


"""simple docstring"""





import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

snake_case__						:   Dict			 = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
snake_case__						:   int			 = "https://storage.googleapis.com/cvdf-datasets/mnist/"


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[Any]				=      numpy.dtype(numpy.uintaa	).newbyteorder('''>'''	)
      return numpy.frombuffer(bytestream.read(4	)       ,      dtype=_UpperCamelCase	)[0]


@deprecated(_UpperCamelCase       ,      '''Please use tf.data to implement this functionality.'''	)
def    _snake_case  (  _snake_case :	Union[str, Any]	):
      print('''Extracting'''       ,      f.name	)
      with gzip.GzipFile(fileobj=_UpperCamelCase	) as bytestream:
            lowerCAmelCase    :		Tuple				=      _readaa(_UpperCamelCase	)
            if magic != 2051:
                  raise ValueError(
                      '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)	)
            lowerCAmelCase    :		Union[str, Any]				=      _readaa(_UpperCamelCase	)
            lowerCAmelCase    :		Any				=      _readaa(_UpperCamelCase	)
            lowerCAmelCase    :		Tuple				=      _readaa(_UpperCamelCase	)
            lowerCAmelCase    :		Dict				=      bytestream.read(rows * cols * num_images	)
            lowerCAmelCase    :		List[str]				=      numpy.frombuffer(_UpperCamelCase       ,      dtype=numpy.uinta	)
            lowerCAmelCase    :		List[str]				=      data.reshape(_UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase       ,      1	)
            return data


@deprecated(_UpperCamelCase       ,      '''Please use tf.one_hot on tensors.'''	)
def    _snake_case  (  _snake_case :	int       ,      _snake_case :	int	):
      lowerCAmelCase    :		Dict				=      labels_dense.shape[0]
      lowerCAmelCase    :		List[Any]				=      numpy.arange(_UpperCamelCase	) * num_classes
      lowerCAmelCase    :		Dict				=      numpy.zeros((num_labels, num_classes)	)
      lowerCAmelCase    :		str				=      1
      return labels_one_hot


@deprecated(_UpperCamelCase       ,      '''Please use tf.data to implement this functionality.'''	)
def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Tuple=False       ,      _snake_case :	Tuple=10	):
      print('''Extracting'''       ,      f.name	)
      with gzip.GzipFile(fileobj=_UpperCamelCase	) as bytestream:
            lowerCAmelCase    :		Tuple				=      _readaa(_UpperCamelCase	)
            if magic != 2049:
                  raise ValueError(
                      '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)	)
            lowerCAmelCase    :		Any				=      _readaa(_UpperCamelCase	)
            lowerCAmelCase    :		Union[str, Any]				=      bytestream.read(_UpperCamelCase	)
            lowerCAmelCase    :		List[Any]				=      numpy.frombuffer(_UpperCamelCase       ,      dtype=numpy.uinta	)
            if one_hot:
                  return _dense_to_one_hot(_UpperCamelCase       ,      _UpperCamelCase	)
            return labels



class 						snake_case_:






     @deprecated(
         _a    , '''Please use alternatives such as official/mnist/_DataSet.py'''
         ''' from tensorflow/models.'''    , )
     def __init__(		self     :    Any    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Dict=False    , UpperCamelCase_     :    Tuple=False    , UpperCamelCase_     :    List[Any]=dtypes.floataa    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    List[Any]=None    , ):
           lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      random_seed.get_seed(_a )
           # If op level seed is not set, use whatever graph level seed is returned
           numpy.random.seed(seeda if seed is None else seeda )
           lowerCAmelCase    :		List[Any]				=      dtypes.as_dtype(_a ).base_dtype
           if dtype not in (dtypes.uinta, dtypes.floataa):
                 raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
           if fake_data:
                 lowerCAmelCase    :		Optional[int]				=      1_0_0_0_0
                 lowerCAmelCase    :		Optional[Any]				=      one_hot
           else:
                 assert (
                     images.shape[0] == labels.shape[0]
                 ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
                 lowerCAmelCase    :		str				=      images.shape[0]

                 # Convert shape from [num examples, rows, columns, depth]
                 # to [num examples, rows*columns] (assuming depth == 1)
                 if reshape:
                       assert images.shape[3] == 1
                       lowerCAmelCase    :		int				=      images.reshape(
                           images.shape[0]    , images.shape[1] * images.shape[2] )
                 if dtype == dtypes.floataa:
                       # Convert from [0, 255] -> [0.0, 1.0].
                       lowerCAmelCase    :		int				=      images.astype(numpy.floataa )
                       lowerCAmelCase    :		List[str]				=      numpy.multiply(_a    , 1.0 / 2_5_5.0 )
           lowerCAmelCase    :		int				=      images
           lowerCAmelCase    :		str				=      labels
           lowerCAmelCase    :		Union[str, Any]				=      0
           lowerCAmelCase    :		str				=      0






