# Copyright 2024 The Flax Authors.
#
# 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.

# Copyright 2023 The Flax Authors.
#
# 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.
"""
This code is created with reference to torchvision/utils.py.

Modify: torch.tensor -> jax.numpy.DeviceArray
If you want to know about this file in detail, please visit the original code:
    https://github.com/pytorch/vision/blob/master/torchvision/utils.py
"""
import math

from flax import serialization
import jax.numpy as jnp
import numpy as np
from PIL import Image


def save_image(ndarray, fp, nrow=8, padding=2, pad_value=0.0, format_img=None):
  """Make a grid of images and Save it into an image file.

  Args:
    ndarray (array_like): 4D mini-batch images of shape (B x H x W x C)
    fp:  A filename(string) or file object
    nrow (int, optional): Number of images displayed in each row of the grid.
      The final grid size is ``(B / nrow, nrow)``. Default: ``8``.
    padding (int, optional): amount of padding. Default: ``2``.
    pad_value (float, optional): Value for the padded pixels. Default: ``0``.
    format_img(Optional):  If omitted, the format to use is determined from the
      filename extension. If a file object was used instead of a filename,
      this parameter should always be used.
  """

  if not (
      isinstance(ndarray, jnp.ndarray)
      or (
          isinstance(ndarray, list)
          and all(isinstance(t, jnp.ndarray) for t in ndarray)
      )
  ):
    raise TypeError(f'array_like of tensors expected, got {type(ndarray)}')

  ndarray = jnp.asarray(ndarray)

  if ndarray.ndim == 4 and ndarray.shape[-1] == 1:  # single-channel images
    ndarray = jnp.concatenate((ndarray, ndarray, ndarray), -1)

  # make the mini-batch of images into a grid
  nmaps = ndarray.shape[0]
  xmaps = min(nrow, nmaps)
  ymaps = int(math.ceil(float(nmaps) / xmaps))
  height, width = (
      int(ndarray.shape[1] + padding),
      int(ndarray.shape[2] + padding),
  )
  num_channels = ndarray.shape[3]
  grid = jnp.full(
      (height * ymaps + padding, width * xmaps + padding, num_channels),
      pad_value,
  ).astype(jnp.float32)
  k = 0
  for y in range(ymaps):
    for x in range(xmaps):
      if k >= nmaps:
        break
      grid = grid.at[
          y * height + padding : (y + 1) * height,
          x * width + padding : (x + 1) * width,
      ].set(ndarray[k])
      k = k + 1

  # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
  ndarr = np.array(jnp.clip(grid * 255.0 + 0.5, 0, 255).astype(jnp.uint8))
  im = Image.fromarray(ndarr.copy())
  im.save(fp, format=format_img)
  return im

def save_model(params,filename):
  with open(filename, 'wb') as f:
    f.write(serialization.to_bytes(params))

def load_model(filename,target=None):
  with open(filename, 'rb') as fp:
    return serialization.from_bytes(target, fp.read())