase     :      Optional[Any]														=       SeqaSeqDataset

     # Get datasets
     _UpperCAmelCase     :      Optional[Any]														=       (
         dataset_class(
             SCREAMING_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
     )
     _UpperCAmelCase     :      Dict														=       (
         dataset_class(
             SCREAMING_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
     )
     _UpperCAmelCase     :      int														=       (
         dataset_class(
             SCREAMING_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
     _UpperCAmelCase     :      Any														=       (
         build_compute_metrics_fn(data_args.task		,    SCREAMING_SNAKE_CASE__							) if training_args.predict_with_generate else None
     )
     _UpperCAmelCase     :      List[str]														=       SeqaSeqTrainer(
         model=SCREAMING_SNAKE_CASE__		,    args=SCREAMING_SNAKE_CASE__		,    data_args=SCREAMING_SNAKE_CASE__		,    train_dataset=SCREAMING_SNAKE_CASE__		,    eval_dataset=SCREAMING_SNAKE_CASE__		,    data_collator=SeqaSeqDataCollator(
             SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    model.config.decoder_start_token_id		,    training_args.tpu_num_cores							)		,    compute_metrics=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__		,    )

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

          _UpperCAmelCase     :      Any														=       trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
          _UpperCAmelCase     :      Tuple														=       train_result.metrics
          _UpperCAmelCase     :      Optional[Any]														=       data_args.n_train

          trainer.save_model()  # this also saves the tokenizer

          if trainer.is_world_process_zero():
               handle_metrics("train"		,    SCREAMING_SNAKE_CASE__		,    training_args.output_dir							)
               all_metrics.update(SCREAMING_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 ***"							)

          _UpperCAmelCase     :      Tuple														=       trainer.evaluate(metric_key_prefix="val"							)
          _UpperCAmelCase     :      Optional[int]														=       data_args.n_val
          _UpperCAmelCase     :      Dict														=       round(metrics["val_loss"]		,    4							)

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

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

          _UpperCAmelCase     :      Tuple														=       trainer.predict(test_dataset=SCREAMING_SNAKE_CASE__		,    metric_key_prefix="test"							)
          _UpperCAmelCase     :      int														=       test_output.metrics
          _UpperCAmelCase     :      Dict														=       data_args.n_test

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

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

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

     return all_metrics

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[Any]							)    ->  List[Any]:





     '''simple docstring'''

     main()


if __name__ == "__main__":
 main()





"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:   List[str]				=				{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase							:   List[str]				=				{
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  Dict						=					GPTaTokenizer



      def __init__(	self	:      Optional[Any]	,      A	:      Optional[int]=None	,      A	:      List[Any]=None	,      A	:      Optional[int]=None	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Union[str, Any]="<|endoftext|>"	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Optional[Any]=False	,      **A	:      str	,      ):
           super().__init__(
               A	,      A	,      tokenizer_file=A	,      unk_token=A	,      bos_token=A	,      eos_token=A	,      add_prefix_space=A	,      **A	,      )

           _UpperCAmelCase     :      List[Any]														=       kwargs.pop("add_bos_token"	,      A		)

           _UpperCAmelCase     :      List[str]														=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
           if pre_tok_state.get("add_prefix_space"	,      A		) != add_prefix_space:
                _UpperCAmelCase     :      Dict														=       getattr(A	,      pre_tok_state.pop("type"		)		)
                _UpperCAmelCase     :      Optional[Any]														=       add_prefix_space
                _UpperCAmelCase     :      Optional[Any]														=       pre_tok_class(**A		)

           _UpperCAmelCase     :      List[Any]														=       add_prefix_space



      def 		snake_case_   (	self	:      List[Any]	,      *A	:      List[Any]	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)
           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      List[Any]	,      **A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)

           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      List[Any]														=       self._tokenizer.model.save(A	,      name=A		)
           return tuple(A		)



      def 		snake_case_   (	self	:      int	,      A	:      "Conversation"		):
           _UpperCAmelCase     :      List[str]														=       []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(A	,      add_special_tokens=A		) + [self.eos_token_id]		)

           if len(A		) > self.model_max_length:
                _UpperCAmelCase     :      Optional[int]														=       input_ids[-self.model_max_length :]
           return input_ids
