"""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				(	_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							Union[str, Any]						):
    __a			      =						copy.deepcopy(_lowerCAmelCase						)
    __a			      =						sorted(_lowerCAmelCase						)
    __a			      =						[bisect.bisect_right(_lowerCAmelCase				, _lowerCAmelCase						) for y in x]
    return quantized

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






class 					a					(				A_							):



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



  def __iter__(		self   :    Optional[Any]     )      -> Any:
      __a			      =						defaultdict(lowerCamelCase_     )
      __a			      =						defaultdict(lowerCamelCase_     )

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



  def __len__(		self   :    Any     )      -> Dict:
      return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size

"""simple docstring"""


from typing import TYPE_CHECKING

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


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
