
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm

def a__   (	A_,					A_ = False ):






  '''simple docstring'''





  if device_specific:
    seed += AcceleratorState().process_index
  random.seed(A_ )
  np.random.seed(A_ )
  torch.manual_seed(A_ )
  if is_xpu_available():
    torch.xpu.manual_seed_all(A_ )
  elif is_npu_available():
    torch.npu.manual_seed_all(A_ )
  else:
    torch.cuda.manual_seed_all(A_ )
  # ^^ safe to call this function even if cuda is not available
  if is_tpu_available():
    xm.set_rng_state(A_ )

def a__   (	A_ = None,					A_ = None ):






  '''simple docstring'''





  if rng_type == RNGType.TORCH:
    __magic_name__    					=	torch.get_rng_state()
  elif rng_type == RNGType.CUDA:
    __magic_name__    					=	torch.cuda.get_rng_state()
  elif rng_type == RNGType.XLA:
    assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
    __magic_name__    					=	torch.tensor(xm.get_rng_state() )
  elif rng_type == RNGType.NPU:
    assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
    __magic_name__    					=	torch.npu.get_rng_state()
  elif rng_type == RNGType.XPU:
    assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
    __magic_name__    					=	torch.xpu.get_rng_state()
  elif rng_type == RNGType.GENERATOR:
    assert generator is not None, "Need a generator to synchronize its seed."
    __magic_name__    					=	generator.get_state()

  # Broadcast the rng state from device 0 to other devices
  __magic_name__    					=	AcceleratorState()
  if state.distributed_type == DistributedType.TPU:
    __magic_name__    					=	rng_state.to(xm.xla_device() )
    xm.collective_broadcast([rng_state] )
    xm.mark_step()
    __magic_name__    					=	rng_state.cpu()
  elif (
      state.distributed_type in CUDA_DISTRIBUTED_TYPES
      or state.distributed_type == DistributedType.MULTI_NPU
      or state.distributed_type == DistributedType.MULTI_XPU
  ):
    __magic_name__    					=	rng_state.to(state.device )
    torch.distributed.broadcast(A_,					0 )
    __magic_name__    					=	rng_state.cpu()
  elif state.distributed_type == DistributedType.MULTI_CPU:
    torch.distributed.broadcast(A_,					0 )

  # Set the broadcast rng state
  if rng_type == RNGType.TORCH:
    torch.set_rng_state(A_ )
  elif rng_type == RNGType.CUDA:
    torch.cuda.set_rng_state(A_ )
  elif rng_type == RNGType.NPU:
    torch.npu.set_rng_state(A_ )
  elif rng_type == RNGType.XPU:
    torch.xpu.set_rng_state(A_ )
  elif rng_type == RNGType.XLA:
    xm.set_rng_state(rng_state.item() )
  elif rng_type == RNGType.GENERATOR:
    generator.set_state(A_ )





def a__   (	A_,					A_ = None ):






  '''simple docstring'''





  for rng_type in rng_types:
    synchronize_rng_state(RNGType(A_ ),					generator=A_ )



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
    import transformer_engine.pytorch as te

def a__   (	A_,					A_=True,					A_=True,					A_=True ):






  '''simple docstring'''





  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(A_,					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
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	te.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					nn.LayerNorm ) and to_transformer_engine and _convert_ln:
      __magic_name__    					=	te.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

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

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

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





def a__   (	A_ ):






  '''simple docstring'''





  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(A_,					(te.LayerNorm, te.Linear) ):
      return True
  return False