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return self._images






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return self._labels






     @property
     def    lowerCamelCase__   (		self     :    List[Any] ):
           return self._num_examples






     @property
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           return self._epochs_completed






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    Dict=False    , UpperCamelCase_     :    Tuple=True ):






           if fake_data:
                 lowerCAmelCase    :		List[str]				=      [1] * 7_8_4
                 lowerCAmelCase    :		int				=      [1] + [0] * 9 if self.one_hot else 0
                 return (
                     [fake_image for _ in range(_a )],
                     [fake_label for _ in range(_a )],
                 )
           lowerCAmelCase    :		Optional[Any]				=      self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
                 lowerCAmelCase    :		str				=      numpy.arange(self._num_examples )
                 numpy.random.shuffle(_a )
                 lowerCAmelCase    :		Any				=      self.images[perma]
                 lowerCAmelCase    :		Union[str, Any]				=      self.labels[perma]
           # Go to the next epoch
           if start + batch_size > self._num_examples:
                 # Finished epoch
                 self._epochs_completed += 1
                 # Get the rest examples in this epoch
                 lowerCAmelCase    :		int				=      self._num_examples - start
                 lowerCAmelCase    :		Any				=      self._images[start : self._num_examples]
                 lowerCAmelCase    :		List[str]				=      self._labels[start : self._num_examples]
                 # Shuffle the data
                 if shuffle:
                       lowerCAmelCase    :		List[str]				=      numpy.arange(self._num_examples )
                       numpy.random.shuffle(_a )
                       lowerCAmelCase    :		Optional[Any]				=      self.images[perm]
                       lowerCAmelCase    :		Dict				=      self.labels[perm]
                 # Start next epoch
                 lowerCAmelCase    :		List[Any]				=      0
                 lowerCAmelCase    :		Tuple				=      batch_size - rest_num_examples
                 lowerCAmelCase    :		Optional[int]				=      self._index_in_epoch
                 lowerCAmelCase    :		Optional[Any]				=      self._images[start:end]
                 lowerCAmelCase    :		Tuple				=      self._labels[start:end]
                 return (
                     numpy.concatenate((images_rest_part, images_new_part)    , axis=0 ),
                     numpy.concatenate((labels_rest_part, labels_new_part)    , axis=0 ),
                 )
           else:
                 self._index_in_epoch += batch_size
                 lowerCAmelCase    :		Tuple				=      self._index_in_epoch
                 return self._images[start:end], self._labels[start:end]



@deprecated(_UpperCamelCase       ,      '''Please write your own downloading logic.'''	)
def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Optional[int]       ,      _snake_case :	List[Any]	):




      if not gfile.Exists(_UpperCamelCase	):
            gfile.MakeDirs(_UpperCamelCase	)
      lowerCAmelCase    :		List[Any]				=      os.path.join(_UpperCamelCase       ,      _UpperCamelCase	)
      if not gfile.Exists(_UpperCamelCase	):
            urllib.request.urlretrieve(_UpperCamelCase       ,      _UpperCamelCase	)  # noqa: S310
            with gfile.GFile(_UpperCamelCase	) as f:
                  lowerCAmelCase    :		List[str]				=      f.size()
            print('''Successfully downloaded'''       ,      _UpperCamelCase       ,      _UpperCamelCase       ,      '''bytes.'''	)
      return filepath






@deprecated(
    _UpperCamelCase       ,      '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''	)
def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	str=False       ,      _snake_case :	str=False       ,      _snake_case :	Any=dtypes.floataa       ,      _snake_case :	Any=True       ,      _snake_case :	Any=5000       ,      _snake_case :	Optional[int]=None       ,      _snake_case :	List[Any]=DEFAULT_SOURCE_URL       ,      ):




      if fake_data:

            def fake():
                  return _DataSet(
                      []       ,      []       ,      fake_data=_UpperCamelCase       ,      one_hot=_UpperCamelCase       ,      dtype=_UpperCamelCase       ,      seed=_UpperCamelCase	)

            lowerCAmelCase    :		List[Any]				=      fake()
            lowerCAmelCase    :		List[str]				=      fake()
            lowerCAmelCase    :		Optional[int]				=      fake()
            return _Datasets(train=_UpperCamelCase       ,      validation=_UpperCamelCase       ,      test=_UpperCamelCase	)

