


'''simple docstring'''


import sys







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[Any]     		=      len(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Tuple     		=      [[0 for x in range(SCREAMING_SNAKE_CASE__     )] for x in range(SCREAMING_SNAKE_CASE__     )]
				_SCREAMING_SNAKE_CASE :       List[str]     		=      [[0 for x in range(SCREAMING_SNAKE_CASE__     )] for x in range(SCREAMING_SNAKE_CASE__     )]

				for chain_length in range(2							,      SCREAMING_SNAKE_CASE__     ):
								for a in range(1							,      n - chain_length + 1     ):
												_SCREAMING_SNAKE_CASE :       int     		=      a + chain_length - 1

												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      sys.maxsize
												for c in range(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):
																_SCREAMING_SNAKE_CASE :       Tuple     		=      (
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      cost
																				_SCREAMING_SNAKE_CASE :       Dict     		=      c
				return matrix, sol







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if i == j:
								print("""A""" + str(SCREAMING_SNAKE_CASE__     )							,      end=""" """     )
				else:
								print("""("""							,      end=""" """     )
								print_optiomal_solution(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      optimal_solution[i][j]     )
								print_optiomal_solution(SCREAMING_SNAKE_CASE__							,      optimal_solution[i][j] + 1							,      SCREAMING_SNAKE_CASE__     )
								print(""")"""							,      end=""" """     )







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      [30, 35, 15, 5, 10, 20, 25]
				_SCREAMING_SNAKE_CASE :       int     		=      len(SCREAMING_SNAKE_CASE__     )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      matrix_chain_order(SCREAMING_SNAKE_CASE__     )

				print("""No. of Operation required: """ + str(matrix[1][n - 1]     )     )
				print_optiomal_solution(SCREAMING_SNAKE_CASE__							,      1							,      n - 1     )


if __name__ == "__main__":
				main()








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




