

import bisect
import copy
from collections import defaultdict

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

from utils import logger
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Tuple:



    '''simple docstring'''






    lowerCAmelCase    			=      copy.deepcopy(_UpperCAmelCase				)
    lowerCAmelCase    			=      sorted(_UpperCAmelCase				)
    lowerCAmelCase    			=      [bisect.bisect_right(_UpperCAmelCase,      _UpperCAmelCase				) for y in x]
    return quantized
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase=0				)					-> Dict:



    '''simple docstring'''






    lowerCAmelCase    			=      np.arange(start=3,      stop=_UpperCAmelCase,      step=4				).tolist() if k > 0 else [10]
    lowerCAmelCase    			=      _quantize(_UpperCAmelCase,      _UpperCAmelCase				)
    # count number of elements per group
    lowerCAmelCase    			=      np.unique(_UpperCAmelCase,      return_counts=_UpperCAmelCase				)[1]
    lowerCAmelCase    			=      [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    __A				(							lowerCAmelCase ):






    def __init__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    int    ):
        if not isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ):
            raise ValueError(
                'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(UpperCAmelCase_    )    )
        lowerCAmelCase    			=      sampler
        lowerCAmelCase    			=      group_ids
        lowerCAmelCase    			=      batch_size






    def __iter__(	self			:    Union[str, Any]    ):
        lowerCAmelCase    			=      defaultdict(UpperCAmelCase_    )
        lowerCAmelCase    			=      defaultdict(UpperCAmelCase_    )

        lowerCAmelCase    			=      0
        for idx in self.sampler:
            lowerCAmelCase    			=      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
        lowerCAmelCase    			=      len(self    )
        lowerCAmelCase    			=      expected_num_batches - num_batches
        if num_remaining > 0:
            # for the remaining batches, group the batches by similar lengths
            lowerCAmelCase    			=      []
            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]
                    lowerCAmelCase    			=      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[int]    ):
        return (len(self.sampler    ) + self.batch_size - 1) // self.batch_size








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A   :     Optional[int]      =			{
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Optional[int]      =			['''SplinterTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Union[str, Any]      =			[
     '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SplinterForQuestionAnswering''',
     '''SplinterForPreTraining''',
     '''SplinterLayer''',
     '''SplinterModel''',
     '''SplinterPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

 __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





