


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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      print("""Extracting"""						,	f.name      )
      with gzip.GzipFile(fileobj=UpperCAmelCase      ) as bytestream:
            __lowerCamelCase     :      str       						=						_readaa(UpperCAmelCase      )
            if magic != 2_051:
                  raise ValueError(
                      """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)      )
            __lowerCamelCase     :      Dict       						=						_readaa(UpperCAmelCase      )
            __lowerCamelCase     :      str       						=						_readaa(UpperCAmelCase      )
            __lowerCamelCase     :      Union[str, Any]       						=						_readaa(UpperCAmelCase      )
            __lowerCamelCase     :      Tuple       						=						bytestream.read(rows * cols * num_images      )
            __lowerCamelCase     :      int       						=						numpy.frombuffer(UpperCAmelCase						,	dtype=numpy.uinta      )
            __lowerCamelCase     :      Optional[int]       						=						data.reshape(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	1      )
            return data





@deprecated(UpperCAmelCase						,	"""Please use tf.one_hot on tensors."""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						labels_dense.shape[0]
      __lowerCamelCase     :      Any       						=						numpy.arange(UpperCAmelCase      ) * num_classes
      __lowerCamelCase     :      int       						=						numpy.zeros((num_labels, num_classes)      )
      __lowerCamelCase     :      Any       						=						1
      return labels_one_hot





@deprecated(UpperCAmelCase						,	"""Please use tf.data to implement this functionality."""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]=False						,	UpperCAmelCase      :			Tuple=10      ):
      """simple docstring"""




      print("""Extracting"""						,	f.name      )
      with gzip.GzipFile(fileobj=UpperCAmelCase      ) as bytestream:
            __lowerCamelCase     :      Dict       						=						_readaa(UpperCAmelCase      )
            if magic != 2_049:
                  raise ValueError(
                      """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)      )
            __lowerCamelCase     :      Optional[int]       						=						_readaa(UpperCAmelCase      )
            __lowerCamelCase     :      List[Any]       						=						bytestream.read(UpperCAmelCase      )
            __lowerCamelCase     :      int       						=						numpy.frombuffer(UpperCAmelCase						,	dtype=numpy.uinta      )
            if one_hot:
                  return _dense_to_one_hot(UpperCAmelCase						,	UpperCAmelCase      )
            return labels



class 							_UpperCamelCase :

   '''simple docstring'''







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






         '''simple docstring'''



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

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






         '''simple docstring'''



         return self._images

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






         '''simple docstring'''



         return self._labels

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return self._num_examples

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return self._epochs_completed


   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : List[str]=True							):






         '''simple docstring'''



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





@deprecated(UpperCAmelCase						,	"""Please write your own downloading logic."""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      if not gfile.Exists(UpperCAmelCase      ):
            gfile.MakeDirs(UpperCAmelCase      )
      __lowerCamelCase     :      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     :      Any       						=						f.size()
            print("""Successfully downloaded"""						,	UpperCAmelCase						,	UpperCAmelCase						,	"""bytes."""      )
      return filepath





@deprecated(
    UpperCAmelCase						,	"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Optional[Any]=False						,	UpperCAmelCase      :			str=False						,	UpperCAmelCase      :			Optional[Any]=dtypes.floataa						,	UpperCAmelCase      :			List[str]=True						,	UpperCAmelCase      :			Optional[Any]=5_000						,	UpperCAmelCase      :			List[Any]=None						,	UpperCAmelCase      :			Dict=DEFAULT_SOURCE_URL						,	):
      """simple docstring"""




      if fake_data:

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

            __lowerCamelCase     :      List[Any]       						=						fake()
            __lowerCamelCase     :      Tuple       						=						fake()
            __lowerCamelCase     :      Union[str, Any]       						=						fake()
            return _Datasets(train=UpperCAmelCase						,	validation=UpperCAmelCase						,	test=UpperCAmelCase      )

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

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

      __lowerCamelCase     :      Union[str, Any]       						=						_maybe_download(
          UpperCAmelCase						,	UpperCAmelCase						,	source_url + train_images_file      )
      with gfile.Open(UpperCAmelCase						,	"""rb"""      ) as f:
            __lowerCamelCase     :      Tuple       						=						_extract_images(UpperCAmelCase      )

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

      __lowerCamelCase     :      List[Any]       						=						_maybe_download(
          UpperCAmelCase						,	UpperCAmelCase						,	source_url + test_images_file      )
      with gfile.Open(UpperCAmelCase						,	"""rb"""      ) as f:
            __lowerCamelCase     :      Dict       						=						_extract_images(UpperCAmelCase      )

      __lowerCamelCase     :      Optional[int]       						=						_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     :      Dict       						=						(
                """Validation size should be between 0 and """
                f"""{len(UpperCAmelCase      )}. Received: {validation_size}."""
            )
            raise ValueError(UpperCAmelCase      )

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

      __lowerCamelCase     :      int       						=						{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

      __lowerCamelCase     :      List[str]       						=						_DataSet(UpperCAmelCase						,	UpperCAmelCase						,	**UpperCAmelCase      )
      __lowerCamelCase     :      int       						=						_DataSet(UpperCAmelCase						,	UpperCAmelCase						,	**UpperCAmelCase      )
      __lowerCamelCase     :      str       						=						_DataSet(UpperCAmelCase						,	UpperCAmelCase						,	**UpperCAmelCase      )

