





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


_a						=   logging.get_logger(__name__)

_a						=   {
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}






class       __A					(					__a				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""funnel"""
			lowerCAmelCase_							  =		{
			    """hidden_size""": """d_model""",
			    """num_attention_heads""": """n_head""",
			}





			def __init__(					self ,						__lowerCAmelCase=3_0_5_2_2 ,						__lowerCAmelCase=[4, 4, 4] ,						__lowerCAmelCase=None ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=7_6_8 ,						__lowerCAmelCase=1_2 ,						__lowerCAmelCase=6_4 ,						__lowerCAmelCase=3_0_7_2 ,						__lowerCAmelCase="gelu_new" ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.0 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1E-9 ,						__lowerCAmelCase="mean" ,						__lowerCAmelCase="relative_shift" ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	vocab_size
				lowerCamelCase__              =	block_sizes
				lowerCamelCase__              =	[1] * len(A__							) if block_repeats is None else block_repeats
				assert len(A__							) == len(
				    self.block_repeats							), "`block_sizes` and `block_repeats` should have the same length."
				lowerCamelCase__              =	num_decoder_layers
				lowerCamelCase__              =	d_model
				lowerCamelCase__              =	n_head
				lowerCamelCase__              =	d_head
				lowerCamelCase__              =	d_inner
				lowerCamelCase__              =	hidden_act
				lowerCamelCase__              =	hidden_dropout
				lowerCamelCase__              =	attention_dropout
				lowerCamelCase__              =	activation_dropout
				lowerCamelCase__              =	initializer_range
				lowerCamelCase__              =	initializer_std
				lowerCamelCase__              =	layer_norm_eps
				assert pooling_type in [
				    "mean",
				    "max",
				], F'Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'
				lowerCamelCase__              =	pooling_type
				assert attention_type in [
				    "relative_shift",
				    "factorized",
				], F'Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'
				lowerCamelCase__              =	attention_type
				lowerCamelCase__              =	separate_cls
				lowerCamelCase__              =	truncate_seq
				lowerCamelCase__              =	pool_q_only

				super().__init__(**A__							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return sum(self.block_sizes							)





			@num_hidden_layers.setter
			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				raise NotImplementedError(
				    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return len(self.block_sizes							)





			@num_blocks.setter
			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''							)










from queue import PriorityQueue
from typing import Any

import numpy as np





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)       ->   float | int:







	'''simple docstring'''







	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		lowerCamelCase__              =	cst_fwd.get(__snake_case ,np.inf							)
		lowerCamelCase__              =	cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)							)
			lowerCamelCase__              =	new_cost_f
			lowerCamelCase__              =	v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				lowerCamelCase__              =	cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	-1

	lowerCamelCase__              =	set()
	lowerCamelCase__              =	set()
	lowerCamelCase__              =	{source: 0}
	lowerCamelCase__              =	{destination: 0}
	lowerCamelCase__              =	{source: None}
	lowerCamelCase__              =	{destination: None}
	lowerCamelCase__              =	PriorityQueue()
	lowerCamelCase__              =	PriorityQueue()

	lowerCamelCase__              =	np.inf

	queue_forward.put((0, source)							)
	queue_backward.put((0, destination)							)

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		lowerCamelCase__       ,			lowerCamelCase__              =	queue_forward.get()
		visited_forward.add(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	queue_backward.get()
		visited_backward.add(__snake_case							)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		lowerCamelCase__              =	shortest_distance
	return shortest_path_distance


_a						=   {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
_a						=   {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
			import doctest

			doctest.testmod()



