


'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'mctct'




		def __init__(		self    ,   UpperCamelCase_=8065    ,   UpperCamelCase_=1536    ,   UpperCamelCase_=36    ,   UpperCamelCase_=6144    ,   UpperCamelCase_=4    ,   UpperCamelCase_=384    ,   UpperCamelCase_=920    ,   UpperCamelCase_=1e-5    ,   UpperCamelCase_=0.3    ,   UpperCamelCase_="relu"    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=0.3    ,   UpperCamelCase_=0.3    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0.3    ,   UpperCamelCase_=1    ,   UpperCamelCase_=(7,)    ,   UpperCamelCase_=(3,)    ,   UpperCamelCase_=80    ,   UpperCamelCase_=1    ,   UpperCamelCase_=None    ,   UpperCamelCase_="sum"    ,   UpperCamelCase_=False    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_    ,   pad_token_id=UpperCamelCase_    ,   bos_token_id=UpperCamelCase_    ,   eos_token_id=UpperCamelCase_							)
							UpperCamelCase__   :Dict												= vocab_size
							UpperCamelCase__   :Optional[int]												= hidden_size
							UpperCamelCase__   :Tuple												= num_hidden_layers
							UpperCamelCase__   :Optional[Any]												= intermediate_size
							UpperCamelCase__   :int												= num_attention_heads
							UpperCamelCase__   :Optional[Any]												= attention_head_dim
							UpperCamelCase__   :Dict												= max_position_embeddings
							UpperCamelCase__   :Tuple												= layer_norm_eps
							UpperCamelCase__   :Optional[int]												= layerdrop
							UpperCamelCase__   :Optional[Any]												= hidden_act
							UpperCamelCase__   :Any												= initializer_range
							UpperCamelCase__   :int												= hidden_dropout_prob
							UpperCamelCase__   :Union[str, Any]												= attention_probs_dropout_prob
							UpperCamelCase__   :Optional[Any]												= pad_token_id
							UpperCamelCase__   :Union[str, Any]												= bos_token_id
							UpperCamelCase__   :Tuple												= eos_token_id
							UpperCamelCase__   :str												= conv_glu_dim
							UpperCamelCase__   :int												= conv_dropout
							UpperCamelCase__   :int												= num_conv_layers
							UpperCamelCase__   :Union[str, Any]												= input_feat_per_channel
							UpperCamelCase__   :Any												= input_channels
							UpperCamelCase__   :str												= conv_channels
							UpperCamelCase__   :Union[str, Any]												= ctc_loss_reduction
							UpperCamelCase__   :List[str]												= ctc_zero_infinity

							# prevents config testing fail with exporting to json
							UpperCamelCase__   :Tuple												= list(UpperCamelCase_							)
							UpperCamelCase__   :int												= list(UpperCamelCase_							)

							if len(self.conv_kernel							) != self.num_conv_layers:
												raise ValueError(
												    '''Configuration for convolutional module is incorrect. '''
												    '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
												    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel							)}`, '''
												    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''							)



'''simple docstring'''
from PIL import Image


def 			a      (			__a ,					__a   )  ->	Image:



					'''simple docstring'''






					def brightness(__a   ) -> float:
										return 128 + level + (c - 128)

					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(__a   )


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