






'''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

UpperCamelCase__			=      collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, 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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   print("Extracting"    ,		f.name				)
   with gzip.GzipFile(fileobj=_UpperCamelCase				) as bytestream:
      lowercase_						:     Any							     =  _readaa(_UpperCamelCase				)
      if magic != 2051:
         raise ValueError(
             "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)				)
      lowercase_						:     str							     =  _readaa(_UpperCamelCase				)
      lowercase_						:     Optional[Any]							     =  _readaa(_UpperCamelCase				)
      lowercase_						:     Tuple							     =  _readaa(_UpperCamelCase				)
      lowercase_						:     Optional[Any]							     =  bytestream.read(rows * cols * num_images				)
      lowercase_						:     int							     =  numpy.frombuffer(_UpperCamelCase    ,		dtype=numpy.uinta				)
      lowercase_						:     Union[str, Any]							     =  data.reshape(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		1				)
      return data




@deprecated(_UpperCamelCase    ,		"Please use tf.one_hot on tensors."				)
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  labels_dense.shape[0]
   lowercase_						:     List[str]							     =  numpy.arange(_UpperCamelCase				) * num_classes
   lowercase_						:     int							     =  numpy.zeros((num_labels, num_classes)				)
   lowercase_						:     Optional[int]							     =  1
   return labels_one_hot




@deprecated(_UpperCamelCase    ,		"Please use tf.data to implement this functionality."				)
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=False    ,		_UpperCamelCase=10				):


   """simple docstring"""




   print("Extracting"    ,		f.name				)
   with gzip.GzipFile(fileobj=_UpperCamelCase				) as bytestream:
      lowercase_						:     List[Any]							     =  _readaa(_UpperCamelCase				)
      if magic != 2049:
         raise ValueError(
             "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)				)
      lowercase_						:     str							     =  _readaa(_UpperCamelCase				)
      lowercase_						:     str							     =  bytestream.read(_UpperCamelCase				)
      lowercase_						:     Tuple							     =  numpy.frombuffer(_UpperCamelCase    ,		dtype=numpy.uinta				)
      if one_hot:
         return _dense_to_one_hot(_UpperCamelCase    ,		_UpperCamelCase				)
      return labels


class 							_UpperCAmelCase    :




    @deprecated(
        a	,  "Please use alternatives such as official/mnist/_DataSet.py"
        " from tensorflow/models."	,  )
    def __init__(      self	:							Tuple	,  a	:							Union[str, Any]	,  a	:							Any	,  a	:							Optional[Any]=False	,  a	:							Dict=False	,  a	:							List[str]=dtypes.floataa	,  a	:							Optional[Any]=True	,  a	:							str=None	,  ):






       '''simple docstring'''






       lowercase_     ,		lowercase_						:     str							     =  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      )
       lowercase_						:     Optional[int]							     =  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:
          lowercase_						:     int							     =  1_0_0_0_0
          lowercase_						:     List[Any]							     =  one_hot
       else:
          assert (
              images.shape[0] == labels.shape[0]
          ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
          lowercase_						:     List[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
             lowercase_						:     int							     =  images.reshape(
                 images.shape[0]	,  images.shape[1] * images.shape[2]      )
          if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             lowercase_						:     Any							     =  images.astype(numpy.floataa      )
             lowercase_						:     List[str]							     =  numpy.multiply(a	,  1.0 / 255.0      )
       lowercase_						:     Optional[int]							     =  images
       lowercase_						:     Optional[int]							     =  labels
       lowercase_						:     Union[str, Any]							     =  0
       lowercase_						:     Union[str, Any]							     =  0




    @property
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       return self._images




    @property
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       return self._labels




    @property
    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       return self._num_examples




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self._epochs_completed





    def 					lowerCAmelCase__					(      self	:							Optional[Any]	,  a	:							Dict	,  a	:							Tuple=False	,  a	:							Union[str, Any]=True      ):






