


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

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







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]				)	->		Any:

       """simple docstring"""

       a_					:		Tuple							=  numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
       return numpy.frombuffer(bytestream.read(4				)  ,    dtype=__A				)[0]







@deprecated(__A  ,    'Please use tf.data to implement this functionality.'				)
def        SCREAMING_SNAKE_CASE_						(			__A :			int				)	->		Any:

       """simple docstring"""

       print('Extracting'  ,    f.name				)
       with gzip.GzipFile(fileobj=__A				) as bytestream:
              a_					:		Optional[Any]							=  _readaa(__A				)
              if magic != 20_51:
                     raise ValueError(
                         'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
              a_					:		str							=  _readaa(__A				)
              a_					:		List[Any]							=  _readaa(__A				)
              a_					:		Union[str, Any]							=  _readaa(__A				)
              a_					:		List[str]							=  bytestream.read(rows * cols * num_images				)
              a_					:		Tuple							=  numpy.frombuffer(__A  ,    dtype=numpy.uinta				)
              a_					:		Union[str, Any]							=  data.reshape(__A  ,    __A  ,    __A  ,    1				)
              return data







@deprecated(__A  ,    'Please use tf.one_hot on tensors.'				)
def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			Optional[int]				)	->		Any:

       """simple docstring"""

       a_					:		List[Any]							=  labels_dense.shape[0]
       a_					:		Any							=  numpy.arange(__A				) * num_classes
       a_					:		Any							=  numpy.zeros((num_labels, num_classes)				)
       a_					:		List[Any]							=  1
       return labels_one_hot







@deprecated(__A  ,    'Please use tf.data to implement this functionality.'				)
def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Union[str, Any]=False  ,    __A :			List[str]=10				)	->		Optional[int]:

       """simple docstring"""

       print('Extracting'  ,    f.name				)
       with gzip.GzipFile(fileobj=__A				) as bytestream:
              a_					:		Union[str, Any]							=  _readaa(__A				)
              if magic != 20_49:
                     raise ValueError(
                         'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
              a_					:		Optional[Any]							=  _readaa(__A				)
              a_					:		Optional[int]							=  bytestream.read(__A				)
              a_					:		str							=  numpy.frombuffer(__A  ,    dtype=numpy.uinta				)
              if one_hot:
                     return _dense_to_one_hot(__A  ,    __A				)
              return labels
class  SCREAMING_SNAKE_CASE__  :
      @deprecated(
          SCREAMING_SNAKE_CASE__     ,							'Please use alternatives such as official/mnist/_DataSet.py'
          ' from tensorflow/models.'     ,							)
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Tuple=False     ,							SCREAMING_SNAKE_CASE__				:       List[str]=dtypes.floataa     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=None     ,							)		->			List[Any]:
             a_							,		a_					:		List[Any]							=  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_					:		List[Any]							=  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_					:		Any							=  1_0_0_0_0
                    a_					:		Union[str, Any]							=  one_hot
             else:
                    assert (
                        images.shape[0] == labels.shape[0]
                    ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
                    a_					:		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
                           a_					:		Dict							=  images.reshape(
                               images.shape[0]     ,							images.shape[1] * images.shape[2]     )
                    if dtype == dtypes.floataa:
                           # Convert from [0, 255] -> [0.0, 1.0].
                           a_					:		Tuple							=  images.astype(numpy.floataa     )
                           a_					:		List[Any]							=  numpy.multiply(SCREAMING_SNAKE_CASE__     ,							1.0 / 255.0     )
             a_					:		Dict							=  images
             a_					:		Union[str, Any]							=  labels
             a_					:		int							=  0
             a_					:		Optional[Any]							=  0
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[Any]:
             return self._images
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[str]:
             return self._labels
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Any:
             return self._num_examples
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             return self._epochs_completed

