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 SCREAMING_SNAKE_CASE			(       lowercase_:		Dict						,  lowercase_:		Dict = False			):

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







def SCREAMING_SNAKE_CASE			(       lowercase_:		Any = None						,  lowercase_:		List[str] = None			):

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

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

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







def SCREAMING_SNAKE_CASE			(       lowercase_:		List[str]						,  lowercase_:		str = None			):

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




def SCREAMING_SNAKE_CASE			(       lowercase_:		list[int]			):

       # validate:
       if not isinstance(lowercase_						,  lowercase_			) or not all(
           isinstance(lowercase_						,  lowercase_			) and item >= 0 for item in citations			):
              raise ValueError('The citations should be a list of non negative integers.'			)

       citations.sort()
       _UpperCamelCase       :    List[str]					  =					len(lowercase_			)

       for i in range(lowercase_			):
              if citations[len_citations - 1 - i] <= i:
                     return i

       return len_citations


if __name__ == "__main__":
 import doctest

 doctest.testmod()



