

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_ :				Dict						 =    logging.getLogger(__name__)



@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					_a		(lowerCamelCase__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= field(default=lowerCamelCase__	,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}       )
					UpperCAmelCase__:     int													= field(
					    default=lowerCamelCase__	,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}       )
					UpperCAmelCase__:     Union[str, Any]													= field(
					    default=lowerCamelCase__	,     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.'''
					        )
					    }	,     )
					UpperCAmelCase__:     Dict													= field(
					    default=lowerCamelCase__	,     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.'''
					        )
					    }	,     )
					UpperCAmelCase__:     Optional[Any]													= field(
					    default=lowerCamelCase__	,     metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }	,     )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	super().to_dict()
						for k, v in d.items():
							if isinstance(__lowerCamelCase					, __lowerCamelCase    ):
								A__							:					str       =	v.to_dict()
						return d






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 		UpperCamelCase      (*lowercase_:				Optional[int]					,   lowercase_:				Optional[Union[Dict, Any]] = None					,   lowercase_:				Dict=True					,   lowercase_:				Tuple=2      )  ->   Dict:
	from .. import __version__

	A__							:					Dict       =	take_from
	A__							:					str       =	()
	if not isinstance(args[0]					,   lowercase_      ):
		A__							:					int       =	(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(lowercase_      ).base_version      ) >= version.parse(lowercase_      ):
			raise ValueError(
			    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
			    f""" version {__version__} is >= {version_name}"""      )

		A__							:					Any       =	None
		if isinstance(lowercase_					,   lowercase_      ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(lowercase_      ),)
			A__							:					List[str]       =	f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
		elif hasattr(lowercase_					,   lowercase_      ):
			values += (getattr(lowercase_					,   lowercase_      ),)
			A__							:					Optional[Any]       =	f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
		elif deprecated_kwargs is None:
			A__							:					int       =	f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

		if warning is not None:
			A__							:					int       =	warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message					,   lowercase_					,   stacklevel=lowercase_      )

	if isinstance(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
		A__							:					Union[str, Any]       =	inspect.getouterframes(inspect.currentframe()      )[1]
		A__							:					Optional[Any]       =	call_frame.filename
		A__							:					Optional[int]       =	call_frame.lineno
		A__							:					Any       =	call_frame.function
		A__     ,     A__							:					List[str]       =	next(iter(deprecated_kwargs.items()      )      )
		raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""      )

	if len(lowercase_      ) == 0:
		return
	elif len(lowercase_      ) == 1:
		return values[0]
	return values



