


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_     ):
       lowerCamelCase__:  Union[str, Any]				     =  """convnext"""

       def __init__( self   :  str   ,				lowerCamelCase_   :  Union[str, Any]=3   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  int=None   ,				lowerCamelCase_   :  Optional[Any]=None   ,				lowerCamelCase_   :  Dict="gelu"   ,				lowerCamelCase_   :  str=0.0_2   ,				lowerCamelCase_   :  Dict=1E-12   ,				lowerCamelCase_   :  List[str]=1E-6   ,				lowerCamelCase_   :  Union[str, Any]=0.0   ,				lowerCamelCase_   :  Optional[Any]=2_2_4   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Dict=None   ,				**lowerCamelCase_   :  List[Any]   ,				):
            super().__init__(**lowerCamelCase_     )

            a_     :  List[str]				      =     num_channels
            a_     :  int				      =     patch_size
            a_     :  List[Any]				      =     num_stages
            a_     :  Optional[int]				      =     [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
            a_     :  Any				      =     [3, 3, 9, 3] if depths is None else depths
            a_     :  Any				      =     hidden_act
            a_     :  Dict				      =     initializer_range
            a_     :  str				      =     layer_norm_eps
            a_     :  Union[str, Any]				      =     layer_scale_init_value
            a_     :  int				      =     drop_path_rate
            a_     :  Tuple				      =     image_size
            a_     :  Tuple				      =     ["""stem"""] + [F'''stage{idx}''' for idx in range(1   ,				len(self.depths     ) + 1     )]
            a_      ,      a_     :  List[str]				      =     get_aligned_output_features_output_indices(
                out_features=lowerCamelCase_   ,				out_indices=lowerCamelCase_   ,				stage_names=self.stage_names     )


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  Tuple				     =  version.parse("""1.11"""     )

       @property
       def     UpperCAmelCase( self   :  str     ):
            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]     )

       @property
       def     UpperCAmelCase( self   :  Optional[Any]     ):
            return 1E-5







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



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

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

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

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



