code: # Copyright The PyTorch Lightning team.
  #
  # 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 base64
  from io import BytesIO
  from pathlib import Path
  from typing import Any, Dict, List
  
  import numpy as np
  import torch
  
  import flash
  from flash.core.data.io.input import DataKeys, Input, ServeInput
  from flash.core.data.utilities.paths import filter_valid_files, has_file_allowed_extension, PATH_TYPE
  from flash.core.data.utilities.samples import to_samples
  from flash.core.data.utils import image_default_loader
  from flash.core.utilities.imports import _TORCHVISION_AVAILABLE, Image, requires
  
  if _TORCHVISION_AVAILABLE:
      from torchvision.datasets.folder import IMG_EXTENSIONS
      from torchvision.transforms.functional import to_pil_image
  else:
      IMG_EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")
  
  NP_EXTENSIONS = (".npy",)
  
  
  def image_loader(filepath: str):
      if has_file_allowed_extension(filepath, IMG_EXTENSIONS):
          img = image_default_loader(filepath)
      elif has_file_allowed_extension(filepath, NP_EXTENSIONS):
          img = Image.fromarray(np.load(filepath).astype("uint8"), "RGB")
      else:
          raise ValueError(
              f"File: {filepath} has an unsupported extension. Supported extensions: "
              f"{list(IMG_EXTENSIONS + NP_EXTENSIONS)}."
          )
      return img
  
  
  class ImageDeserializer(ServeInput):
      @requires("image")
      def serve_load_sample(self, data: str) -> Dict:
          encoded_with_padding = (data + "===").encode("ascii")
          img = base64.b64decode(encoded_with_padding)
          buffer = BytesIO(img)
          img = Image.open(buffer, mode="r")
          return {
              DataKeys.INPUT: img,
          }
  
      @property
      def example_input(self) -> str:
          with (Path(flash.ASSETS_ROOT) / "fish.jpg").open("rb") as f:
              return base64.b64encode(f.read()).decode("UTF-8")
  
  
  class ImageInput(Input):
      @requires("image")
      def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
          w, h = sample[DataKeys.INPUT].size  # W x H
          if DataKeys.METADATA not in sample:
              sample[DataKeys.METADATA] = {}
          sample[DataKeys.METADATA]["size"] = (h, w)
          return sample
  
  
  class ImageFilesInput(ImageInput):
      def load_data(self, files: List[PATH_TYPE]) -> List[Dict[str, Any]]:
          files = filter_valid_files(files, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS)
          return to_samples(files)
  
      def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
          filepath = sample[DataKeys.INPUT]
          sample[DataKeys.INPUT] = image_loader(filepath)
          sample = super().load_sample(sample)
          sample[DataKeys.METADATA]["filepath"] = filepath
          return sample
  
  
  class ImageTensorInput(ImageInput):
      def load_data(self, tensor: Any) -> List[Dict[str, Any]]:
          return to_samples(tensor)
  
      def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
          img = to_pil_image(sample[DataKeys.INPUT])
          sample[DataKeys.INPUT] = img
          return super().load_sample(sample)
  
  
  class ImageNumpyInput(ImageInput):
      def load_data(self, array: Any) -> List[Dict[str, Any]]:
          return to_samples(array)
  
      def load_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
          img = to_pil_image(torch.from_numpy(sample[DataKeys.INPUT]))
          sample[DataKeys.INPUT] = img
          return super().load_sample(sample)
  
apis: numpy.load torch.from_numpy