text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Copyright 1999-2020 Alibaba Group Holding Ltd.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import itertools
  
  import numpy as np
  
  from ... import opcodes as OperandDef
  from ...serialization.serializables import KeyField, StringField
  from ...utils import has_unknown_shape
  from ..utils import unify_chunks, broadcast_shape
  from ..operands import TensorOperand, TensorOperandMixin
  from ..datasource import tensor as astensor
  from .broadcast_to import broadcast_to
  from ..array_utils import as_same_device, device
  
  
  class TensorCopyTo(TensorOperand, TensorOperandMixin):
      _op_type_ = OperandDef.COPYTO
  
      _src = KeyField('src')
      _dst = KeyField('dest')
      _casting = StringField('casting')
      _where = KeyField('where')
  
      def __init__(self, casting=None, **kw):
          super().__init__(_casting=casting, **kw)
  
      @property
      def src(self):
          return self._src
  
      @property
      def dst(self):
          return self._dst
  
      @property
      def casting(self):
          return self._casting
  
      @property
      def where(self):
          return self._where
  
      def check_inputs(self, inputs):
          if not 2 <= len(inputs) <= 3:
              raise ValueError("inputs' length must be 2 or 3")
  
      def _set_inputs(self, inputs):
          super()._set_inputs(inputs)
  
          self._src = self._inputs[0]
          self._dst = self._inputs[1]
          if len(self._inputs) > 2:
              self._where = self._inputs[2]
  
      @staticmethod
      def _extract_inputs(inputs):
          if len(inputs) == 2:
              (src, dst), where = inputs, None
          else:
              src, dst, where = inputs
              if where is True:
                  where = None
              else:
                  where = astensor(where)
  
          return src, dst, where
  
      def __call__(self, *inputs):
          from ..core import Tensor
  
          src, dst, where = self._extract_inputs(inputs)
  
          if not isinstance(dst, Tensor):
              raise TypeError('dst has to be a Tensor')
  
          self.dtype = dst.dtype
          self.gpu = dst.op.gpu
          self.sparse = dst.issparse()
  
          if not np.can_cast(src.dtype, dst.dtype, casting=self.casting):
              raise TypeError(f'Cannot cast array from {src.dtype!r} to {dst.dtype!r} '
                              f'according to the rule {self.casting!s}')
  
          try:
              broadcast_to(src, dst.shape)
          except ValueError:
              raise ValueError('could not broadcast input array '
                               f'from shape {src.shape!r} into shape {dst.shape!r}')
          if where:
              try:
                  broadcast_to(where, dst.shape)
              except ValueError:
                  raise ValueError('could not broadcast where mask '
                                   f'from shape {src.shape!r} into shape {dst.shape!r}')
  
          inps = [src, dst]
          if where is not None:
              inps.append(where)
          ret = self.new_tensor(inps, dst.shape, order=dst.order)
          dst.data = ret.data
  
      @classmethod
      def tile(cls, op):
          if has_unknown_shape(*op.inputs):
              yield
          inputs = yield from unify_chunks(
              *[(input, list(range(input.ndim))[::-1]) for input in op.inputs])
          output = op.outputs[0]
  
          chunk_shapes = [t.chunk_shape if hasattr(t, 'chunk_shape') else t
                          for t in inputs]
          out_chunk_shape = broadcast_shape(*chunk_shapes)
  
          out_chunks = []
          nsplits = [[np.nan] * shape for shape in out_chunk_shape]
          get_index = lambda idx, t: tuple(0 if t.nsplits[i] == (1,) else ix
                                           for i, ix in enumerate(idx))
          for out_idx in itertools.product(*(map(range, out_chunk_shape))):
              in_chunks = [t.cix[get_index(out_idx[-t.ndim:], t)] if t.ndim != 0 else t.chunks[0]
                           for t in inputs]
              out_chunk = op.copy().reset_key().new_chunk(
                  in_chunks, shape=in_chunks[1].shape, order=output.order, index=out_idx)
              out_chunks.append(out_chunk)
              for i, idx, s in zip(itertools.count(0), out_idx, out_chunk.shape):
                  nsplits[i][idx] = s
  
          new_op = op.copy()
          return new_op.new_tensors(op.inputs, output.shape, order=output.order,
                                    chunks=out_chunks, nsplits=nsplits)
  
      @classmethod
      def execute(cls, ctx, op):
          inputs, device_id, xp = as_same_device(
              [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True)
  
          with device(device_id):
              dst = inputs[1].copy()
              src = inputs[0]
              where = inputs[2] if len(inputs) > 2 else True
  
              xp.copyto(dst, src, casting=op.casting, where=where)
              ctx[op.outputs[0].key] = dst
  
  
  def copyto(dst, src, casting='same_kind', where=True):
      """
      Copies values from one array to another, broadcasting as necessary.
  
      Raises a TypeError if the `casting` rule is violated, and if
      `where` is provided, it selects which elements to copy.
  
      Parameters
      ----------
      dst : Tensor
          The tensor into which values are copied.
      src : array_like
          The tensor from which values are copied.
      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
          Controls what kind of data casting may occur when copying.
  
            * 'no' means the data types should not be cast at all.
            * 'equiv' means only byte-order changes are allowed.
            * 'safe' means only casts which can preserve values are allowed.
            * 'same_kind' means only safe casts or casts within a kind,
              like float64 to float32, are allowed.
            * 'unsafe' means any data conversions may be done.
      where : array_like of bool, optional
          A boolean tensor which is broadcasted to match the dimensions
          of `dst`, and selects elements to copy from `src` to `dst`
          wherever it contains the value True.
      """
      op = TensorCopyTo(casting=casting)
      return op(src, dst, where)
  