
'''simple docstring'''







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


_lowercase			:  Tuple   							=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__					)
class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					field(default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
					lowerCAmelCase_					       =					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.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					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.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }   ,						)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	super().to_dict()
											for k, v in d.items():
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					str													=	v.to_dict()
											return d





'''simple docstring'''







from PIL import Image
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Image				,				__SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	(259 * (level + 255)) / (255 * (259 - level))

						def contrast(__SCREAMING_SNAKE_CASE :			int							) -> int:
												return int(128 + factor * (c - 128)							)

						return img.point(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change contrast to 170
														_lowercase			:  Union[str, Any]   							=	change_contrast(img, 1_7_0)
														cont_img.save("image_data/lena_high_contrast.png", format="png")



