




def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       [7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
			for gap in gaps:
						for i in range(SCREAMING_SNAKE_CASE       ,						len(SCREAMING_SNAKE_CASE ) ):
									SCREAMING_SNAKE_CASE     =       collection[i]
									SCREAMING_SNAKE_CASE     =       i
									while j >= gap and collection[j - gap] > insert_value:
												SCREAMING_SNAKE_CASE     =       collection[j - gap]
												j -= gap
									if j != i:
												SCREAMING_SNAKE_CASE     =       insert_value
			return collection


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__A    :				Optional[int]    						=	input("""Enter numbers separated by a comma:\n""").strip()
		__A    :				Dict    						=	[int(item) for item in user_input.split(""",""")]
		print(shell_sort(unsorted))












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A    :				List[str]    						=	logging.get_logger(__name__)

__A    :				List[Any]    						=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  '''mega'''





							def __init__(       self							:						Any							,  a							:						List[str]=30_522							,  a							:						Dict=128							,  a							:						Dict=4							,  a							:						Union[str, Any]=256							,  a							:						Tuple=16							,  a							:						Any=True							,  a							:						Dict=64							,  a							:						Union[str, Any]=False							,  a							:						Tuple=-1							,  a							:						str=None							,  a							:						int=True							,  a							:						Optional[int]="scalenorm"							,  a							:						Optional[int]=True							,  a							:						Dict="silu"							,  a							:						Optional[Any]="softmax"							,  a							:						Any=0.1							,  a							:						List[Any]=0.1							,  a							:						Tuple=0.1							,  a							:						Optional[int]=False							,  a							:						List[str]=True							,  a							:						Optional[int]=256							,  a							:						List[str]=True							,  a							:						int=0.1							,  a							:						str=2_048							,  a							:						Tuple=False							,  a							:						Optional[int]=2							,  a							:						Optional[int]=0.02							,  a							:						Optional[int]=0.2							,  a							:						Union[str, Any]=0.02							,  a							:						str=1.0							,  a							:						Dict=1							,  a							:						Union[str, Any]=0							,  a							:						Dict=2							,  a							:						List[str]="rotary"							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Tuple=True							,  **a							:						Optional[int]							,  )      ->       Dict:
										super().__init__(pad_token_id=a							,  bos_token_id=a							,  eos_token_id=a							,  **a      )

										SCREAMING_SNAKE_CASE     =       vocab_size
										SCREAMING_SNAKE_CASE     =       hidden_size
										SCREAMING_SNAKE_CASE     =       num_hidden_layers
										SCREAMING_SNAKE_CASE     =       activation
										SCREAMING_SNAKE_CASE     =       attention_activation
										SCREAMING_SNAKE_CASE     =       intermediate_size
										SCREAMING_SNAKE_CASE     =       ema_projection_size
										SCREAMING_SNAKE_CASE     =       bidirectional
										SCREAMING_SNAKE_CASE     =       shared_representation_size
										SCREAMING_SNAKE_CASE     =       use_chunking
										SCREAMING_SNAKE_CASE     =       chunk_size
										SCREAMING_SNAKE_CASE     =       truncation
										SCREAMING_SNAKE_CASE     =       normalize_before_mega
										SCREAMING_SNAKE_CASE     =       normalization_type
										SCREAMING_SNAKE_CASE     =       norm_affine
										SCREAMING_SNAKE_CASE     =       dropout_prob
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_feature_dropout
										SCREAMING_SNAKE_CASE     =       use_normalized_ffn
										SCREAMING_SNAKE_CASE     =       nffn_hidden_size
										SCREAMING_SNAKE_CASE     =       normalize_before_ffn
										SCREAMING_SNAKE_CASE     =       nffn_activation_dropout_prob
										SCREAMING_SNAKE_CASE     =       max_positions
										SCREAMING_SNAKE_CASE     =       add_token_type_embeddings
										SCREAMING_SNAKE_CASE     =       type_vocab_size
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       ema_delta_alpha_range
										SCREAMING_SNAKE_CASE     =       ema_beta_range
										SCREAMING_SNAKE_CASE     =       ema_gamma_omega_range
										SCREAMING_SNAKE_CASE     =       relative_positional_bias
										SCREAMING_SNAKE_CASE     =       use_cache
										SCREAMING_SNAKE_CASE     =       classifier_dropout
										SCREAMING_SNAKE_CASE     =       add_lm_hidden_dense_layer
										SCREAMING_SNAKE_CASE     =       1  # not used but required by Hugging Face





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]      )






