"""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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	Optional[Any]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  a__:	List[str]     				=      sorted(_SCREAMING_SNAKE_CASE  )
  a__:	List[Any]     				=      [bisect.bisect_right(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ) for y in x]
  return quantized







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=0  )      ->Tuple:
  a__:	Optional[Any]     				=      np.arange(start=3						,	stop=_SCREAMING_SNAKE_CASE						,	step=4  ).tolist() if k > 0 else [10]
  a__:	Optional[Any]     				=      _quantize(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  # count number of elements per group
  a__:	Any     				=      np.unique(_SCREAMING_SNAKE_CASE						,	return_counts=_SCREAMING_SNAKE_CASE  )[1]
  a__:	Dict     				=      [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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          raise ValueError(
              'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase))
        a__:	List[str]     				=      sampler
        a__:	List[str]     				=      group_ids
        a__:	Tuple     				=      batch_size



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





        '''simple docstring'''





        a__:	Union[str, Any]     				=      defaultdict(lowercase)
        a__:	Dict     				=      defaultdict(lowercase)

        a__:	Optional[int]     				=      0
        for idx in self.sampler:
          a__:	Optional[int]     				=      self.group_ids[idx]
          buffer_per_group[group_id].append(lowercase)
          samples_per_group[group_id].append(lowercase)
          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
        a__:	List[str]     				=      len(self)
        a__:	List[str]     				=      expected_num_batches - num_batches
        if num_remaining > 0:
          # for the remaining batches, group the batches by similar lengths
          a__:	Dict     				=      []
          for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowercase: x[0]):
            batch_idx.extend(lowercase)
            if len(lowercase) >= self.batch_size:
              yield batch_idx[: self.batch_size]
              a__:	List[Any]     				=      batch_idx[self.batch_size :]
              num_remaining -= 1
          if len(lowercase) > 0:
            yield batch_idx
            num_remaining -= 1
        assert num_remaining == 0



      def __len__(		self)      ->     int:





        '''simple docstring'''





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



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__   			=      {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      ['MPNetTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MPNetForMaskedLM',
     'MPNetForMultipleChoice',
     'MPNetForQuestionAnswering',
     'MPNetForSequenceClassification',
     'MPNetForTokenClassification',
     'MPNetLayer',
     'MPNetModel',
     'MPNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__   			=      [
     'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMPNetEmbeddings',
     'TFMPNetForMaskedLM',
     'TFMPNetForMultipleChoice',
     'TFMPNetForQuestionAnswering',
     'TFMPNetForSequenceClassification',
     'TFMPNetForTokenClassification',
     'TFMPNetMainLayer',
     'TFMPNetModel',
     'TFMPNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 lowercase__   			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