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Tuple=True     )		->			Tuple:
             if fake_data:
                    a_					:		str							=  [1] * 7_8_4
                    a_					:		str							=  [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_					:		Union[str, Any]							=  self._index_in_epoch
             # Shuffle for the first epoch
             if self._epochs_completed == 0 and start == 0 and shuffle:
                    a_					:		Tuple							=  numpy.arange(self._num_examples     )
                    numpy.random.shuffle(SCREAMING_SNAKE_CASE__     )
                    a_					:		str							=  self.images[perma]
                    a_					:		Tuple							=  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_					:		List[Any]							=  self._num_examples - start
                    a_					:		Optional[Any]							=  self._images[start : self._num_examples]
                    a_					:		List[str]							=  self._labels[start : self._num_examples]
                    # Shuffle the data
                    if shuffle:
                           a_					:		Union[str, Any]							=  numpy.arange(self._num_examples     )
                           numpy.random.shuffle(SCREAMING_SNAKE_CASE__     )
                           a_					:		List[str]							=  self.images[perm]
                           a_					:		Union[str, Any]							=  self.labels[perm]
                    # Start next epoch
                    a_					:		int							=  0
                    a_					:		Optional[int]							=  batch_size - rest_num_examples
                    a_					:		Dict							=  self._index_in_epoch
                    a_					:		Union[str, Any]							=  self._images[start:end]
                    a_					:		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
                    a_					:		List[str]							=  self._index_in_epoch
                    return self._images[start:end], self._labels[start:end]







@deprecated(__A  ,    'Please write your own downloading logic.'				)
def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			List[str]				)	->		Tuple:

       """simple docstring"""

       if not gfile.Exists(__A				):
              gfile.MakeDirs(__A				)
       a_					:		Tuple							=  os.path.join(__A  ,    __A				)
       if not gfile.Exists(__A				):
              urllib.request.urlretrieve(__A  ,    __A				)  # noqa: S310
              with gfile.GFile(__A				) as f:
                     a_					:		Any							=  f.size()
              print('Successfully downloaded'  ,    __A  ,    __A  ,    'bytes.'				)
       return filepath







@deprecated(
    __A  ,    'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Any=False  ,    __A :			Tuple=False  ,    __A :			List[str]=dtypes.floataa  ,    __A :			List[str]=True  ,    __A :			Optional[int]=50_00  ,    __A :			str=None  ,    __A :			Optional[Any]=DEFAULT_SOURCE_URL  ,    )	->		int:

       """simple docstring"""

       if fake_data:

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

              a_					:		Dict							=  fake()
              a_					:		Tuple							=  fake()
              a_					:		int							=  fake()
              return _Datasets(train=__A  ,    validation=__A  ,    test=__A				)

       if not source_url:  # empty string check
              a_					:		Tuple							=  DEFAULT_SOURCE_URL

       a_					:		str							=  'train-images-idx3-ubyte.gz'
       a_					:		Tuple							=  'train-labels-idx1-ubyte.gz'
       a_					:		int							=  't10k-images-idx3-ubyte.gz'
       a_					:		List[str]							=  't10k-labels-idx1-ubyte.gz'

       a_					:		Optional[int]							=  _maybe_download(
           __A  ,    __A  ,    source_url + train_images_file				)
       with gfile.Open(__A  ,    'rb'				) as f:
              a_					:		Union[str, Any]							=  _extract_images(__A				)

       a_					:		Dict							=  _maybe_download(
           __A  ,    __A  ,    source_url + train_labels_file				)
       with gfile.Open(__A  ,    'rb'				) as f:
              a_					:		Optional[int]							=  _extract_labels(__A  ,    one_hot=__A				)

       a_					:		Tuple							=  _maybe_download(
           __A  ,    __A  ,    source_url + test_images_file				)
       with gfile.Open(__A  ,    'rb'				) as f:
              a_					:		Tuple							=  _extract_images(__A				)

       a_					:		Optional[int]							=  _maybe_download(
           __A  ,    __A  ,    source_url + test_labels_file				)
       with gfile.Open(__A  ,    'rb'				) as f:
              a_					:		Union[str, Any]							=  _extract_labels(__A  ,    one_hot=__A				)

