


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


A			:						Union[str, Any]        =  logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					field(default=UpperCAmelCase__					,    metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
					A__     =					field(
					    default=UpperCAmelCase__					,    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.'''
					        )
					    }					,    )
					A__     =					field(
					    default=UpperCAmelCase__					,    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.'''
					        )
					    }					,    )
					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }					,    )

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




							"""simple docstring"""


							lowercase__	     =						super().to_dict()
							for k, v in d.items():
									if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											lowercase__	     =						v.to_dict()
							return d





import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