      if not source_url:  # empty string check
            lowerCAmelCase    :		List[Any]				=      DEFAULT_SOURCE_URL

      lowerCAmelCase    :		List[Any]				=      '''train-images-idx3-ubyte.gz'''
      lowerCAmelCase    :		Dict				=      '''train-labels-idx1-ubyte.gz'''
      lowerCAmelCase    :		Union[str, Any]				=      '''t10k-images-idx3-ubyte.gz'''
      lowerCAmelCase    :		Any				=      '''t10k-labels-idx1-ubyte.gz'''

      lowerCAmelCase    :		Dict				=      _maybe_download(
          _UpperCamelCase       ,      _UpperCamelCase       ,      source_url + train_images_file	)
      with gfile.Open(_UpperCamelCase       ,      '''rb'''	) as f:
            lowerCAmelCase    :		Tuple				=      _extract_images(_UpperCamelCase	)

      lowerCAmelCase    :		Dict				=      _maybe_download(
          _UpperCamelCase       ,      _UpperCamelCase       ,      source_url + train_labels_file	)
      with gfile.Open(_UpperCamelCase       ,      '''rb'''	) as f:
            lowerCAmelCase    :		str				=      _extract_labels(_UpperCamelCase       ,      one_hot=_UpperCamelCase	)

      lowerCAmelCase    :		Dict				=      _maybe_download(
          _UpperCamelCase       ,      _UpperCamelCase       ,      source_url + test_images_file	)
      with gfile.Open(_UpperCamelCase       ,      '''rb'''	) as f:
            lowerCAmelCase    :		Any				=      _extract_images(_UpperCamelCase	)

      lowerCAmelCase    :		List[str]				=      _maybe_download(
          _UpperCamelCase       ,      _UpperCamelCase       ,      source_url + test_labels_file	)
      with gfile.Open(_UpperCamelCase       ,      '''rb'''	) as f:
            lowerCAmelCase    :		Optional[Any]				=      _extract_labels(_UpperCamelCase       ,      one_hot=_UpperCamelCase	)

      if not 0 <= validation_size <= len(_UpperCamelCase	):
            lowerCAmelCase    :		List[Any]				=      (
                '''Validation size should be between 0 and '''
                f'''{len(_UpperCamelCase	)}. Received: {validation_size}.'''
            )
            raise ValueError(_UpperCamelCase	)

      lowerCAmelCase    :		Any				=      train_images[:validation_size]
      lowerCAmelCase    :		List[str]				=      train_labels[:validation_size]
      lowerCAmelCase    :		Union[str, Any]				=      train_images[validation_size:]
      lowerCAmelCase    :		Optional[Any]				=      train_labels[validation_size:]

      lowerCAmelCase    :		Any				=      {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

      lowerCAmelCase    :		List[str]				=      _DataSet(_UpperCamelCase       ,      _UpperCamelCase       ,      **_UpperCamelCase	)
      lowerCAmelCase    :		Tuple				=      _DataSet(_UpperCamelCase       ,      _UpperCamelCase       ,      **_UpperCamelCase	)
      lowerCAmelCase    :		Any				=      _DataSet(_UpperCamelCase       ,      _UpperCamelCase       ,      **_UpperCamelCase	)

      return _Datasets(train=_UpperCamelCase       ,      validation=_UpperCamelCase       ,      test=_UpperCamelCase	)





'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
            _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _SCREAMING_SNAKE_CASE							    =num_queries
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =init_xavier_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =auxiliary_loss
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       # deformable attributes
       _SCREAMING_SNAKE_CASE							    =num_feature_levels
       _SCREAMING_SNAKE_CASE							    =encoder_n_points
       _SCREAMING_SNAKE_CASE							    =decoder_n_points
       _SCREAMING_SNAKE_CASE							    =two_stage
       _SCREAMING_SNAKE_CASE							    =two_stage_num_proposals
       _SCREAMING_SNAKE_CASE							    =with_box_refine
       _SCREAMING_SNAKE_CASE							    =assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('If two_stage is True, with_box_refine must be True.' )
       # Hungarian matcher
       _SCREAMING_SNAKE_CASE							    =class_cost
       _SCREAMING_SNAKE_CASE							    =bbox_cost
       _SCREAMING_SNAKE_CASE							    =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							    =mask_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =dice_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =eos_coefficient
       _SCREAMING_SNAKE_CASE							    =focal_alpha
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output