       '''simple docstring'''






       if fake_data:
          lowercase_						:     Union[str, Any]							     =  [1] * 7_8_4
          lowercase_						:     Union[str, Any]							     =  [1] + [0] * 9 if self.one_hot else 0
          return (
              [fake_image for _ in range(a      )],
              [fake_label for _ in range(a      )],
          )
       lowercase_						:     List[str]							     =  self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
          lowercase_						:     Optional[Any]							     =  numpy.arange(self._num_examples      )
          numpy.random.shuffle(a      )
          lowercase_						:     str							     =  self.images[perma]
          lowercase_						:     Dict							     =  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
          lowercase_						:     List[Any]							     =  self._num_examples - start
          lowercase_						:     Optional[Any]							     =  self._images[start : self._num_examples]
          lowercase_						:     int							     =  self._labels[start : self._num_examples]
          # Shuffle the data
          if shuffle:
             lowercase_						:     Any							     =  numpy.arange(self._num_examples      )
             numpy.random.shuffle(a      )
             lowercase_						:     Any							     =  self.images[perm]
             lowercase_						:     Optional[Any]							     =  self.labels[perm]
          # Start next epoch
          lowercase_						:     Optional[int]							     =  0
          lowercase_						:     Union[str, Any]							     =  batch_size - rest_num_examples
          lowercase_						:     str							     =  self._index_in_epoch
          lowercase_						:     str							     =  self._images[start:end]
          lowercase_						:     Union[str, Any]							     =  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
          lowercase_						:     List[Any]							     =  self._index_in_epoch
          return self._images[start:end], self._labels[start:end]




@deprecated(_UpperCamelCase    ,		"Please write your own downloading logic."				)
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




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





@deprecated(
    _UpperCamelCase    ,		"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"				)
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase=False    ,		_UpperCamelCase=False    ,		_UpperCamelCase=dtypes.floataa    ,		_UpperCamelCase=True    ,		_UpperCamelCase=5000    ,		_UpperCamelCase=None    ,		_UpperCamelCase=DEFAULT_SOURCE_URL    ,		):


   """simple docstring"""




   if fake_data:

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

      lowercase_						:     Tuple							     =  fake()
      lowercase_						:     Any							     =  fake()
      lowercase_						:     List[str]							     =  fake()
      return _Datasets(train=_UpperCamelCase    ,		validation=_UpperCamelCase    ,		test=_UpperCamelCase				)

   if not source_url:  # empty string check
      lowercase_						:     Any							     =  DEFAULT_SOURCE_URL

   lowercase_						:     Tuple							     =  "train-images-idx3-ubyte.gz"
   lowercase_						:     str							     =  "train-labels-idx1-ubyte.gz"
   lowercase_						:     List[str]							     =  "t10k-images-idx3-ubyte.gz"
   lowercase_						:     Union[str, Any]							     =  "t10k-labels-idx1-ubyte.gz"

   lowercase_						:     List[str]							     =  _maybe_download(
       _UpperCamelCase    ,		_UpperCamelCase    ,		source_url + train_images_file				)
   with gfile.Open(_UpperCamelCase    ,		"rb"				) as f:
      lowercase_						:     Tuple							     =  _extract_images(_UpperCamelCase				)

   lowercase_						:     Union[str, Any]							     =  _maybe_download(
       _UpperCamelCase    ,		_UpperCamelCase    ,		source_url + train_labels_file				)
   with gfile.Open(_UpperCamelCase    ,		"rb"				) as f:
      lowercase_						:     List[str]							     =  _extract_labels(_UpperCamelCase    ,		one_hot=_UpperCamelCase				)

   lowercase_						:     Any							     =  _maybe_download(
       _UpperCamelCase    ,		_UpperCamelCase    ,		source_url + test_images_file				)
   with gfile.Open(_UpperCamelCase    ,		"rb"				) as f:
      lowercase_						:     Tuple							     =  _extract_images(_UpperCamelCase				)

   lowercase_						:     Dict							     =  _maybe_download(
       _UpperCamelCase    ,		_UpperCamelCase    ,		source_url + test_labels_file				)
   with gfile.Open(_UpperCamelCase    ,		"rb"				) as f:
      lowercase_						:     Dict							     =  _extract_labels(_UpperCamelCase    ,		one_hot=_UpperCamelCase				)

   if not 0 <= validation_size <= len(_UpperCamelCase				):
      lowercase_						:     Dict							     =  (
          "Validation size should be between 0 and "
          F"""{len(_UpperCamelCase				)}. Received: {validation_size}."""
      )
      raise ValueError(_UpperCamelCase				)

   lowercase_						:     int							     =  train_images[:validation_size]
   lowercase_						:     Union[str, Any]							     =  train_labels[:validation_size]
   lowercase_						:     str							     =  train_images[validation_size:]
   lowercase_						:     Dict							     =  train_labels[validation_size:]

   lowercase_						:     Optional[Any]							     =  {"dtype": dtype, "reshape": reshape, "seed": seed}

   lowercase_						:     Any							     =  _DataSet(_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase				)
   lowercase_						:     Dict							     =  _DataSet(_UpperCamelCase    ,		_UpperCamelCase    ,		**_UpperCamelCase				)
   lowercase_						:     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


UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Union[str, Any]									=     'deta'
    __lowerCamelCase: str									=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }




    def __init__(      self	:							List[str]	,  a	:							Union[str, Any]=None	,  a	:							Dict=9_0_0	,  a	:							str=2_0_4_8	,  a	:							Union[str, Any]=6	,  a	:							int=2_0_4_8	,  a	:							Optional[int]=8	,  a	:							Optional[Any]=6	,  a	:							Optional[Any]=1_0_2_4	,  a	:							Any=8	,  a	:							Dict=0.0	,  a	:							Optional[int]=True	,  a	:							Union[str, Any]="relu"	,  a	:							str=2_5_6	,  a	:							str=0.1	,  a	:							Any=0.0	,  a	:							Optional[Any]=0.0	,  a	:							str=0.02	,  a	:							int=1.0	,  a	:							Tuple=True	,  a	:							Optional[int]=False	,  a	:							str="sine"	,  a	:							List[Any]=5	,  a	:							str=4	,  a	:							Any=4	,  a	:							Optional[Any]=True	,  a	:							Union[str, Any]=3_0_0	,  a	:							List[Any]=True	,  a	:							Tuple=True	,  a	:							Any=1	,  a	:							int=5	,  a	:							Optional[int]=2	,  a	:							Tuple=1	,  a	:							List[str]=1	,  a	:							Tuple=5	,  a	:							Tuple=2	,  a	:							List[str]=0.1	,  a	:							Tuple=0.25	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
          lowercase_						:     Optional[int]							     =  CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
       else:
          if isinstance(a	,  a      ):
             lowercase_						:     Any							     =  backbone_config.pop("model_type"      )
             lowercase_						:     str							     =  CONFIG_MAPPING[backbone_model_type]
             lowercase_						:     Any							     =  config_class.from_dict(a      )

       lowercase_						:     Optional[int]							     =  backbone_config
       lowercase_						:     Dict							     =  num_queries
       lowercase_						:     Tuple							     =  max_position_embeddings
       lowercase_						:     Union[str, Any]							     =  d_model
       lowercase_						:     Optional[Any]							     =  encoder_ffn_dim
       lowercase_						:     Any							     =  encoder_layers
       lowercase_						:     int							     =  encoder_attention_heads
       lowercase_						:     Tuple							     =  decoder_ffn_dim
       lowercase_						:     Dict							     =  decoder_layers
       lowercase_						:     Dict							     =  decoder_attention_heads
       lowercase_						:     List[Any]							     =  dropout
       lowercase_						:     Union[str, Any]							     =  attention_dropout
       lowercase_						:     List[str]							     =  activation_dropout
       lowercase_						:     Optional[Any]							     =  activation_function
       lowercase_						:     str							     =  init_std
       lowercase_						:     Dict							     =  init_xavier_std
       lowercase_						:     List[Any]							     =  encoder_layerdrop
       lowercase_						:     Optional[int]							     =  auxiliary_loss
       lowercase_						:     str							     =  position_embedding_type
       # deformable attributes
       lowercase_						:     Optional[int]							     =  num_feature_levels
       lowercase_						:     List[Any]							     =  encoder_n_points
       lowercase_						:     Optional[Any]							     =  decoder_n_points
       lowercase_						:     List[Any]							     =  two_stage
       lowercase_						:     Any							     =  two_stage_num_proposals
       lowercase_						:     List[str]							     =  with_box_refine
       lowercase_						:     Optional[int]							     =  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
       lowercase_						:     int							     =  class_cost
       lowercase_						:     Optional[int]							     =  bbox_cost
       lowercase_						:     str							     =  giou_cost
       # Loss coefficients
       lowercase_						:     Optional[int]							     =  mask_loss_coefficient
       lowercase_						:     Union[str, Any]							     =  dice_loss_coefficient
       lowercase_						:     Optional[Any]							     =  bbox_loss_coefficient
       lowercase_						:     Optional[Any]							     =  giou_loss_coefficient
       lowercase_						:     Any							     =  eos_coefficient
       lowercase_						:     str							     =  focal_alpha
       super().__init__(is_encoder_decoder=a	,  **a      )




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.encoder_attention_heads




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.d_model





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  copy.deepcopy(self.__dict__      )
       lowercase_						:     Union[str, Any]							     =  self.backbone_config.to_dict()
       lowercase_						:     str							     =  self.__class__.model_type
       return output




