




"""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__ :    int 					=							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class     A__      (      lowercase_     ):
	A_							:				List[str]						     =      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	A_							:				Any						     =      """squeezebert"""




	def __init__(   self  :	Tuple      ,    lowercase  :	int=30_522      ,    lowercase  :	Any=768      ,    lowercase  :	Dict=12      ,    lowercase  :	Optional[int]=12      ,    lowercase  :	List[str]=3_072      ,    lowercase  :	int="gelu"      ,    lowercase  :	Any=0.1      ,    lowercase  :	int=0.1      ,    lowercase  :	Dict=512      ,    lowercase  :	List[Any]=2      ,    lowercase  :	Any=0.02      ,    lowercase  :	Optional[Any]=1e-12      ,    lowercase  :	Tuple=0      ,    lowercase  :	List[Any]=768      ,    lowercase  :	Union[str, Any]=4      ,    lowercase  :	List[Any]=4      ,    lowercase  :	int=4      ,    lowercase  :	List[str]=1      ,    lowercase  :	List[Any]=4      ,    lowercase  :	int=4      ,    **lowercase  :	Dict      ,    )					-> Optional[Any]:
						super().__init__(pad_token_id=lowercase      ,    **lowercase	)

						__snake_case          =	vocab_size
						__snake_case          =	hidden_size
						__snake_case          =	num_hidden_layers
						__snake_case          =	num_attention_heads
						__snake_case          =	hidden_act
						__snake_case          =	intermediate_size
						__snake_case          =	hidden_dropout_prob
						__snake_case          =	attention_probs_dropout_prob
						__snake_case          =	max_position_embeddings
						__snake_case          =	type_vocab_size
						__snake_case          =	initializer_range
						__snake_case          =	layer_norm_eps
						__snake_case          =	embedding_size
						__snake_case          =	q_groups
						__snake_case          =	k_groups
						__snake_case          =	v_groups
						__snake_case          =	post_attention_groups
						__snake_case          =	intermediate_groups
						__snake_case          =	output_groups

class     A__      (      lowercase_     ):




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








"""simple docstring"""

import functools
from math import sqrt
from time import time




def       snake_case__	(       lowerCAmelCase   :		int				,					*lowerCAmelCase   :		str				,					**lowerCAmelCase   :		List[Any]		):
					__snake_case          =	time()
					__snake_case          =	func(*lowerCAmelCase				,					**lowerCAmelCase		)
					__snake_case          =	time()
					if int(end - start		) > 0:
										print(F'''{func.__name__} runtime: {(end - start):0.4f} s'''		)
					else:
										print(F'''{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms'''		)
					return output




def       snake_case__	(       lowerCAmelCase   :		int		):
					if n < 0:
										raise Exception("""n is negative"""		)
					if n == 0:
										return [0]
					__snake_case          =	[0, 1]
					for _ in range(n - 1		):
										fib.append(fib[-1] + fib[-2]		)
					return fib




def       snake_case__	(       lowerCAmelCase   :		int		):

					def fib_recursive_term(lowerCAmelCase   :		int		) -> int:
										if i < 0:
															raise Exception("""n is negative"""		)
										if i < 2:
															return i
										return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

					if n < 0:
										raise Exception("""n is negative"""		)
					return [fib_recursive_term(lowerCAmelCase		) for i in range(n + 1		)]




def       snake_case__	(       lowerCAmelCase   :		int		):

					@functools.cache
					def fib_recursive_term(lowerCAmelCase   :		int		) -> int:
										if i < 0:
															raise Exception("""n is negative"""		)
										if i < 2:
															return i
										return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

					if n < 0:
										raise Exception("""n is negative"""		)
					return [fib_recursive_term(lowerCAmelCase		) for i in range(n + 1		)]




def       snake_case__	(       lowerCAmelCase   :		int		):
					if n < 0:
										raise Exception("""n is negative"""		)
					# Cache must be outside recursuive function
					# other it will reset every time it calls itself.
					__snake_case          =	{0: 0, 1: 1, 2: 1}  # Prefilled cache

					def rec_fn_memoized(lowerCAmelCase   :		int		) -> int:
										if num in cache:
															return cache[num]

										__snake_case          =	rec_fn_memoized(num - 1		) + rec_fn_memoized(num - 2		)
										__snake_case          =	value
										return value

					return [rec_fn_memoized(lowerCAmelCase		) for i in range(n + 1		)]




def       snake_case__	(       lowerCAmelCase   :		int		):
					if n < 0:
										raise Exception("""n is negative"""		)
					if n >= 1_475:
										raise Exception("""n is too large"""		)
					__snake_case          =	sqrt(5		)
					__snake_case          =	(1 + sqrt_a) / 2
					return [round(phi**i / sqrt_a		) for i in range(n + 1		)]


if __name__ == "__main__":
		UpperCamelCase__ :    Optional[Any] 					=							30
		time_func(fib_iterative, num)
		time_func(fib_recursive, num)  # Around 3s runtime
		time_func(fib_recursive_cached, num)  # Around 0ms runtime
		time_func(fib_memoization, num)
		time_func(fib_binet, num)


