code: 
  
  
  
  
  
  '''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      :      str		, _lowerCamelCase      :      Optional[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      :      Tuple=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      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    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__       =    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 lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    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							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  