





'''simple docstring'''

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
      from fairscale.optim import OSS


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A=None ,		A=None ,		*A ,		**A       )		->   Optional[int]:
        super().__init__(*A ,		**A       )

        if config is None:
            assert isinstance(self.model ,		A       ), (
                "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                f' {self.model.__class__}'
            )
            _SCREAMING_SNAKE_CASE									=			self.model.config
        else:
            _SCREAMING_SNAKE_CASE									=			config

        _SCREAMING_SNAKE_CASE									=			data_args
        _SCREAMING_SNAKE_CASE									=			self.config.tgt_vocab_size if isinstance(self.config ,		A       ) else self.config.vocab_size

        if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
            assert self.config.pad_token_id is not None, (
                "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                " calculation or doing label smoothing."
            )

        if self.config.pad_token_id is None and self.config.eos_token_id is not None:
            logger.warning(
                f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                """ padding.."""       )

        if self.args.label_smoothing == 0:
            _SCREAMING_SNAKE_CASE									=			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
        else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            _SCREAMING_SNAKE_CASE									=			label_smoothed_nll_loss


    def 			snake_case_(		self ,		A       )		->   int:
        if self.optimizer is None:
            _SCREAMING_SNAKE_CASE									=			["""bias""", """LayerNorm.weight"""]
            _SCREAMING_SNAKE_CASE									=			[
                {
                    """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                    """weight_decay""": self.args.weight_decay,
                },
                {
                    """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                    """weight_decay""": 0.0,
                },
            ]
            _SCREAMING_SNAKE_CASE									=			Adafactor if self.args.adafactor else AdamW
            if self.args.adafactor:
                _SCREAMING_SNAKE_CASE									=			Adafactor
                _SCREAMING_SNAKE_CASE									=			{"""scale_parameter""": False, """relative_step""": False}
            else:
                _SCREAMING_SNAKE_CASE									=			AdamW
                _SCREAMING_SNAKE_CASE									=			{
                    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
                    """eps""": self.args.adam_epsilon,
                }
            _SCREAMING_SNAKE_CASE									=			self.args.learning_rate
            if self.sharded_ddp:
                _SCREAMING_SNAKE_CASE									=			OSS(
                    params=A ,		optim=A ,		**A ,		)
            else:
                _SCREAMING_SNAKE_CASE									=			optimizer_cls(A ,		**A       )

        if self.lr_scheduler is None:
            _SCREAMING_SNAKE_CASE									=			self._get_lr_scheduler(A       )
        else:  # ignoring --lr_scheduler
            logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
            _SCREAMING_SNAKE_CASE									=			schedule_func(self.optimizer       )
        elif self.args.lr_scheduler == "constant_w_warmup":
            _SCREAMING_SNAKE_CASE									=			schedule_func(self.optimizer ,		num_warmup_steps=self.args.warmup_steps       )
        else:
            _SCREAMING_SNAKE_CASE									=			schedule_func(
                self.optimizer ,		num_warmup_steps=self.args.warmup_steps ,		num_training_steps=A       )
        return scheduler


    def 			snake_case_(		self       )		->   Optional[torch.utils.data.Sampler]:
        if isinstance(self.train_dataset ,		torch.utils.data.IterableDataset       ):
            return None
        elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset       )
        else:
            if self.args.sortish_sampler:
                self.train_dataset.make_sortish_sampler(
                    self.args.per_device_train_batch_size ,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) ,		)

            return (
                RandomSampler(self.train_dataset       )
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset       )
            )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   List[str]:
        if self.args.label_smoothing == 0:
            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                # force training to ignore pad token
                _SCREAMING_SNAKE_CASE									=			model(**A ,		use_cache=A       )[0]
                _SCREAMING_SNAKE_CASE									=			self.loss_fn(logits.view(-1 ,		logits.shape[-1]       ) ,		labels.view(-1       )       )
            else:
                # compute usual loss via models
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			model(**A ,		labels=A ,		use_cache=A       )[:2]
        else:
            # compute label smoothed loss
            _SCREAMING_SNAKE_CASE									=			model(**A ,		use_cache=A       )[0]
            _SCREAMING_SNAKE_CASE									=			torch.nn.functional.log_softmax(A ,		dim=-1       )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.loss_fn(A ,		A ,		self.args.label_smoothing ,		ignore_index=self.config.pad_token_id       )
        return loss, logits


    def 			snake_case_(		self ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			inputs.pop("""labels"""       )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._compute_loss(A ,		A ,		A       )
        return loss


    def 			snake_case_(		self ,		A ,		A ,		A ,		A = None ,		)		->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        _SCREAMING_SNAKE_CASE									=			self._prepare_inputs(A       )

