


import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union




class A  (       enum.Enum					):
      UpperCamelCase__							:  List[Any]				='timm'
      UpperCamelCase__							:  List[Any]				='transformers'




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Iterable[str]]		,      SCREAMING_SNAKE_CASE__ :				Optional[Iterable[int]]		,      SCREAMING_SNAKE_CASE__ :				Optional[Iterable[str]]					):
       '''simple docstring'''





       if stage_names is None:
              raise ValueError('Stage_names must be set for transformers backbones'					)

       if out_features is not None:
              if not isinstance(SCREAMING_SNAKE_CASE__		,      (list,)					):
                     raise ValueError(F'''out_features must be a list {type(SCREAMING_SNAKE_CASE__					)}'''					)
              if any(feat not in stage_names for feat in out_features					):
                     raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''					)

       if out_indices is not None:
              if not isinstance(SCREAMING_SNAKE_CASE__		,      (list, tuple)					):
                     raise ValueError(F'''out_indices must be a list or tuple, got {type(SCREAMING_SNAKE_CASE__					)}'''					)
              if any(idx >= len(SCREAMING_SNAKE_CASE__					) for idx in out_indices					):
                     raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'					)

       if out_features is not None and out_indices is not None:
              if len(SCREAMING_SNAKE_CASE__					) != len(SCREAMING_SNAKE_CASE__					):
                     raise ValueError('out_features and out_indices should have the same length if both are set'					)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError('out_features and out_indices should correspond to the same stages if both are set'					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[List[str]]		,      SCREAMING_SNAKE_CASE__ :				Optional[Union[List[int], Tuple[int]]]		,      SCREAMING_SNAKE_CASE__ :				List[str]		,      ):
       '''simple docstring'''





       if out_indices is None and out_features is None:
              _lowerCamelCase	:		str          =[len(SCREAMING_SNAKE_CASE__					) - 1]
              _lowerCamelCase	:		List[Any]          =[stage_names[-1]]
       elif out_indices is None and out_features is not None:
              _lowerCamelCase	:		List[str]          =[stage_names.index(SCREAMING_SNAKE_CASE__					) for layer in out_features]
       elif out_features is None and out_indices is not None:
              _lowerCamelCase	:		Tuple          =[stage_names[idx] for idx in out_indices]
       return out_features, out_indices




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[List[str]]		,      SCREAMING_SNAKE_CASE__ :				Optional[Union[List[int], Tuple[int]]]		,      SCREAMING_SNAKE_CASE__ :				List[str]		,      ):
       '''simple docstring'''





       verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE__		,      out_indices=SCREAMING_SNAKE_CASE__		,      stage_names=SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =_align_output_features_output_indices(
           out_features=SCREAMING_SNAKE_CASE__		,      out_indices=SCREAMING_SNAKE_CASE__		,      stage_names=SCREAMING_SNAKE_CASE__					)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE__		,      out_indices=SCREAMING_SNAKE_CASE__		,      stage_names=SCREAMING_SNAKE_CASE__					)
       return output_features, output_indices




class A  :
      UpperCamelCase__							:  Optional[BackboneType]				=None






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]			) ->						None:
             """simple docstring"""




             if getattr(self      ,       '_backbone'      ,       lowercase_			) is None:
                    raise ValueError('self._backbone must be set before calling _init_timm_backbone'			)

             # These will diagree with the defaults for the transformers models e.g. for resnet50
             # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
             # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
             _lowerCamelCase	:		Dict          =[stage['module'] for stage in self._backbone.feature_info.info]
             _lowerCamelCase	:		List[Any]          =[stage['num_chs'] for stage in self._backbone.feature_info.info]
             _lowerCamelCase	:		List[Any]          =self._backbone.feature_info.out_indices
             _lowerCamelCase	:		Any          =self._backbone.feature_info.module_name()

             # We verify the out indices and out features are valid
             verify_out_features_out_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=self.stage_names			)
             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =out_features, out_indices






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Any          =getattr(lowercase_      ,       'stage_names'			)
             _lowerCamelCase	:		str          =getattr(lowercase_      ,       'out_features'      ,       lowercase_			)
             _lowerCamelCase	:		List[Any]          =getattr(lowercase_      ,       'out_indices'      ,       lowercase_			)

             _lowerCamelCase	:		Any          =stage_names
             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =get_aligned_output_features_output_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=lowercase_			)
             # Number of channels for each stage. This is set in the transformer backbone model init
             _lowerCamelCase	:		List[str]          =None






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					List[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =config

             _lowerCamelCase	:		int          =getattr(lowercase_      ,       'use_timm_backbone'      ,       lowercase_			)
             _lowerCamelCase	:		Dict          =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

             if self.backbone_type == BackboneType.TIMM:
                    self._init_timm_backbone(lowercase_			)
             elif self.backbone_type == BackboneType.TRANSFORMERS:
                    self._init_transformers_backbone(lowercase_			)
             else:
                    raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''			)






      @property
      def 	lowerCamelCase	( self						:					str			) ->						str:
             """simple docstring"""




             return self._out_features






      @out_features.setter
      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =get_aligned_output_features_output_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=self.stage_names			)






      @property
      def 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[Any]:
             """simple docstring"""




             return self._out_indices






      @out_indices.setter
      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Union[Tuple[int], List[int]]			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Any          =get_aligned_output_features_output_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=self.stage_names			)






      @property
      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Optional[Any]:
             """simple docstring"""




             return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names			)}






      @property
      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """simple docstring"""




             return [self.out_feature_channels[name] for name in self.out_features]






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       *lowercase_						:					Optional[int]      ,       **lowercase_						:					Optional[int]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =dict(inspect.signature(self.forward			).parameters			)
             _lowerCamelCase	:		Optional[int]          ={k: v for k, v in kwargs.items() if k in signature}
             return self(*lowercase_      ,       **lowercase_			)






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[bool] = None      ,       lowercase_						:					Optional[bool] = None      ,       lowercase_						:					Optional[bool] = None      ,       ) ->						Tuple:
             """simple docstring"""




             raise NotImplementedError('This method should be implemented by the derived class.'			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =super().to_dict()
             _lowerCamelCase	:		Union[str, Any]          =output.pop('_out_features'			)
             _lowerCamelCase	:		Tuple          =output.pop('_out_indices'			)
             return output




class A  :






      @property
      def 	lowerCamelCase	( self						:					int			) ->						List[str]:
             """simple docstring"""




             return self._out_features






      @out_features.setter
      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					List[str]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =get_aligned_output_features_output_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=self.stage_names			)






      @property
      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Any:
             """simple docstring"""




             return self._out_indices






      @out_indices.setter
      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					Union[Tuple[int], List[int]]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Any          =get_aligned_output_features_output_indices(
                 out_features=lowercase_      ,       out_indices=lowercase_      ,       stage_names=self.stage_names			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		str          =super().to_dict()
             _lowerCamelCase	:		Any          =output.pop('_out_features'			)
             _lowerCamelCase	:		List[Any]          =output.pop('_out_indices'			)
             return output






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_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





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




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





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




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =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
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    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]
                                  _lowerCamelCase	:		str          =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						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




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


