


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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "mobilenet_v1"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=224				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE="relu6"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0.9_9_9				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.0_0_1				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[int]:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''		)

         A_      :				List[Any]		     =					num_channels
         A_      :				Optional[Any]		     =					image_size
         A_      :				Optional[int]		     =					depth_multiplier
         A_      :				Union[str, Any]		     =					min_depth
         A_      :				Union[str, Any]		     =					hidden_act
         A_      :				List[Any]		     =					tf_padding
         A_      :				List[str]		     =					classifier_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					layer_norm_eps






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    version.parse("1.11"			)



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         return OrderedDict([('''pixel_values''', {0: '''batch'''})]		)



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
         else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)







       @property
       def        _snake_case       (      self		)->float:




         '''simple docstring'''





         return 1e-4








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	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					numpy.dtype(numpy.uintaa       ).newbyteorder('''>'''       )
  return numpy.frombuffer(bytestream.read(4       ) ,			dtype=SCREAMING_SNAKE_CASE       )[0]


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.data to implement this functionality.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  print('''Extracting''' ,			f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE       ) as bytestream:
    A_      :				Union[str, Any]		     =					_readaa(SCREAMING_SNAKE_CASE       )
    if magic != 2_051:
      raise ValueError(
          '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)       )
    A_      :				str		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Dict		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					bytestream.read(rows * cols * num_images       )
    A_      :				Dict		     =					numpy.frombuffer(SCREAMING_SNAKE_CASE ,			dtype=numpy.uinta       )
    A_      :				Union[str, Any]		     =					data.reshape(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			1       )
    return data


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.one_hot on tensors.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[Any]		     =					labels_dense.shape[0]
  A_      :				List[str]		     =					numpy.arange(SCREAMING_SNAKE_CASE       ) * num_classes
  A_      :				int		     =					numpy.zeros((num_labels, num_classes)       )
  A_      :				Tuple		     =					1
  return labels_one_hot


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.data to implement this functionality.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=10       ):
  print('''Extracting''' ,			f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE       ) as bytestream:
    A_      :				Any		     =					_readaa(SCREAMING_SNAKE_CASE       )
    if magic != 2_049:
      raise ValueError(
          '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)       )
    A_      :				Tuple		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					bytestream.read(SCREAMING_SNAKE_CASE       )
    A_      :				int		     =					numpy.frombuffer(SCREAMING_SNAKE_CASE ,			dtype=numpy.uinta       )
    if one_hot:
      return _dense_to_one_hot(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    return labels






class   _lowerCamelCase :




       """simple docstring"""



       @deprecated(
           _SCREAMING_SNAKE_CASE				,       '''Please use alternatives such as official/mnist/_DataSet.py'''
           ''' from tensorflow/models.'''				,       )
       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=dtypes.floataa				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=None				,       )->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				List[str]		     =					random_seed.get_seed(_SCREAMING_SNAKE_CASE		)
         # If op level seed is not set, use whatever graph level seed is returned
         numpy.random.seed(seeda if seed is None else seeda		)
         A_      :				Tuple		     =					dtypes.as_dtype(_SCREAMING_SNAKE_CASE		).base_dtype
         if dtype not in (dtypes.uinta, dtypes.floataa):
           raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype		)
         if fake_data:
           A_      :				Optional[Any]		     =					1_0000
           A_      :				List[str]		     =					one_hot
         else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
           A_      :				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
             A_      :				int		     =					images.reshape(
                 images.shape[0]				,       images.shape[1] * images.shape[2]		)
           if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             A_      :				Optional[int]		     =					images.astype(numpy.floataa		)
             A_      :				List[str]		     =					numpy.multiply(_SCREAMING_SNAKE_CASE				,       1.0 / 2_5_5.0		)
         A_      :				int		     =					images
         A_      :				Optional[int]		     =					labels
         A_      :				List[str]		     =					0
         A_      :				List[Any]		     =					0



       @property
       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         return self._images



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




         '''simple docstring'''





         return self._labels



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




         '''simple docstring'''





         return self._num_examples



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




         '''simple docstring'''





         return self._epochs_completed







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True		)->str:




         '''simple docstring'''





         if fake_data:
           A_      :				Any		     =					[1] * 784
           A_      :				int		     =					[1] + [0] * 9 if self.one_hot else 0
           return (
               [fake_image for _ in range(_SCREAMING_SNAKE_CASE		)],
               [fake_label for _ in range(_SCREAMING_SNAKE_CASE		)],
           )
         A_      :				Any		     =					self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
           A_      :				Any		     =					numpy.arange(self._num_examples		)
           numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
           A_      :				Optional[Any]		     =					self.images[perma]
           A_      :				List[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
           A_      :				Tuple		     =					self._num_examples - start
           A_      :				Union[str, Any]		     =					self._images[start : self._num_examples]
           A_      :				List[Any]		     =					self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
             A_      :				List[Any]		     =					numpy.arange(self._num_examples		)
             numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
             A_      :				Tuple		     =					self.images[perm]
             A_      :				int		     =					self.labels[perm]
           # Start next epoch
           A_      :				Tuple		     =					0
           A_      :				Optional[Any]		     =					batch_size - rest_num_examples
           A_      :				Tuple		     =					self._index_in_epoch
           A_      :				Union[str, Any]		     =					self._images[start:end]
           A_      :				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
           A_      :				List[str]		     =					self._index_in_epoch
           return self._images[start:end], self._labels[start:end]




@deprecated(SCREAMING_SNAKE_CASE ,			'''Please write your own downloading logic.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if not gfile.Exists(SCREAMING_SNAKE_CASE       ):
    gfile.MakeDirs(SCREAMING_SNAKE_CASE       )
  A_      :				str		     =					os.path.join(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  if not gfile.Exists(SCREAMING_SNAKE_CASE       ):
    urllib.request.urlretrieve(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )  # noqa: S310
    with gfile.GFile(SCREAMING_SNAKE_CASE       ) as f:
      A_      :				Dict		     =					f.size()
    print('''Successfully downloaded''' ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			'''bytes.'''       )
  return filepath







@deprecated(
    SCREAMING_SNAKE_CASE ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=dtypes.floataa ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=5_000 ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL ,			):
  if fake_data:

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

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

  if not source_url:  # empty string check
    A_      :				List[str]		     =					DEFAULT_SOURCE_URL

  A_      :				List[Any]		     =					'''train-images-idx3-ubyte.gz'''
  A_      :				Tuple		     =					'''train-labels-idx1-ubyte.gz'''
  A_      :				Optional[int]		     =					'''t10k-images-idx3-ubyte.gz'''
  A_      :				Any		     =					'''t10k-labels-idx1-ubyte.gz'''

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Optional[int]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Tuple		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				List[str]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Any		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

  if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE       ):
    A_      :				str		     =					(
        '''Validation size should be between 0 and '''
        f'''{len(SCREAMING_SNAKE_CASE       )}. Received: {validation_size}.'''
    )
    raise ValueError(SCREAMING_SNAKE_CASE       )

  A_      :				Optional[Any]		     =					train_images[:validation_size]
  A_      :				Optional[Any]		     =					train_labels[:validation_size]
  A_      :				Any		     =					train_images[validation_size:]
  A_      :				Any		     =					train_labels[validation_size:]

  A_      :				Optional[Any]		     =					{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

  A_      :				List[str]		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )

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




