"""simple docstring"""




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 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__ = False					):






							"""simple docstring"""





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






def 	UpperCAmelCase       (						UpperCamelCase__ = None							,     UpperCamelCase__ = None					):






							"""simple docstring"""





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

							# Broadcast the rng state from device 0 to other devices
							A__						  =				AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														A__						  =				rng_state.to(xm.xla_device()					)
														xm.collective_broadcast([rng_state]					)
														xm.mark_step()
														A__						  =				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
							):
														A__						  =				rng_state.to(state.device					)
														torch.distributed.broadcast(_SCREAMING_SNAKE_CASE							,     0					)
														A__						  =				rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(_SCREAMING_SNAKE_CASE							,     0					)

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__ = None					):






							"""simple docstring"""





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






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


