






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		:       List[str]			     = logging.getLogger(__name__)
@dataclass
class     a    :



      """simple docstring"""


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


@dataclass
class     a    :



      """simple docstring"""


      a  : str							       =	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
      a  : Optional[str]							       =	field(
          default='summarization'							,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,       )
      a  : 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.'
              )
          }							,       )
      a  : 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.'
              )
          }							,       )
      a  : 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``.'
              )
          }							,       )
      a  : 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.'
              )
          }							,       )
      a  : Optional[int]							       =	field(default=-1							,       metadata={'help': '# training examples. -1 means use all.'}   )
      a  : Optional[int]							       =	field(default=-1							,       metadata={'help': '# validation examples. -1 means use all.'}   )
      a  : Optional[int]							       =	field(default=-1							,       metadata={'help': '# test examples. -1 means use all.'}   )
      a  : Optional[str]							       =	field(default=lowercase__							,       metadata={'help': 'Source language id for translation.'}   )
      a  : Optional[str]							       =	field(default=lowercase__							,       metadata={'help': 'Target language id for translation.'}   )
      a  : Optional[int]							       =	field(default=lowercase__							,       metadata={'help': '# num_beams to use for evaluation.'}   )
      a  : bool							       =	field(
          default=lowercase__							,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,       )






def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Any		):
  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 				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.

  __UpperCAmelCase							:	int           =  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							:	List[str]           =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Any           =  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.

  __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							:	Optional[int]           =  ("""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		)		)

  __UpperCAmelCase							:	List[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[Any]           =  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:
    __UpperCAmelCase							:	Union[str, 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		):
      __UpperCAmelCase							:	List[Any]           =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      __UpperCAmelCase							:	str           =  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()		)

  __UpperCAmelCase							:	Optional[int]           =  SeqaSeqDataset

  # Get datasets
  __UpperCAmelCase							:	str           =  (
      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
  )
  __UpperCAmelCase							:	Optional[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
  )
  __UpperCAmelCase							:	List[str]           =  (
      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
  __UpperCAmelCase							:	Any           =  (
      build_compute_metrics_fn(data_args.task  ,		__lowerCamelCase		) if training_args.predict_with_generate else None
  )
  __UpperCAmelCase							:	Tuple           =  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  ,		)

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

    __UpperCAmelCase							:	Dict           =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
    __UpperCAmelCase							:	Union[str, Any]           =  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"""  ,		__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 ***"""		)

    __UpperCAmelCase							:	Tuple           =  trainer.evaluate(metric_key_prefix="""val"""		)
    __UpperCAmelCase							:	Any           =  data_args.n_val
    __UpperCAmelCase							:	Tuple           =  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 ***"""		)

    __UpperCAmelCase							:	int           =  trainer.predict(test_dataset=__lowerCamelCase  ,		metric_key_prefix="""test"""		)
    __UpperCAmelCase							:	Any           =  test_output.metrics
    __UpperCAmelCase							:	Optional[Any]           =  data_args.n_test

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

      if training_args.predict_with_generate:
        __UpperCAmelCase							:	Dict           =  tokenizer.batch_decode(
            test_output.predictions  ,		skip_special_tokens=__lowerCamelCase  ,		clean_up_tokenization_spaces=__lowerCamelCase		)
        __UpperCAmelCase							:	Tuple           =  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 				lowerCamelCase__      (    __lowerCamelCase			:     Any		):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
       main()










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Dict			     = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	't5'
      a  : Dict							       =	['past_key_values']
      a  : Tuple							       =	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
      def __init__(      self					:			str		,      __lowercase					:			Optional[int]=32128		,      __lowercase					:			Optional[int]=512		,      __lowercase					:			int=64		,      __lowercase					:			Any=2048		,      __lowercase					:			Tuple=6		,      __lowercase					:			Tuple=None		,      __lowercase					:			int=8		,      __lowercase					:			List[Any]=32		,      __lowercase					:			Dict=128		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			int=1e-6		,      __lowercase					:			List[str]=1.0		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Dict=True		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Tuple=0		,      __lowercase					:			List[str]=1		,      **__lowercase					:			Any		,      )							->      str:
        __UpperCAmelCase							:	Tuple           =  vocab_size
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Union[str, Any]           =  d_kv
        __UpperCAmelCase							:	Union[str, Any]           =  d_ff
        __UpperCAmelCase							:	int           =  num_layers
        __UpperCAmelCase							:	Any           =  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCAmelCase							:	Dict           =  num_heads
        __UpperCAmelCase							:	List[Any]           =  relative_attention_num_buckets
        __UpperCAmelCase							:	List[str]           =  relative_attention_max_distance
        __UpperCAmelCase							:	Union[str, Any]           =  dropout_rate
        __UpperCAmelCase							:	List[str]           =  layer_norm_epsilon
        __UpperCAmelCase							:	str           =  initializer_factor
        __UpperCAmelCase							:	Dict           =  feed_forward_proj
        __UpperCAmelCase							:	Optional[int]           =  use_cache

        __UpperCAmelCase							:	List[Any]           =  self.feed_forward_proj.split("""-"""					)
        __UpperCAmelCase							:	Tuple           =  act_info[-1]
        __UpperCAmelCase							:	int           =  act_info[0] == """gated"""

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 2:
          raise ValueError(
              f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
              """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
              """'gated-gelu' or 'relu'"""					)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          __UpperCAmelCase							:	Dict           =  """gelu_new"""

        super().__init__(
            pad_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      **__lowercase		,      )


class     a    ( lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase							:	Union[str, Any]           =  {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
          __UpperCAmelCase							:	List[Any]           =  """past_encoder_sequence + sequence"""
          __UpperCAmelCase							:	Optional[int]           =  {0: """batch"""}
          __UpperCAmelCase							:	Union[str, Any]           =  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
          self.fill_with_past_key_values_(__lowercase		,      direction="""inputs"""					)

        return common_inputs




      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      int:
        return 13


