





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a   	=    {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    ["NllbTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a   	=    ["NllbTokenizerFast"]


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb import NllbTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

    a   	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


