

"""simple docstring"""




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__A     :    Optional[Any]    =		{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					{start}
					A			=					[start]
					A			=					Queue()
					queue.put(lowercase__      )
					while not queue.empty():
										A			=					queue.get()
										for w in graph[v]:
															if w not in explored:
																				explored.add(lowercase__      )
																				result.append(lowercase__      )
																				queue.put(lowercase__      )
					return result
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					{start}
					A			=					[start]
					A			=					deque([start]      )
					while queue:
										A			=					queue.popleft()
										for child in graph[v]:
															if child not in visited:
																				visited.add(lowercase__      )
																				result.append(lowercase__      )
																				queue.append(lowercase__      )
					return result
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					F"""from __main__ import G, {name}"""
					A			=					10_000
					A			=					timeit(F"""{name}(G, 'A')"""			,							setup=lowercase__			,							number=lowercase__      )
					print(F"""{name:<35} finished {number} runs in {res:.5f} seconds"""      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds








"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





