


'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowercase__				     =		logging.get_logger(__name__)






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]				)      ->     int:







      '''simple docstring'''






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

      _a						=		tf.shape(lowerCamelCase__				)

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

      _a						=		tensor.shape.as_list()

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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: int = None      ,  __lowerCamelCase		: Union[str, Any] = None				)      ->     List[str]:







      '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Optional[Any]=1E-5      ,  __lowerCamelCase		: List[Any]=-1				)      ->     Any:







      '''simple docstring'''






      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCamelCase__      ,  lowerCamelCase__				):
            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						=		tf.nn.moments(lowerCamelCase__      ,  axes=[axis]      ,  keepdims=lowerCamelCase__				)

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

      # Compute layer normalization using the batch_normalization
      # function.
      _a						=		tf.nn.batch_normalization(
          lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__      ,  offset=lowerCamelCase__      ,  scale=lowerCamelCase__      ,  variance_epsilon=lowerCamelCase__      ,  )
      return outputs






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Tuple=0      ,  __lowerCamelCase		: int=-1				)      ->     int:







      '''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						=		tf.shape(lowerCamelCase__				)
      _a						=		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
      _a						=		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,  axis=0				)
      return tf.reshape(lowerCamelCase__      ,  lowerCamelCase__				)






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]				)      ->     List[str]:







      '''simple docstring'''






      if not isinstance(lowerCamelCase__      ,  tf.Tensor				):
            _a						=		tf.convert_to_tensor(lowerCamelCase__				)  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            _a						=		encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            _a						=		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						=		(
          tf.cast(1      ,  encoder_attention_mask.dtype				) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask






def __UpperCamelCase   (			__lowerCamelCase		: Tuple      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: List[str] = "input_ids"				)      ->     List[str]:







      '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Union[str, Any]				)      ->     Tuple:







      '''simple docstring'''






      _a						=		6_4512
      # 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						=		[x for x in data if len(lowerCamelCase__				) > 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						=		np.asarray(lowerCamelCase__				)

      _a						=		1
      _a						=		np.array_split(lowerCamelCase__      ,  lowerCamelCase__				)

      # 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						=		np.array_split(lowerCamelCase__      ,  lowerCamelCase__				)

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(lowerCamelCase__				):
                  _a						=		chunk_data
      else:
            _a						=		data






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Optional[int]				)      ->     str:







      '''simple docstring'''






      if name in group.attrs:
            _a						=		[n.decode("utf8"				) if hasattr(lowerCamelCase__      ,  "decode"				) else n for n in group.attrs[name]]
      else:
            _a						=		[]
            _a						=		0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode("utf8"				) if hasattr(lowerCamelCase__      ,  "decode"				) else n for n in group.attrs["%s%d" % (name, chunk_id)]]				)
                  chunk_id += 1
      return data






def __UpperCamelCase   (			__lowerCamelCase		: List[str]				)      ->     Optional[int]:







      '''simple docstring'''






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

      return tf.nest.map_structure(_expand_single_ad_tensor      ,  lowerCamelCase__				)





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

