import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__SCREAMING_SNAKE_CASE						:   int         =			logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Whether to use SortishSampler or not.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': (
							            'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
							            'to the `max_length` value of the model configuration.'
							        )
							    }						,			)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': (
							            'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
							            'to the `num_beams` value of the model configuration.'
							        )
							    }						,			)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
							    }						,			)



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   super().to_dict()
													for k, v in d.items():
																			if isinstance(lowercase_				,    lowercase_    ):
																									_snake_case						:					Dict    =   v.to_dict()
													return d
import logging

from transformers import PretrainedConfig


__SCREAMING_SNAKE_CASE						:   Any         =			logging.getLogger(__name__)


__SCREAMING_SNAKE_CASE						:   int         =			{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'bertabs'



							def __init__(						self				,    lowercase_=30_522				,    lowercase_=512				,    lowercase_=6				,    lowercase_=512				,    lowercase_=8				,    lowercase_=512				,    lowercase_=0.2				,    lowercase_=6				,    lowercase_=768				,    lowercase_=8				,    lowercase_=2_048				,    lowercase_=0.2				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					List[Any]    =   vocab_size
													_snake_case						:					int    =   max_pos

													_snake_case						:					Tuple    =   enc_layers
													_snake_case						:					Optional[Any]    =   enc_hidden_size
													_snake_case						:					Union[str, Any]    =   enc_heads
													_snake_case						:					str    =   enc_ff_size
													_snake_case						:					Any    =   enc_dropout

													_snake_case						:					Tuple    =   dec_layers
													_snake_case						:					Optional[Any]    =   dec_hidden_size
													_snake_case						:					Dict    =   dec_heads
													_snake_case						:					str    =   dec_ff_size
													_snake_case						:					List[str]    =   dec_dropout