

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					:Any    = logging.getLogger(__name__)

@dataclass
class      __a							:






     '''simple docstring'''
     _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      __a							:






     '''simple docstring'''
     _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=10_24   ,	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_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."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = 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``."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = 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."""
             )
         }   ,	)
     _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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      logger.info(F'''***** {split} metrics *****'''							)
      for key in sorted(metrics.keys()							):
            logger.info(F'''  {key} = {metrics[key]}'''							)
      save_json(__lowerCAmelCase  ,					os.path.join(__lowerCAmelCase  ,					F'''{split}_results.json'''							)							)




def  _lowercase					(    )       ->				Dict:
      # 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.

      SCREAMING_SNAKE_CASE__   :      Dict      		=	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.
            SCREAMING_SNAKE_CASE__   :      int      		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            SCREAMING_SNAKE_CASE__   :      str      		=	parser.parse_args_into_dataclasses()

      check_output_dir(__lowerCAmelCase							)

      # 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"""  ,					__lowerCAmelCase							)

      # 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.

      SCREAMING_SNAKE_CASE__   :      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  ,					)

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

      SCREAMING_SNAKE_CASE__   :      int      		=	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  ,					)
      SCREAMING_SNAKE_CASE__   :      int      		=	AutoModelForSeqaSeqLM.from_pretrained(
          model_args.model_name_or_path  ,					from_tf=""".ckpt""" in model_args.model_name_or_path  ,					config=__lowerCAmelCase  ,					cache_dir=model_args.cache_dir  ,					)

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

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

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

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

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	SeqaSeqDataset

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

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{}
      # Training
      if training_args.do_train:
            logger.info("""*** Train ***"""							)

            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	train_result.metrics
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	data_args.n_train

            trainer.save_model()  # this also saves the tokenizer

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

                  # 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 ***"""							)

            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	trainer.evaluate(metric_key_prefix="""val"""							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	data_args.n_val
            SCREAMING_SNAKE_CASE__   :      Dict      		=	round(metrics["""val_loss"""]  ,					4							)

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

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

            SCREAMING_SNAKE_CASE__   :      int      		=	trainer.predict(test_dataset=__lowerCAmelCase  ,					metric_key_prefix="""test"""							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	test_output.metrics
            SCREAMING_SNAKE_CASE__   :      int      		=	data_args.n_test

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

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

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

      return all_metrics




def  _lowercase					(    __lowerCAmelCase							)       ->				List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()







"""simple docstring"""


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy

class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a = None	,    _a = None	,    _a = False	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(features=_a	,    cache_dir=_a	,    keep_in_memory=_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	Sql(
               cache_dir=_a	,    features=_a	,    sql=_a	,    con=_a	,    **_a	,    )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           SCREAMING_SNAKE_CASE__   :      Dict      		=	None
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	None

           self.builder.download_and_prepare(
               download_config=_a	,    download_mode=_a	,    verification_mode=_a	,    base_path=_a	,    )

           # Build dataset for splits
           SCREAMING_SNAKE_CASE__   :      str      		=	self.builder.as_dataset(
               split="""train"""	,    verification_mode=_a	,    in_memory=self.keep_in_memory  )
           return dataset





class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    _a = None	,    _a = None	,    **_a	,    )  ->      Any:


           """simple docstring"""


           if num_proc is not None and num_proc <= 0:
                 raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

           SCREAMING_SNAKE_CASE__   :      int      		=	dataset
           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	con
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           SCREAMING_SNAKE_CASE__   :      int      		=	num_proc
           SCREAMING_SNAKE_CASE__   :      int      		=	to_sql_kwargs

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.to_sql_kwargs.pop("""sql"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""con"""	,    _a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.to_sql_kwargs.pop("""index"""	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self._write(index=_a	,    **self.to_sql_kwargs  )
           return written

     def 					_a			(						self	,    _a  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	args
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           SCREAMING_SNAKE_CASE__   :      Any      		=	query_table(
               table=self.dataset.data	,    key=slice(_a	,    offset + self.batch_size  )	,    indices=self.dataset._indices	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	batch.to_pandas()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	df.to_sql(self.name	,    self.con	,    index=_a	,    **_a  )
           return num_rows or len(_a  )





     def 					_a			(						self	,    _a	,    **_a  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	0

           if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0	,    len(self.dataset  )	,    self.batch_size  )	,    unit="""ba"""	,    disable=not logging.is_progress_bar_enabled()	,    desc="""Creating SQL from Arrow format"""	,    ):
                       written += self._batch_sql((offset, index, to_sql_kwargs)  )
           else:
                 SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	len(self.dataset  ), self.batch_size
                 with multiprocessing.Pool(self.num_proc  ) as pool:
                       for num_rows in logging.tqdm(
                           pool.imap(
                               self._batch_sql	,    [(offset, index, to_sql_kwargs) for offset in range(0	,    _a	,    _a  )]	,    )	,    total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,    unit="""ba"""	,    disable=not logging.is_progress_bar_enabled()	,    desc="""Creating SQL from Arrow format"""	,    ):
                             written += num_rows

           return written




