
"""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						, lowercase      )     ->							Optional[int]:


     """simple docstring"""







     UpperCAmelCase_						:    Optional[int]    					=  copy.deepcopy(lowercase      )
     UpperCAmelCase_						:    Optional[int]    					=  sorted(lowercase      )
     UpperCAmelCase_						:    List[str]    					=  [bisect.bisect_right(lowercase						, lowercase      ) for y in x]
     return quantized




def 			A_			(					lowercase						, lowercase=0      )     ->							Tuple:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  np.arange(start=3						, stop=lowercase						, step=4      ).tolist() if k > 0 else [10]
     UpperCAmelCase_						:    Union[str, Any]    					=  _quantize(lowercase						, lowercase      )
     # count number of elements per group
     UpperCAmelCase_						:    int    					=  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 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 def __init__( self       : Optional[Any]     ,   a_       : Union[str, Any]     ,   a_       : str     ,   a_       : Dict )->					Dict:



      """simple docstring"""

      if not isinstance(a_     ,   a_ ):
           raise ValueError(
               """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(a_ ) )
      UpperCAmelCase_						:    Optional[int]    					=  sampler
      UpperCAmelCase_						:    Union[str, Any]    					=  group_ids
      UpperCAmelCase_						:    Optional[int]    					=  batch_size




 def __iter__( self       : Dict )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  defaultdict(a_ )
      UpperCAmelCase_						:    Optional[Any]    					=  defaultdict(a_ )

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






 def __len__( self       : str )->					Optional[Any]:



      """simple docstring"""

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



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

