import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te





def      __lowercase  (		lowerCamelCase      :  Any						,						lowerCamelCase      :  Optional[int]=True						,						lowerCamelCase      :  Union[str, Any]=True						,						lowerCamelCase      :  Any=True			):
 if not is_fpa_available():
  raise ImportError('Using `convert_model` requires transformer_engine to be installed.'			)
 for name, module in model.named_children():
  if isinstance(lowerCamelCase						,						nn.Linear			) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape			):
    return
   UpperCamelCase_      :		Optional[Any]				=      module.bias is not None
   UpperCamelCase_      :		Union[str, Any]				=      te.Linear(
       module.in_features						,						module.out_features						,						bias=lowerCamelCase						,						params_dtype=module.weight.dtype			)
   UpperCamelCase_      :		Tuple				=      module.weight.data.clone()
   if has_bias:
    UpperCamelCase_      :		Union[str, Any]				=      module.bias.data.clone()

   setattr(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)
  elif isinstance(lowerCamelCase						,						nn.LayerNorm			) and to_transformer_engine and _convert_ln:
   UpperCamelCase_      :		Optional[Any]				=      te.LayerNorm(module.normalized_shape[0]						,						eps=module.eps						,						params_dtype=module.weight.dtype			)
   UpperCamelCase_      :		Optional[int]				=      module.weight.data.clone()
   UpperCamelCase_      :		Dict				=      module.bias.data.clone()

   setattr(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)
  elif isinstance(lowerCamelCase						,						te.Linear			) and not to_transformer_engine and _convert_linear:
   UpperCamelCase_      :		Tuple				=      module.bias is not None
   UpperCamelCase_      :		Any				=      nn.Linear(
       module.in_features						,						module.out_features						,						bias=lowerCamelCase						,						params_dtype=module.weight.dtype			)
   UpperCamelCase_      :		Tuple				=      module.weight.data.clone()
   if has_bias:
    UpperCamelCase_      :		Dict				=      module.bias.data.clone()

   setattr(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)
  elif isinstance(lowerCamelCase						,						te.LayerNorm			) and not to_transformer_engine and _convert_ln:
   UpperCamelCase_      :		Dict				=      nn.LayerNorm(module.normalized_shape[0]						,						eps=module.eps						,						params_dtype=module.weight.dtype			)
   UpperCamelCase_      :		Dict				=      module.weight.data.clone()
   UpperCamelCase_      :		List[str]				=      module.bias.data.clone()

   setattr(lowerCamelCase						,						lowerCamelCase						,						lowerCamelCase			)
  else:
   convert_model(
       lowerCamelCase						,						to_transformer_engine=lowerCamelCase						,						_convert_linear=_convert_linear						,						_convert_ln=_convert_ln						,						)






def      __lowercase  (		lowerCamelCase      :  Optional[int]			):
 if not is_fpa_available():
  raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'			)
 for m in model.modules():
  if isinstance(lowerCamelCase						,						(te.LayerNorm, te.Linear)			):
   return True
 return False




from __future__ import annotations

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

a_					      = {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Tuple				=      {start}
 UpperCamelCase_      :		Optional[Any]				=      [start]
 UpperCamelCase_      :		Queue				=      Queue()
 queue.put(lowerCamelCase			)
 while not queue.empty():
  UpperCamelCase_      :		Union[str, Any]				=      queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.put(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  dict						,						lowerCamelCase      :  str			):
 UpperCamelCase_      :		Dict				=      {start}
 UpperCamelCase_      :		Optional[int]				=      [start]
 UpperCamelCase_      :		Union[str, Any]				=      deque([start]			)
 while queue:
  UpperCamelCase_      :		Optional[int]				=      queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(lowerCamelCase			)
    result.append(lowerCamelCase			)
    queue.append(lowerCamelCase			)
 return result





def      __lowercase  (		lowerCamelCase      :  str			):
 UpperCamelCase_      :		str				=      F"from __main__ import G, {name}"
 UpperCamelCase_      :		int				=      10000
 UpperCamelCase_      :		Optional[Any]				=      timeit(F"{name}(G, 'A')"						,						setup=lowerCamelCase						,						number=lowerCamelCase			)
 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



