
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


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





def    _lowerCAmelCase    (     lowerCamelCase__							:							np.ndarray,						lowerCamelCase__							:							Union[int, Iterable[int]],						lowerCamelCase__							:							bool,						lowerCamelCase__							:							int				)  ->    Tuple[int, int]:
  def constraint_to_multiple_of(lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Tuple=0,						lowerCamelCase__							:							str=None				):
    _SCREAMING_SNAKE_CASE   :				Tuple							    = round(val / multiple				) * multiple

    if max_val is not None and x > max_val:
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = math.floor(val / multiple				) * multiple

    if x < min_val:
      _SCREAMING_SNAKE_CASE   :				str							    = math.ceil(val / multiple				) * multiple

    return x

  _SCREAMING_SNAKE_CASE   :				List[str]							    = (output_size, output_size) if isinstance(lowerCamelCase__,						lowerCamelCase__				) else output_size

  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_image_size(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = output_size

  # determine new height and width
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = output_height / input_height
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = output_width / input_width

  if keep_aspect_ratio:
    # scale as little as possible
    if abs(1 - scale_width				) < abs(1 - scale_height				):
      # fit width
      _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = scale_width
    else:
      # fit height
      _SCREAMING_SNAKE_CASE   :				Tuple							    = scale_height

  _SCREAMING_SNAKE_CASE   :				Dict							    = constraint_to_multiple_of(scale_height * input_height,						multiple=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = constraint_to_multiple_of(scale_width * input_width,						multiple=lowerCamelCase__				)

  return (new_height, new_width)





class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""pixel_values"""]
     def __init__(     self  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = PILImageResampling.BILINEAR  ,     snake_case__ = False  ,     snake_case__ = 1  ,     snake_case__ = True  ,     snake_case__ = 1 / 255  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(**snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = size if size is not None else {"height": 384, "width": 384}
       _SCREAMING_SNAKE_CASE   :				Any							    = get_size_dict(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = do_resize
       _SCREAMING_SNAKE_CASE   :				Any							    = size
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = keep_aspect_ratio
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ensure_multiple_of
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = resample
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = do_rescale
       _SCREAMING_SNAKE_CASE   :				Any							    = rescale_factor
       _SCREAMING_SNAKE_CASE   :				int							    = do_normalize
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _SCREAMING_SNAKE_CASE   :				str							    = image_std if image_std is not None else IMAGENET_STANDARD_STD
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = 1  ,     snake_case__ = PILImageResampling.BICUBIC  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = get_size_dict(snake_case__				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_resize_output_image_size(
           snake_case__  ,     output_size=(size["height"], size["width"])  ,     keep_aspect_ratio=snake_case__  ,     multiple=snake_case__  ,     )
       return resize(snake_case__  ,     size=snake_case__  ,     resample=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return rescale(snake_case__  ,     scale=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       return normalize(snake_case__  ,     mean=snake_case__  ,     std=snake_case__  ,     data_format=snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = ChannelDimension.FIRST  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = do_resize if do_resize is not None else self.do_resize
       _SCREAMING_SNAKE_CASE   :				Tuple							    = size if size is not None else self.size
       _SCREAMING_SNAKE_CASE   :				List[str]							    = get_size_dict(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       _SCREAMING_SNAKE_CASE   :				Dict							    = resample if resample is not None else self.resample
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_rescale if do_rescale is not None else self.do_rescale
       _SCREAMING_SNAKE_CASE   :				Any							    = rescale_factor if rescale_factor is not None else self.rescale_factor
       _SCREAMING_SNAKE_CASE   :				Tuple							    = do_normalize if do_normalize is not None else self.do_normalize
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = image_mean if image_mean is not None else self.image_mean
       _SCREAMING_SNAKE_CASE   :				List[str]							    = image_std if image_std is not None else self.image_std

       _SCREAMING_SNAKE_CASE   :				Any							    = make_list_of_images(snake_case__				)

       if not valid_images(snake_case__				):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."				)

       if do_resize and size is None or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."				)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."				)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."				)

       # All transformations expect numpy arrays.
       _SCREAMING_SNAKE_CASE   :				List[str]							    = [to_numpy_array(snake_case__				) for image in images]

       if do_resize:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = [self.resize(image=snake_case__  ,     size=snake_case__  ,     resample=snake_case__				) for image in images]

       if do_rescale:
         _SCREAMING_SNAKE_CASE   :				Dict							    = [self.rescale(image=snake_case__  ,     scale=snake_case__				) for image in images]

       if do_normalize:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = [self.normalize(image=snake_case__  ,     mean=snake_case__  ,     std=snake_case__				) for image in images]

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [to_channel_dimension_format(snake_case__  ,     snake_case__				) for image in images]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {"pixel_values": images}
       return BatchFeature(data=snake_case__  ,     tensor_type=snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
         if len(snake_case__				) != len(snake_case__				):
           raise ValueError(
               "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

         if is_torch_tensor(snake_case__				):
           _SCREAMING_SNAKE_CASE   :				str							    = target_sizes.numpy()

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = []

         for idx in range(len(snake_case__				)				):
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = torch.nn.functional.interpolate(
               logits[idx].unsqueeze(dim=0				)  ,     size=target_sizes[idx]  ,     mode="bilinear"  ,     align_corners=snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = resized_logits[0].argmax(dim=0				)
           semantic_segmentation.append(snake_case__				)
       else:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = logits.argmax(dim=1				)
         _SCREAMING_SNAKE_CASE   :				int							    = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

       return semantic_segmentation





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files",						[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tmp_path_factory.mktemp("dset_infos_dir"				)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md",						"w"				) as f:
      f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json",						"w"				) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@pytest.mark.parametrize(
    "dataset_info",						[
        DatasetInfo(),
        DatasetInfo(
            description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfo(
      description="foo",						citation="bar",						homepage="https://foo.bar",						license="CC0",						features=Features({"a": Value("int32"				)}				),						post_processed={},						supervised_keys=(),						task_templates=[],						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train", "num_examples": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key],						(list, dict, int, str)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict",						[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo",						features=Features({"a": Value("int32"				)}				),						builder_name="builder",						config_name="config",						version="1.0.0",						splits=[{"name": "train"}],						download_size=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(lowerCamelCase__,						"README.md"				)				)



