




'''simple docstring'''




import bisect
import copy
from collections import defaultdict

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

from utils import logger

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ):
 '''simple docstring'''





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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str=0       ):
 '''simple docstring'''





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







class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def __init__(						self    :	Dict						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	List[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 ) )
   UpperCAmelCase__										=    sampler
   UpperCAmelCase__										=    group_ids
   UpperCAmelCase__										=    batch_size


  def __iter__(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    defaultdict(_UpperCAmelCase )
   UpperCAmelCase__										=    defaultdict(_UpperCAmelCase )

   UpperCAmelCase__										=    0
   for idx in self.sampler:
    UpperCAmelCase__										=    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
   UpperCAmelCase__										=    len(self )
   UpperCAmelCase__										=    expected_num_batches - num_batches
   if num_remaining > 0:
    # for the remaining batches, group the batches by similar lengths
    UpperCAmelCase__										=    []
    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]
      UpperCAmelCase__										=    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    :	Optional[Any] ):






   """simple docstring"""






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






'''simple docstring'''




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Dict       ):
 '''simple docstring'''





 if isinstance(SCREAMING_SNAKE_CASE__       ,						(list, tuple)       ):
  return honor_type(SCREAMING_SNAKE_CASE__       ,						(move_to_device(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ) for t in state)       )
 elif isinstance(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ):
  return type(SCREAMING_SNAKE_CASE__       )({k: move_to_device(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ) for k, v in state.items()}       )
 elif isinstance(SCREAMING_SNAKE_CASE__       ,						torch.Tensor       ):
  return state.to(SCREAMING_SNAKE_CASE__       )
 return state







class 						lowerCAmelCase_   ( torch.optim.Optimizer   ):







  '''simple docstring'''







  def __init__(						self    :	List[str]						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Union[str, Any]=True						,		_UpperCAmelCase    :	List[str]=None ):






   """simple docstring"""






   UpperCAmelCase__										=    optimizer
   UpperCAmelCase__										=    scaler
   UpperCAmelCase__										=    AcceleratorState()
   UpperCAmelCase__										=    GradientState()
   UpperCAmelCase__										=    device_placement
   UpperCAmelCase__										=    False
   UpperCAmelCase__										=    None

   # Handle device placement
   if device_placement:
    UpperCAmelCase__										=    self.optimizer.state_dict()
    if self.accelerator_state.distributed_type == DistributedType.TPU:
     xm.send_cpu_data_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
    else:
     UpperCAmelCase__										=    move_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
    self.optimizer.load_state_dict(_UpperCAmelCase )


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   return self.optimizer.state


  @state.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    state


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return self.optimizer.param_groups


  @param_groups.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    param_groups


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   return self.optimizer.defaults


  @defaults.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    defaults


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any] ):






   """simple docstring"""






   self.optimizer.add_param_group(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
    xm.send_cpu_data_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
   self.optimizer.load_state_dict(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   return self.optimizer.state_dict()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any]=None ):






   """simple docstring"""






   if self.gradient_state.sync_gradients:
    UpperCAmelCase__										=    """set_to_none""" in inspect.signature(self.optimizer.zero_grad ).parameters
    if accept_arg:
     if set_to_none is None:
      UpperCAmelCase__										=    False
     self.optimizer.zero_grad(set_to_none=_UpperCAmelCase )
    else:
     if set_to_none is not None:
      raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.""" )
     self.optimizer.zero_grad()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	List[str]=None ):






   """simple docstring"""






   if self.gradient_state.sync_gradients:
    if self.accelerator_state.distributed_type == DistributedType.TPU:
     UpperCAmelCase__										=    {"""closure""": closure} if closure is not None else {}
     xm.optimizer_step(self.optimizer						,		optimizer_args=_UpperCAmelCase )
    elif self.scaler is not None:
     UpperCAmelCase__										=    False
     if self._last_scale is None:
      # `get_scale` is an async operation requiring full synchronization
      # on CPU and GPUs before finishing. As a result, we store away
      # the prior one to reduce the call overhead
      UpperCAmelCase__										=    self.scaler.get_scale()
      UpperCAmelCase__										=    True
     self.scaler.step(self.optimizer						,		_UpperCAmelCase )
     self.scaler.update()
     UpperCAmelCase__										=    self.scaler.get_scale()
     if not new_scale:
      # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
      UpperCAmelCase__										=    scale_after < self._last_scale
     UpperCAmelCase__										=    scale_after

    else:
     self.optimizer.step(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   for param_group in self.optimizer.param_groups:
    UpperCAmelCase__										=    [parameters_map.get(_UpperCAmelCase						,		_UpperCAmelCase ) for p in param_group["""params"""]]


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   warnings.warn(
       """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
       """`optimizer.step_was_skipped` instead."""						,		_UpperCAmelCase						,		)
   return self._is_overflow


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._is_overflow


  def __getstate__(						self    :	Dict ):






   """simple docstring"""






   return self.__dict__.copy()



  def __setstate__(						self    :	Dict						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   self.__dict__.update(_UpperCAmelCase )
