

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


_lowerCamelCase       :     int           =							logging.get_logger(__name__)

_lowerCamelCase       :     List[Any]           =							{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  """opt"""
   UpperCamelCase       =  ["""past_key_values"""]



   def __init__(							self							:  int,	__A							:  Optional[Any]=5_0_2_7_2,	__A							:  Tuple=7_6_8,	__A							:  str=1_2,	__A							:  Any=3_0_7_2,	__A							:  Dict=2_0_4_8,	__A							:  Optional[Any]=True,	__A							:  int=False,	__A							:  List[Any]=None,	__A							:  Tuple=0.1,	__A							:  List[str]=0.0,	__A							:  Any=1_2,	__A							:  List[Any]="relu",	__A							:  Union[str, Any]=0.0,	__A							:  Union[str, Any]=0.0_2,	__A							:  int=True,	__A							:  Union[str, Any]=1,	__A							:  List[str]=2,	__A							:  int=2,	__A							:  Optional[int]=True,	__A							:  int=True,	**__A							:  Optional[int],	):
     super().__init__(
         pad_token_id=__A,	bos_token_id=__A,	eos_token_id=__A,	**__A,	)
     UpperCAmelCase :							Optional[int]								=				vocab_size
     UpperCAmelCase :							int								=				max_position_embeddings
     UpperCAmelCase :							Union[str, Any]								=				num_attention_heads
     UpperCAmelCase :							Optional[int]								=				word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     UpperCAmelCase :							str								=				ffn_dim
     UpperCAmelCase :							Dict								=				hidden_size
     UpperCAmelCase :							int								=				num_hidden_layers
     UpperCAmelCase :							List[Any]								=				dropout
     UpperCAmelCase :							List[Any]								=				attention_dropout
     UpperCAmelCase :							Dict								=				activation_function
     UpperCAmelCase :							Optional[Any]								=				init_std
     UpperCAmelCase :							str								=				layerdrop
     UpperCAmelCase :							int								=				use_cache
     UpperCAmelCase :							Tuple								=				do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     UpperCAmelCase :							Tuple								=				enable_bias
     UpperCAmelCase :							List[Any]								=				layer_norm_elementwise_affine

     # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
     # with checkpoints that have been fine-tuned before transformers v4.20.1
     # see https://github.com/facebookresearch/metaseq/pull/164
     UpperCAmelCase :							List[Any]								=				_remove_final_layer_norm








from __future__ import annotations

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

_lowerCamelCase       :     Optional[int]           =							{
    "A": ["B", "C"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B"],
    "E": ["B", "F"],
    "F": ["C", "E"],
}

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							Dict								=				{start}
  UpperCAmelCase :							Optional[int]								=				[start]
  UpperCAmelCase :							Queue								=				Queue()
  queue.put(UpperCAmelCase    )
  while not queue.empty():
    UpperCAmelCase :							List[Any]								=				queue.get()
    for w in graph[v]:
      if w not in explored:
        explored.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.put(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    dict       ,				UpperCAmelCase							:    str    )						->		list[str]:
  UpperCAmelCase :							str								=				{start}
  UpperCAmelCase :							List[str]								=				[start]
  UpperCAmelCase :							Union[str, Any]								=				deque([start]    )
  while queue:
    UpperCAmelCase :							Dict								=				queue.popleft()
    for child in graph[v]:
      if child not in visited:
        visited.add(UpperCAmelCase    )
        result.append(UpperCAmelCase    )
        queue.append(UpperCAmelCase    )
  return result

def     a__  (	UpperCAmelCase							:    str    )						->		None:
  UpperCAmelCase :							Optional[int]								=				f'''from __main__ import G, {name}'''
  UpperCAmelCase :							Tuple								=				10_000
  UpperCAmelCase :							Any								=				timeit(f'''{name}(G, \'A\')'''       ,				setup=UpperCAmelCase       ,				number=UpperCAmelCase    )
  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





