


"""simple docstring"""


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a = None    ,   __a = None    ,   __a = False    ,   __a = False    ,   __a = None    ,   __a = None    ,   **__a    ,   ):
										super().__init__(
										    features=__a    ,   cache_dir=__a    ,   keep_in_memory=__a    ,   streaming=__a    ,   num_proc=__a    ,   **__a    ,   )
										__lowerCAmelCase				    =			Generator(
										    cache_dir=__a    ,   features=__a    ,   generator=__a    ,   gen_kwargs=__a    ,   **__a    ,   )



							def 			snake_case				(     self	):
										# Build iterable dataset
										if self.streaming:
													__lowerCAmelCase				    =			self.builder.as_streaming_dataset(split="train"	)
										# Build regular (map-style) dataset
										else:
													__lowerCAmelCase				    =			None
													__lowerCAmelCase				    =			None
													__lowerCAmelCase				    =			None
													__lowerCAmelCase				    =			None

													self.builder.download_and_prepare(
													    download_config=__a    ,   download_mode=__a    ,   verification_mode=__a    ,   base_path=__a    ,   num_proc=self.num_proc    ,   )
													__lowerCAmelCase				    =			self.builder.as_dataset(
													    split="train"    ,   verification_mode=__a    ,   in_memory=self.keep_in_memory	)
										return dataset








"""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     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







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




