





"""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 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       copy.deepcopy(lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       sorted(lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [bisect.bisect_right(lowercase_ ,      lowercase_		) for y in x]
 return quantized




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Optional[Any]=0		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       np.arange(start=3 ,      stop=lowercase_ ,      step=4		).tolist() if k > 0 else [10]
 __SCREAMING_SNAKE_CASE :			int												=       _quantize(lowercase_ ,      lowercase_		)
 # count number of elements per group
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       np.unique(lowercase_ ,      return_counts=lowercase_		)[1]
 __SCREAMING_SNAKE_CASE :			Dict												=       [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 			snake_case		(   __UpperCAmelCase   ):

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

      def __iter__(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       defaultdict(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       defaultdict(_lowerCamelCase						)

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








"""simple docstring"""





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= c_float

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       return sizeof(self.value						)







def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 if dimension <= 0:
  __SCREAMING_SNAKE_CASE :			str												=       fixed_dimension

 dimension -= num_token_to_add
 return dimension




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							ParameterFormat		):

 '''simple docstring'''






 return num_parameters * dtype.size






def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(lowercase_		)
 except (ValueError, OSError, KeyError):
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       None, None
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoTokenizer.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass

  if tokenizer is not None and feature_extractor is not None:
   raise ValueError(
       F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''		)
  elif tokenizer is None and feature_extractor is None:
   return None
  elif tokenizer is not None:
   return tokenizer
  else:
   return feature_extractor

