





import bisect
import copy
from collections import defaultdict

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

from utils import logger




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Optional[Any]:
       UpperCamelCase_      				=     copy.deepcopy(UpperCamelCase_     )
       UpperCamelCase_      				=     sorted(UpperCamelCase_     )
       UpperCamelCase_      				=     [bisect.bisect_right(UpperCamelCase_    ,						UpperCamelCase_     ) for y in x]
       return quantized




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







class 							_UpperCamelCase    (		lowerCAmelCase_   ):

       def __init__(						self:       List[Any]    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]    ,      _SCREAMING_SNAKE_CASE:       str    ,      _SCREAMING_SNAKE_CASE:       List[str]						)				->       List[str]:




              """simple docstring"""


              if not isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                     raise ValueError(
                         "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(_SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     sampler
              UpperCamelCase_      				=     group_ids
              UpperCamelCase_      				=     batch_size

       def __iter__(						self:       Optional[int]						)				->       Optional[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     defaultdict(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     defaultdict(_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     0
              for idx in self.sampler:
                     UpperCamelCase_      				=     self.group_ids[idx]
                     buffer_per_group[group_id].append(_SCREAMING_SNAKE_CASE						)
                     samples_per_group[group_id].append(_SCREAMING_SNAKE_CASE						)
                     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 _SCREAMING_SNAKE_CASE						: x[0]						):
                            batch_idx.extend(_SCREAMING_SNAKE_CASE						)
                            if len(_SCREAMING_SNAKE_CASE						) >= self.batch_size:
                                   yield batch_idx[: self.batch_size]
                                   UpperCamelCase_      				=     batch_idx[self.batch_size :]
                                   num_remaining -= 1
                     if len(_SCREAMING_SNAKE_CASE						) > 0:
                            yield batch_idx
                            num_remaining -= 1
              assert num_remaining == 0



       def __len__(						self:       Dict						)				->       Union[str, Any]:




              """simple docstring"""


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








import warnings

from .generation import GenerationMixin



class 							_UpperCamelCase    (		lowerCAmelCase_   ):
       # warning at import time
       warnings.warn(
           '''Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '''
           '''be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'''     ,  lowerCAmelCase_     ,  )

