



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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


lowercase_	       =			logging.getLogger(__name__)






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









from typing import TYPE_CHECKING

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


lowercase_	       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EncodecModel''',
				    '''EncodecPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




