
"""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 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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      if len(buffer_per_group[group_id]   ) == self.batch_size:
        yield buffer_per_group[group_id]  # TODO
        num_batches += 1
        del buffer_per_group[group_id]
      assert len(buffer_per_group[group_id]   ) < self.batch_size

    # now we have run out of elements that satisfy
    # the group criteria, let's return the remaining
    # elements so that the size of the sampler is
    # deterministic
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





  def __len__(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

