


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


snake_case_  :	Optional[int]							  =							logging.get_logger(__name__)

snake_case_  :	Optional[Any]							  =							{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "reformer"
						UpperCAmelCase									=  ["past_buckets_states"]
						UpperCAmelCase									=  {}




						def __init__(				self    :					List[str] ,			_a    :					List[str]=64 ,			_a    :					str=["local", "lsh", "local", "lsh", "local", "lsh"] ,			_a    :					Tuple=1.0 ,			_a    :					Tuple=True ,			_a    :					Union[str, Any]=[64, 64] ,			_a    :					List[Any]=[64, 192] ,			_a    :					List[str]=0 ,			_a    :					Dict=2 ,			_a    :					Tuple=512 ,			_a    :					Any=None ,			_a    :					List[Any]="relu" ,			_a    :					Any=0.05 ,			_a    :					Dict=256 ,			_a    :					Optional[Any]=0.02 ,			_a    :					Dict=False ,			_a    :					int=1E-12 ,			_a    :					Tuple=1 ,			_a    :					str=0 ,			_a    :					Optional[int]=0.05 ,			_a    :					Tuple=64 ,			_a    :					Tuple=64 ,			_a    :					Tuple=0.0 ,			_a    :					List[Any]=1 ,			_a    :					List[str]=0 ,			_a    :					Dict=4096 ,			_a    :					List[str]=12 ,			_a    :					Optional[int]=None ,			_a    :					str=1 ,			_a    :					Any=0 ,			_a    :					Optional[Any]=320 ,			_a    :					str=False ,			_a    :					Optional[int]=True ,			_a    :					str=None ,			**_a    :					Tuple ,			)							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =hash_seed
										_SCREAMING_SNAKE_CASE	     =vocab_size
										_SCREAMING_SNAKE_CASE	     =attention_head_size
										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =num_hashes
										_SCREAMING_SNAKE_CASE	     =len(_a     )
										_SCREAMING_SNAKE_CASE	     =tuple(_a     ) if isinstance(_a ,			_a     ) else num_buckets
										_SCREAMING_SNAKE_CASE	     =lsh_attn_chunk_length
										_SCREAMING_SNAKE_CASE	     =local_attn_chunk_length
										_SCREAMING_SNAKE_CASE	     =lsh_num_chunks_after
										_SCREAMING_SNAKE_CASE	     =lsh_num_chunks_before
										_SCREAMING_SNAKE_CASE	     =local_num_chunks_after
										_SCREAMING_SNAKE_CASE	     =local_num_chunks_before
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =feed_forward_size
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =lsh_attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =local_attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =max_position_embeddings
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =axial_pos_embds
										_SCREAMING_SNAKE_CASE	     =tuple(_a     )
										_SCREAMING_SNAKE_CASE	     =tuple(_a     )
										_SCREAMING_SNAKE_CASE	     =axial_norm_std
										_SCREAMING_SNAKE_CASE	     =chunk_size_lm_head
										_SCREAMING_SNAKE_CASE	     =attn_layers
										_SCREAMING_SNAKE_CASE	     =use_cache
										_SCREAMING_SNAKE_CASE	     =classifier_dropout
										super().__init__(
										    pad_token_id=_a ,			eos_token_id=_a ,			is_decoder=_a ,			tie_word_embeddings=_a ,			**_a ,			)



import math as m







def   lowerCamelCase(      a__		,a__		,a__=0.001):
				return (f(a + h) - f(a - h)) / (2 * h)







def   lowerCamelCase(      a__		,a__=0		,a__=100		,a__=0.0001		,a__=1e-6		,a__=False):
				_SCREAMING_SNAKE_CASE	     =xa  # set the initial guess
				_SCREAMING_SNAKE_CASE	     =[a]
				_SCREAMING_SNAKE_CASE	     =abs(f(a__))
				_SCREAMING_SNAKE_CASE	     =lambda a__: calc_derivative(a__		,a__		,h=a__)  # noqa: E731  Derivative of f(x)
				for _ in range(a__):
								if fa(a__) == 0:
												raise ValueError('''No converging solution found''')
								_SCREAMING_SNAKE_CASE	     =a - f(a__) / fa(a__)  # Calculate the next estimate
								if logsteps:
												steps.append(a__)
								if error < maxerror:
												break
				else:
								raise ValueError('''Iteration limit reached, no converging solution found''')
				if logsteps:
								# If logstep is true, then log intermediate steps
								return a, error, steps
				return a, error


if __name__ == "__main__":
							from matplotlib import pyplot as plt

							snake_case_  :	Tuple							  =							lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							snake_case_  , snake_case_  , snake_case_  :	str							  =							newton_raphson(
							    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
							)
							plt.plot([abs(f(x)) for x in steps])
							plt.xlabel('''step''')
							plt.ylabel('''error''')
							plt.show()
							print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")