


'''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      (		_lowercase   :      Union[str, Any]     ,   _lowercase   :      int    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     copy.deepcopy(_lowercase    )
     __UpperCAmelCase      :					List[Any]    				=     sorted(_lowercase    )
     __UpperCAmelCase      :					Union[str, Any]    				=     [bisect.bisect_right(_lowercase     ,   _lowercase    ) for y in x]
     return quantized




def       _a      (		_lowercase   :      Dict     ,   _lowercase   :      Optional[Any]=0    ):





     '''simple docstring'''






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



class       a							(						_a			):


     """simple docstring"""






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

     def __iter__(							self	:						Dict     )		->				Any:
          __UpperCAmelCase      :					Dict    				=     defaultdict(snake_case     )
          __UpperCAmelCase      :					Tuple    				=     defaultdict(snake_case     )

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






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


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "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:
      __UpperCAmelCase     :Any												=  [
          "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

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