        _SCREAMING_SNAKE_CASE									=			{
            """max_length""": self.data_args.val_max_target_length
            if self.data_args is not None
            else self.config.max_length,
            """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
        }

        if self.args.predict_with_generate and not self.args.prediction_loss_only:
            _SCREAMING_SNAKE_CASE									=			self.model.generate(
                inputs["""input_ids"""] ,		attention_mask=inputs["""attention_mask"""] ,		**A ,		)
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                _SCREAMING_SNAKE_CASE									=			self._pad_tensors_to_max_len(A ,		gen_kwargs["""max_length"""]       )

        _SCREAMING_SNAKE_CASE									=			inputs.pop("""labels"""       )
        with torch.no_grad():
            # compute loss on predict data
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._compute_loss(A ,		A ,		A       )

        _SCREAMING_SNAKE_CASE									=			loss.mean().detach()
        if self.args.prediction_loss_only:
            return (loss, None, None)

        _SCREAMING_SNAKE_CASE									=			generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
            _SCREAMING_SNAKE_CASE									=			self._pad_tensors_to_max_len(A ,		gen_kwargs["""max_length"""]       )

        return (loss, logits, labels)





    def 			snake_case_(		self ,		A ,		A       )		->   int:
        # If PAD token is not defined at least EOS token has to be defined
        _SCREAMING_SNAKE_CASE									=			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

        if pad_token_id is None:
            raise ValueError(
                """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
                f' padded to `max_length`={max_length}'       )

        _SCREAMING_SNAKE_CASE									=			pad_token_id * torch.ones(
            (tensor.shape[0], max_length) ,		dtype=tensor.dtype ,		device=tensor.device       )
        _SCREAMING_SNAKE_CASE									=			tensor
        return padded_tensor







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					np.inf

  def set_batch_size(SCREAMING_SNAKE_CASE       ) -> None:
    nonlocal batch_size
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				Tuple		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				str		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and feature.dtype == "binary":
      A_      :				Union[str, Any]		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS       )

  _visit(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  return None if batch_size is np.inf else batch_size






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->str:




         '''simple docstring'''





         super().__init__(
             _SCREAMING_SNAKE_CASE				,       split=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       cache_dir=_SCREAMING_SNAKE_CASE				,       keep_in_memory=_SCREAMING_SNAKE_CASE				,       streaming=_SCREAMING_SNAKE_CASE				,       num_proc=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
         A_      :				str		     =					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
         A_      :				Optional[int]		     =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
         A_      :				Union[str, Any]		     =					Parquet(
             cache_dir=_SCREAMING_SNAKE_CASE				,       data_files=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       hash=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )







       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         if self.streaming:
           A_      :				List[str]		     =					self.builder.as_streaming_dataset(split=self.split		)
         # Build regular (map-style) dataset
         else:
           A_      :				List[str]		     =					None
           A_      :				List[str]		     =					None
           A_      :				List[Any]		     =					None
           A_      :				Dict		     =					None

           self.builder.download_and_prepare(
               download_config=_SCREAMING_SNAKE_CASE				,       download_mode=_SCREAMING_SNAKE_CASE				,       verification_mode=_SCREAMING_SNAKE_CASE				,       base_path=_SCREAMING_SNAKE_CASE				,       num_proc=self.num_proc				,       )
           A_      :				Union[str, Any]		     =					self.builder.as_dataset(
               split=self.split				,       verification_mode=_SCREAMING_SNAKE_CASE				,       in_memory=self.keep_in_memory		)
         return dataset






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					dataset
         A_      :				Union[str, Any]		     =					path_or_buf
         A_      :				Any		     =					batch_size or get_writer_batch_size(dataset.features		)
         A_      :				Optional[int]		     =					parquet_writer_kwargs



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf				,       (str, bytes, os.PathLike)		):
           with open(self.path_or_buf				,       '''wb+'''		) as buffer:
             A_      :				str		     =					self._write(file_obj=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         else:
           A_      :				Tuple		     =					self._write(file_obj=self.path_or_buf				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         return written







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         A_      :				List[Any]		     =					0
         A_      :				int		     =					parquet_writer_kwargs.pop('''path_or_buf'''				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					self.dataset.features.arrow_schema

         A_      :				List[str]		     =					pq.ParquetWriter(_SCREAMING_SNAKE_CASE				,       schema=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         for offset in logging.tqdm(
             range(0				,       len(self.dataset		)				,       _SCREAMING_SNAKE_CASE		)				,       unit='''ba'''				,       disable=not logging.is_progress_bar_enabled()				,       desc='''Creating parquet from Arrow format'''				,       ):
           A_      :				List[Any]		     =					query_table(
               table=self.dataset._data				,       key=slice(_SCREAMING_SNAKE_CASE				,       offset + batch_size		)				,       indices=self.dataset._indices if self.dataset._indices is not None else None				,       )
           writer.write_table(_SCREAMING_SNAKE_CASE		)
           written += batch.nbytes
         writer.close()
         return written




