
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			{
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"falcon"
	_lowerCAmelCase							       =				["past_key_values"]





	def __init__(			self				,       UpperCamelCase__=6_5024				,       UpperCamelCase__=4544				,       UpperCamelCase__=32				,       UpperCamelCase__=71				,       UpperCamelCase__=1e-5				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=True				,       UpperCamelCase__=0.0				,       UpperCamelCase__=0.0				,       UpperCamelCase__=None				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=False				,       UpperCamelCase__=11				,       UpperCamelCase__=11				,       **UpperCamelCase__				,       ):
					A__  :	List[Any]         =							vocab_size
					# Backward compatibility with n_embed kwarg
					A__  :	Union[str, Any]         =							kwargs.pop('''n_embed'''				,       UpperCamelCase__		)
					A__  :	Optional[Any]         =							hidden_size if n_embed is None else n_embed
					A__  :	Any         =							num_hidden_layers
					A__  :	Union[str, Any]         =							num_attention_heads
					A__  :	List[str]         =							layer_norm_epsilon
					A__  :	List[str]         =							initializer_range
					A__  :	Optional[int]         =							use_cache
					A__  :	str         =							hidden_dropout
					A__  :	Dict         =							attention_dropout

					A__  :	Optional[Any]         =							bos_token_id
					A__  :	List[Any]         =							eos_token_id
					A__  :	List[Any]         =							num_attention_heads if num_kv_heads is None else num_kv_heads
					A__  :	Tuple         =							alibi
					A__  :	Tuple         =							new_decoder_architecture
					A__  :	Optional[Any]         =							multi_query  # Ignored when new_decoder_architecture is True
					A__  :	Optional[Any]         =							parallel_attn
					A__  :	int         =							bias

					super().__init__(bos_token_id=UpperCamelCase__				,       eos_token_id=UpperCamelCase__				,       **UpperCamelCase__		)





	@property
	def   __snake_case				(			self		):
					return self.hidden_size // self.num_attention_heads





	@property
	def   __snake_case				(			self		):
					return not self.alibi

from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')