


def   _lowercase(					__a				:						Optional[Any]    ,     __a				:						Optional[int]     ):
							# "extended trapezoidal rule"
							# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
							a__      =(boundary[1] - boundary[0]) / steps
							a__      =boundary[0]
							a__      =boundary[1]
							a__      =make_points(__a    ,     __a    ,     __a     )
							a__      =0.0
							y += (h / 2.0) * f(__a     )
							for i in x_i:
														# print(i)
														y += h * f(__a     )
							y += (h / 2.0) * f(__a     )
							return y






def   _lowercase(					__a				:						Tuple    ,     __a				:						str    ,     __a				:						Union[str, Any]     ):
							a__      =a + h
							while x < (b - h):
														yield x
														a__      =x + h






def   _lowercase(					__a				:						Dict     ):  # enter your function here
							a__      =(x - 0) * (x - 0)
							return y






def   _lowercase(					):
							a__      =0.0  # Lower bound of integration
							a__      =1.0  # Upper bound of integration
							a__      =10.0  # define number of steps or resolution
							a__      =[a, b]  # define boundary of integration
							a__      =method_a(__a    ,     __a     )
							print(f"""y = {y}"""     )


if __name__ == "__main__":
	main()









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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''transfo-xl'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=26_77_35			,     a=[2_00_00, 4_00_00, 20_00_00]			,     a=10_24			,     a=10_24			,     a=16			,     a=64			,     a=40_96			,     a=4			,     a=False			,     a=18			,     a=16_00			,     a=10_00			,     a=True			,     a=True			,     a=0			,     a=-1			,     a=True			,     a=0.1			,     a=0.0			,     a=True			,     a="normal"			,     a=0.01			,     a=0.01			,     a=0.02			,     a=1E-5			,     a=0			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				[]
								self.cutoffs.extend(a   )
								if proj_share_all_but_first:
															snake_case_           =				[False] + [True] * len(self.cutoffs   )
								else:
															snake_case_           =				[False] + [False] * len(self.cutoffs   )
								snake_case_           =				d_model
								snake_case_           =				d_embed
								snake_case_           =				d_head
								snake_case_           =				d_inner
								snake_case_           =				div_val
								snake_case_           =				pre_lnorm
								snake_case_           =				n_layer
								snake_case_           =				n_head
								snake_case_           =				mem_len
								snake_case_           =				same_length
								snake_case_           =				attn_type
								snake_case_           =				clamp_len
								snake_case_           =				sample_softmax
								snake_case_           =				adaptive
								snake_case_           =				dropout
								snake_case_           =				dropatt
								snake_case_           =				untie_r
								snake_case_           =				init
								snake_case_           =				init_range
								snake_case_           =				proj_init_std
								snake_case_           =				init_std
								snake_case_           =				layer_norm_epsilon
								super().__init__(eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								# Message copied from Transformer-XL documentation
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
								return -1






	@max_position_embeddings.setter
	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





