






"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase    :   List[str]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:Union[tf.Tensor, np.ndarray]				):







  '''simple docstring'''

  if isinstance(A_     ,      np.ndarray				):
    return list(tensor.shape				)

  a					:      int    =       tf.shape(A_				)

  if tensor.shape == tf.TensorShape(A_				):
    return dynamic

  a					:      Optional[Any]    =       tensor.shape.as_list()

  return [dynamic[i] if s is None else s for i, s in enumerate(A_				)]




def       snake_case  (A_				:tf.Tensor     ,      A_				:Optional[int] = None     ,      A_				:Optional[str] = None				):







  '''simple docstring'''

  return tf.nn.softmax(logits=logits + 1E-9     ,      axis=A_     ,      name=A_				)




def       snake_case  (A_				:Optional[Any]     ,      A_				:List[Any]     ,      A_				:int     ,      A_				:Dict=1E-5     ,      A_				:Union[str, Any]=-1				):







  '''simple docstring'''

  if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(A_     ,      A_				):
    raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'				)

  # Get mean and variance on the axis to be normalized
  a,					a					:      List[str]    =       tf.nn.moments(A_     ,      axes=[axis]     ,      keepdims=A_				)

  if axis != -1:
    # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
    # on every dimension except axis
    a					:      Tuple    =       [1] * inputs.shape.rank
    a					:      List[Any]    =       shape_list(A_				)[axis]
    a					:      Optional[Any]    =       tf.reshape(A_     ,      A_				)
    a					:      Dict    =       tf.reshape(A_     ,      A_				)

  # Compute layer normalization using the batch_normalization
  # function.
  a					:      Union[str, Any]    =       tf.nn.batch_normalization(
      A_     ,      A_     ,      A_     ,      offset=A_     ,      scale=A_     ,      variance_epsilon=A_     ,      )
  return outputs




def       snake_case  (A_				:Tuple     ,      A_				:List[str]=0     ,      A_				:str=-1				):







  '''simple docstring'''

  if end_dim < 0:
    end_dim += input.shape.rank
  if start_dim < 0:
    start_dim += input.shape.rank

  if start_dim == end_dim:
    return input

  a					:      str    =       tf.shape(A_				)
  a					:      List[str]    =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
  a					:      List[Any]    =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,      axis=0				)
  return tf.reshape(A_     ,      A_				)




def       snake_case  (A_				:tf.Tensor				):







  '''simple docstring'''

  if not isinstance(A_     ,      tf.Tensor				):
    a					:      List[Any]    =       tf.convert_to_tensor(A_				)  # Catches stray NumPy inputs
  if encoder_attention_mask.shape.rank == 3:
    a					:      List[str]    =       encoder_attention_mask[:, None, :, :]
  if encoder_attention_mask.shape.rank == 2:
    a					:      Optional[int]    =       encoder_attention_mask[:, None, None, :]
  # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
  # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
  # /transformer/transformer_layers.py#L270
  # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
  # encoder_extended_attention_mask.transpose(-1, -2))
  a					:      Union[str, Any]    =       (
      tf.cast(1     ,      encoder_attention_mask.dtype				) - encoder_extended_attention_mask
  ) * encoder_extended_attention_mask.dtype.min

  return encoder_extended_attention_mask




def       snake_case  (A_				:tf.Tensor     ,      A_				:int     ,      A_				:str = "input_ids"				):







  '''simple docstring'''

  tf.debugging.assert_less(
      A_     ,      tf.cast(A_     ,      dtype=tensor.dtype				)     ,      message=(
          f'''The maximum value of {tensor_name} ({tf.math.reduce_max(A_				)}) must be smaller than the embedding '''
          f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
      )     ,      )




def       snake_case  (A_				:Tuple     ,      A_				:Optional[Any]     ,      A_				:Union[str, Any]				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       6_4_5_1_2
  # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
  # because in that case even chunking the array would not make the saving
  # possible.
  a					:      Union[str, Any]    =       [x for x in data if len(A_				) > HDF5_OBJECT_HEADER_LIMIT]

  # Expecting this to never be true.
  if bad_attributes:
    raise RuntimeError(
        'The following attributes cannot be saved to HDF5 file because '
        f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
        f'''bytes: {bad_attributes}'''				)

  a					:      Optional[int]    =       np.asarray(A_				)

  a					:      Any    =       1
  a					:      Dict    =       np.array_split(A_     ,      A_				)

  # This will never loop forever thanks to the test above.
  while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
    num_chunks += 1
    a					:      str    =       np.array_split(A_     ,      A_				)

  if num_chunks > 1:
    for chunk_id, chunk_data in enumerate(A_				):
      a					:      Dict    =       chunk_data
  else:
    a					:      Optional[Any]    =       data




def       snake_case  (A_				:Optional[Any]     ,      A_				:Dict				):







  '''simple docstring'''

  if name in group.attrs:
    a					:      Union[str, Any]    =       [n.decode('utf8'				) if hasattr(A_     ,      'decode'				) else n for n in group.attrs[name]]
  else:
    a					:      Optional[Any]    =       []
    a					:      int    =       0
    while "%s%d" % (name, chunk_id) in group.attrs:
      data.extend(
          [n.decode('utf8'				) if hasattr(A_     ,      'decode'				) else n for n in group.attrs['%s%d' % (name, chunk_id)]]				)
      chunk_id += 1
  return data






def       snake_case  (A_				:List[str]				):







  '''simple docstring'''

  def _expand_single_ad_tensor(A_				:List[Any]				):
    if isinstance(A_     ,      tf.Tensor				) and t.shape.rank == 1:
      return tf.expand_dims(A_     ,      axis=-1				)
    return t

  return tf.nest.map_structure(_expand_single_ad_tensor     ,      A_				)












"""simple docstring"""


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    :   Union[str, Any]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





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


      '''simple docstring'''




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





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


      '''simple docstring'''




      return 1E-4




