import logging
import os

from .state import PartialState
class     lowerCAmelCase_     (       logging.LoggerAdapter ):







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Any		):
												_UpperCamelCase			     =     PartialState()
												return not main_process_only or (main_process_only and state.is_main_process)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     str ,			*_A      :     int ,			**_A      :     List[Any]		):
												if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
												_UpperCamelCase			     =     kwargs.pop('''main_process_only''' ,			_A		)
												_UpperCamelCase			     =     kwargs.pop('''in_order''' ,			_A		)

												if self.isEnabledFor(_A		):
																	if self._should_log(_A		):
																						_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																						self.logger.log(_A ,			_A ,			*_A ,			**_A		)

																	elif in_order:
																						_UpperCamelCase			     =     PartialState()
																						for i in range(state.num_processes		):
																											if i == state.process_index:
																																_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																																self.logger.log(_A ,			_A ,			*_A ,			**_A		)
																											state.wait_for_everyone()




def 					_snake_case	(  __snake_case  ,     __snake_case = None						):
					if log_level is None:
										_UpperCamelCase			     =     os.environ.get('''ACCELERATE_LOG_LEVEL'''  ,     __snake_case						)
					_UpperCamelCase			     =     logging.getLogger(__snake_case						)
					if log_level is not None:
										logger.setLevel(log_level.upper()						)
										logger.root.setLevel(log_level.upper()						)
					return MultiProcessAdapter(__snake_case  ,     {}						)




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__	)

