
"""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					:	Tuple							=		logging.getLogger(__name__)




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







@dataclass
class 		a_						:
    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=1024     ,						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=_UpperCAmelCase     ,						metadata={'help': 'Source language id for translation.'}	)
    a      :				Optional[str]     =				field(default=_UpperCAmelCase     ,						metadata={'help': 'Target language id for translation.'}	)
    a      :				Optional[int]     =				field(default=_UpperCAmelCase     ,						metadata={'help': '# num_beams to use for evaluation.'}	)
    a      :				bool     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}     ,						)







def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

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


def  _UpperCamelCase	(						)    ->  Tuple:





    """simple docstring"""

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

    check_output_dir(_A      )

    # 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"""					,      _A      )

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

    _UpperCAmelCase          =   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          =   AutoModelForSeqaSeqLM.from_pretrained(
        model_args.model_name_or_path					,      from_tf=""".ckpt""" in model_args.model_name_or_path					,      config=_A					,      cache_dir=model_args.cache_dir					,      )

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

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

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

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

    _UpperCAmelCase          =   SeqaSeqDataset

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

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

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

        trainer.save_model()  # this also saves the tokenizer

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

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

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

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

        _UpperCAmelCase          =   trainer.predict(test_dataset=_A					,      metric_key_prefix="""test"""      )
        _UpperCAmelCase          =   test_output.metrics
        _UpperCAmelCase          =   data_args.n_test

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

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

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

    return all_metrics


def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    main()


if __name__ == "__main__":
 main()

"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

a					:	List[Any]							=		get_logger()

a					:	Optional[dict]							=		None




class 		a_						( TensorFormatter[Mapping, 'jax.Array', Mapping]	):







    def __init__(			self			:       int     ,					__UpperCamelCase			:       List[str]=None     ,					__UpperCamelCase			:       Optional[int]=None     ,					**__UpperCamelCase			:       int     )		->Tuple:






        '''simple docstring'''






        super().__init__(features=__UpperCamelCase     )
        import jax
        from jaxlib.xla_client import Device

        if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ):
            raise ValueError(
                f"""Expected {device} to be a `str` not {type(__UpperCamelCase     )}, as `jaxlib.xla_extension.Device` """
                """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
                """the device with `str()` to get its string identifier that will be internally mapped """
                """to the actual `jaxlib.xla_extension.Device`."""     )
        _UpperCAmelCase          =   device if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) else str(jax.devices()[0]     )
        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
            _UpperCAmelCase          =   self._map_devices_to_str()
        if self.device not in list(DEVICE_MAPPING.keys()     ):
            logger.warning(
                f"""Device with string identifier {self.device} not listed among the available """
                f"""devices: {list(DEVICE_MAPPING.keys()     )}, so falling back to the default """
                f"""device: {str(jax.devices()[0]     )}."""     )
            _UpperCAmelCase          =   str(jax.devices()[0]     )
        _UpperCAmelCase          =   jnp_array_kwargs







    @staticmethod
    def      _snake_case					(			)		->Dict[str, "jaxlib.xla_extension.Device"]:






        '''simple docstring'''






        import jax

        return {str(__UpperCamelCase     ): device for device in jax.devices()}







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

        if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) and column:
            if all(
                isinstance(__UpperCamelCase     ,					jax.Array     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                return jnp.stack(__UpperCamelCase     ,					axis=0     )
        return column







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     )		->Optional[int]:






        '''simple docstring'''






        import jax
        import jax.numpy as jnp

        if isinstance(__UpperCamelCase     ,					(str, bytes, type(__UpperCamelCase     ))     ):
            return value
        elif isinstance(__UpperCamelCase     ,					(np.character, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.character     ):
            return value.tolist()

        _UpperCAmelCase          =   {}

        if isinstance(__UpperCamelCase     ,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.integer     ):
            # the default int precision depends on the jax config
            # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
            if jax.config.jax_enable_xaa:
                _UpperCAmelCase          =   {"""dtype""": jnp.intaa}
            else:
                _UpperCAmelCase          =   {"""dtype""": jnp.intaa}
        elif isinstance(__UpperCamelCase     ,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype     ,					np.floating     ):
            _UpperCAmelCase          =   {"""dtype""": jnp.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(__UpperCamelCase     ,					PIL.Image.Image     ):
                _UpperCAmelCase          =   np.asarray(__UpperCamelCase     )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
        global DEVICE_MAPPING
        if DEVICE_MAPPING is None:
            _UpperCAmelCase          =   self._map_devices_to_str()

        with jax.default_device(DEVICE_MAPPING[self.device]     ):
            # calling jnp.array on a np.ndarray does copy the data
            # see https://github.com/google/jax/issues/4486
            return jnp.array(__UpperCamelCase     ,					**{**default_dtype, **self.jnp_array_kwargs}     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     )		->Any:






        '''simple docstring'''






        import jax

        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
            import torch

            if isinstance(__UpperCamelCase     ,					torch.Tensor     ):
                return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
        if hasattr(__UpperCamelCase     ,					"""__array__"""     ) and not isinstance(__UpperCamelCase     ,					jax.Array     ):
            _UpperCAmelCase          =   data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(__UpperCamelCase     ,					np.ndarray     ):
            if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                return self._consolidate([self.recursive_tensorize(__UpperCamelCase     ) for substruct in data_struct]     )
        elif isinstance(__UpperCamelCase     ,					(list, tuple)     ):
            return self._consolidate([self.recursive_tensorize(__UpperCamelCase     ) for substruct in data_struct]     )
        return self._tensorize(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       dict     )		->int:






        '''simple docstring'''






        return map_nested(self._recursive_tensorize     ,					__UpperCamelCase     ,					map_list=__UpperCamelCase     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_row(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_row(__UpperCamelCase     )
        return self.recursive_tensorize(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       pa.Table     )		->"jax.Array":






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_column(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_column(__UpperCamelCase     ,					pa_table.column_names[0]     )
        _UpperCAmelCase          =   self.recursive_tensorize(__UpperCamelCase     )
        _UpperCAmelCase          =   self._consolidate(__UpperCamelCase     )
        return column







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       pa.Table     )		->Mapping:






        '''simple docstring'''






        _UpperCAmelCase          =   self.numpy_arrow_extractor().extract_batch(__UpperCamelCase     )
        _UpperCAmelCase          =   self.python_features_decoder.decode_batch(__UpperCamelCase     )
        _UpperCAmelCase          =   self.recursive_tensorize(__UpperCamelCase     )
        for column_name in batch:
            _UpperCAmelCase          =   self._consolidate(batch[column_name]     )
        return batch