       if not 0 <= validation_size <= len(__A				):
              a_					:		List[Any]							=  (
                  'Validation size should be between 0 and '
                  F"""{len(__A				)}. Received: {validation_size}."""
              )
              raise ValueError(__A				)

       a_					:		int							=  train_images[:validation_size]
       a_					:		Tuple							=  train_labels[:validation_size]
       a_					:		List[Any]							=  train_images[validation_size:]
       a_					:		Optional[int]							=  train_labels[validation_size:]

       a_					:		Dict							=  {'dtype': dtype, 'reshape': reshape, 'seed': seed}

       a_					:		Optional[Any]							=  _DataSet(__A  ,    __A  ,    **__A				)
       a_					:		Any							=  _DataSet(__A  ,    __A  ,    **__A				)
       a_					:		Optional[Any]							=  _DataSet(__A  ,    __A  ,    **__A				)

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      : Dict  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Union[str, Any]  					=       {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[str]        =						'''switch_transformers'''
      snake_case__				:     Optional[int]        =						['''past_key_values''']
      snake_case__				:     Optional[Any]        =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_2_1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=6_4     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=8     ,							SCREAMING_SNAKE_CASE__				:       Dict=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.01     ,							SCREAMING_SNAKE_CASE__				:       str="float32"     ,							SCREAMING_SNAKE_CASE__				:       str=False     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=1_2_8     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Dict=1E-6     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.001     ,							SCREAMING_SNAKE_CASE__				:       Any=0.001     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1.0     ,							SCREAMING_SNAKE_CASE__				:       Any="relu"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Dict=True     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1     ,							**SCREAMING_SNAKE_CASE__				:       Dict     ,							)		->			Optional[Any]:
             a_					:		Optional[int]							=  vocab_size
             a_					:		List[str]							=  d_model
             a_					:		Tuple							=  d_kv
             a_					:		Optional[Any]							=  d_ff

             a_					:		List[Any]							=  num_sparse_encoder_layers

             a_					:		Any							=  num_layers
             a_					:		str							=  (
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             a_					:		List[Any]							=  num_sparse_decoder_layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_encoder_layers > 0:
                    a_					:		Optional[int]							=  self.num_layers // self.num_sparse_encoder_layers
             else:
                    a_					:		List[Any]							=  self.num_layers  # HACK: this will create 0 sparse layers

             # This tells us, each how many encoder layer we'll have to set a sparse layer.
             if self.num_sparse_decoder_layers > 0:
                    a_					:		Union[str, Any]							=  self.num_decoder_layers // self.num_sparse_decoder_layers
             else:
                    a_					:		List[str]							=  self.num_decoder_layers  # HACK: this will create 0 sparse layers

             a_					:		Dict							=  num_heads
             a_					:		str							=  num_experts
             a_					:		Any							=  expert_capacity
             a_					:		List[Any]							=  router_bias
             a_					:		str							=  router_jitter_noise
             if router_dtype not in ["float32", "float16", "bfloat16"]:
                    raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""     )
             a_					:		Optional[int]							=  router_dtype

             a_					:		int							=  router_ignore_padding_tokens
             a_					:		Any							=  relative_attention_num_buckets
             a_					:		List[str]							=  relative_attention_max_distance

             a_					:		Optional[Any]							=  dropout_rate
             a_					:		Tuple							=  layer_norm_epsilon
             a_					:		Dict							=  initializer_factor
             a_					:		Any							=  feed_forward_proj
             a_					:		Tuple							=  use_cache
             a_					:		str							=  add_router_probs

             a_					:		Optional[int]							=  router_z_loss_coef
             a_					:		List[str]							=  router_aux_loss_coef

             a_					:		int							=  self.feed_forward_proj.split('-'     )
             a_					:		int							=  act_info[-1]
             a_					:		Optional[int]							=  act_info[0] == 'gated'

             if len(SCREAMING_SNAKE_CASE__     ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__     ) > 2:
                    raise ValueError(
                        F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                        'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                        '\'gated-gelu\' or \'relu\''     )

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                    a_					:		Any							=  'gelu_new'

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							is_encoder_decoder=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)


