





'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      list						)	->	list:

				if len(lowercase						) <= 1:
								return [tuple(lowercase						)]

				_a								= []

				def generate(lowercase						:      int    ,							lowercase						:      list						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1    ,							lowercase						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																_a	,					_a								= arr[k - 1], arr[i]
												else:  # k is odd
																_a	,					_a								= arr[k - 1], arr[0]
												generate(k - 1    ,							lowercase						)

				generate(len(lowercase						)    ,							lowercase						)
				return res


if __name__ == "__main__":
					lowerCAmelCase_					:    Dict      	=			input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_					:    Optional[Any]      	=			[int(item) for item in user_input.split(',')]
					print(heaps(arr))







'''simple docstring'''







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


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				decoder_start_token_id=__a					,				**__a					,				)