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







import copy
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 ..auto import CONFIG_MAPPING


__UpperCamelCase    :						Optional[int]     =							logging.get_logger(__name__)

__UpperCamelCase    :						Any     =							{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[int]    = "conditional_detr"
   a_				:							Union[str, Any]    = ["past_key_values"]
   a_				:							str    = {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }

   def __init__(  self  : Dict       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : int=None       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Tuple=3_0_0       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Union[str, Any]=2_0_4_8       ,  _lowerCamelCase  : List[str]=8       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Any=2_0_4_8       ,  _lowerCamelCase  : List[Any]=8       ,  _lowerCamelCase  : Optional[int]=0.0       ,  _lowerCamelCase  : Any=0.0       ,  _lowerCamelCase  : Tuple=True       ,  _lowerCamelCase  : Union[str, Any]="relu"       ,  _lowerCamelCase  : str=2_5_6       ,  _lowerCamelCase  : int=0.1       ,  _lowerCamelCase  : List[str]=0.0       ,  _lowerCamelCase  : Tuple=0.0       ,  _lowerCamelCase  : List[str]=0.02       ,  _lowerCamelCase  : Union[str, Any]=1.0       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : List[str]="sine"       ,  _lowerCamelCase  : Optional[Any]="resnet50"       ,  _lowerCamelCase  : List[str]=True       ,  _lowerCamelCase  : Dict=False       ,  _lowerCamelCase  : Optional[int]=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : List[Any]=2       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Union[str, Any]=0.25       ,  **_lowerCamelCase  : Tuple       ,  ):






         '''simple docstring'''



         if backbone_config is not None and use_timm_backbone:
               raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""							)

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
                     __lowerCamelCase     :      Any       						=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     __lowerCamelCase     :      str       						=						backbone_config.get("""model_type"""							)
                     __lowerCamelCase     :      Optional[Any]       						=						CONFIG_MAPPING[backbone_model_type]
                     __lowerCamelCase     :      Optional[Any]       						=						config_class.from_dict(_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						use_timm_backbone
         __lowerCamelCase     :      Union[str, Any]       						=						backbone_config
         __lowerCamelCase     :      List[Any]       						=						num_channels
         __lowerCamelCase     :      str       						=						num_queries
         __lowerCamelCase     :      Dict       						=						d_model
         __lowerCamelCase     :      List[Any]       						=						encoder_ffn_dim
         __lowerCamelCase     :      Optional[Any]       						=						encoder_layers
         __lowerCamelCase     :      Any       						=						encoder_attention_heads
         __lowerCamelCase     :      Dict       						=						decoder_ffn_dim
         __lowerCamelCase     :      List[str]       						=						decoder_layers
         __lowerCamelCase     :      Tuple       						=						decoder_attention_heads
         __lowerCamelCase     :      List[Any]       						=						dropout
         __lowerCamelCase     :      Any       						=						attention_dropout
         __lowerCamelCase     :      Optional[Any]       						=						activation_dropout
         __lowerCamelCase     :      List[str]       						=						activation_function
         __lowerCamelCase     :      Optional[int]       						=						init_std
         __lowerCamelCase     :      Union[str, Any]       						=						init_xavier_std
         __lowerCamelCase     :      Union[str, Any]       						=						encoder_layerdrop
         __lowerCamelCase     :      int       						=						decoder_layerdrop
         __lowerCamelCase     :      Dict       						=						encoder_layers
         __lowerCamelCase     :      Tuple       						=						auxiliary_loss
         __lowerCamelCase     :      Any       						=						position_embedding_type
         __lowerCamelCase     :      Tuple       						=						backbone
         __lowerCamelCase     :      int       						=						use_pretrained_backbone
         __lowerCamelCase     :      Tuple       						=						dilation
         # Hungarian matcher
         __lowerCamelCase     :      Dict       						=						class_cost
         __lowerCamelCase     :      Optional[Any]       						=						bbox_cost
         __lowerCamelCase     :      Any       						=						giou_cost
         # Loss coefficients
         __lowerCamelCase     :      Union[str, Any]       						=						mask_loss_coefficient
         __lowerCamelCase     :      List[Any]       						=						dice_loss_coefficient
         __lowerCamelCase     :      Any       						=						cls_loss_coefficient
         __lowerCamelCase     :      str       						=						bbox_loss_coefficient
         __lowerCamelCase     :      Dict       						=						giou_loss_coefficient
         __lowerCamelCase     :      List[str]       						=						focal_alpha
         super().__init__(is_encoder_decoder=_lowerCamelCase       ,  **_lowerCamelCase							)

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






         '''simple docstring'''



         return self.encoder_attention_heads

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






         '''simple docstring'''



         return self.d_model


   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						copy.deepcopy(self.__dict__							)
         if self.backbone_config is not None:
               __lowerCamelCase     :      Dict       						=						self.backbone_config.to_dict()
         __lowerCamelCase     :      int       						=						self.__class__.model_type
         return output



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Dict    = version.parse("1.11"						)

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ("""pixel_mask""", {0: """batch"""}),
             ]							)

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






         '''simple docstring'''



         return 1E-5


   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return 1_2



