"""simple docstring"""





import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CONFIG_NAME


      @register_to_config
      def __init__(       self		,      a = True		,      a = 8		,      a = "lanczos"		,      a = True		,      a = False		,      )	-> Optional[Any]:
             super().__init__()


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> PIL.Image.Image:
             if images.ndim == 3:
                    lowercase__      :		Union[str, Any]          =     images[None, ...]
             lowercase__      :		Union[str, Any]          =     (images * 2_5_5).round().astype('uint8'							)
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    lowercase__      :		int          =     [Image.fromarray(image.squeeze()		,      mode='L'							) for image in images]
             else:
                    lowercase__      :		str          =     [Image.fromarray(a							) for image in images]

             return pil_images


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> np.ndarray:
             if not isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     [images]
             lowercase__      :		Optional[int]          =     [np.array(a							).astype(np.floataa							) / 255.0 for image in images]
             lowercase__      :		List[Any]          =     np.stack(a		,      axis=0							)

             return images


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> torch.FloatTensor:
             if images.ndim == 3:
                    lowercase__      :		Optional[Any]          =     images[..., None]

             lowercase__      :		List[Any]          =     torch.from_numpy(images.transpose(0		,      3		,      1		,      2							)							)
             return images


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> np.ndarray:
             lowercase__      :		Optional[int]          =     images.cpu().permute(0		,      2		,      3		,      1							).float().numpy()
             return images


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> int:
             return 2.0 * images - 1.0


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> str:
             return (images / 2 + 0.5).clamp(0		,      1							)


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> PIL.Image.Image:
             lowercase__      :		List[Any]          =     image.convert('RGB'							)
             return image


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = None		,      )	-> PIL.Image.Image:
             if height is None:
                    lowercase__      :		Optional[int]          =     image.height
             if width is None:
                    lowercase__      :		Any          =     image.width

             lowercase__   ,				lowercase__      :		Union[str, Any]          =     (
                 x - x % self.config.vae_scale_factor for x in (width, height)
             )  # resize to integer multiple of vae_scale_factor
             lowercase__      :		Tuple          =     image.resize((width, height)		,      resample=PIL_INTERPOLATION[self.config.resample]							)
             return image


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = None		,      )	-> torch.Tensor:
             lowercase__      :		List[Any]          =     (PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(a		,      a							):
                    lowercase__      :		Optional[int]          =     [image]
             elif not (isinstance(a		,      a							) and all(isinstance(a		,      a							) for i in image							)):
                    raise ValueError(
                        f"""Input is in incorrect format: {[type(a							) for i in image]}. Currently, we only support {", ".join(a							)}"""							)

             if isinstance(image[0]		,      PIL.Image.Image							):
                    if self.config.do_convert_rgb:
                           lowercase__      :		List[Any]          =     [self.convert_to_rgb(a							) for i in image]
                    if self.config.do_resize:
                           lowercase__      :		List[str]          =     [self.resize(a		,      a		,      a							) for i in image]
                    lowercase__      :		Optional[Any]          =     self.pil_to_numpy(a							)  # to np
                    lowercase__      :		Optional[Any]          =     self.numpy_to_pt(a							)  # to pt

             elif isinstance(image[0]		,      np.ndarray							):
                    lowercase__      :		Union[str, Any]          =     np.concatenate(a		,      axis=0							) if image[0].ndim == 4 else np.stack(a		,      axis=0							)
                    lowercase__      :		Dict          =     self.numpy_to_pt(a							)
                    lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__      :		Union[str, Any]          =     image.shape
                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""							)

             elif isinstance(image[0]		,      torch.Tensor							):
                    lowercase__      :		Optional[int]          =     torch.cat(a		,      axis=0							) if image[0].ndim == 4 else torch.stack(a		,      axis=0							)
                    lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__      :		int          =     image.shape

                    # don't need any preprocess if the image is latents
                    if channel == 4:
                           return image

                    if self.config.do_resize and (
                        height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                    ):
                           raise ValueError(
                               f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                               f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""							)

        # expected range [0,1], normalize to [-1,1]
             lowercase__      :		Union[str, Any]          =     self.config.do_normalize
             if image.min() < 0:
                    warnings.warn(
                        'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                        f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""		,      a		,      )
                    lowercase__      :		Optional[Any]          =     False

             if do_normalize:
                    lowercase__      :		List[str]          =     self.normalize(a							)

             return image


      def 		_UpperCAmelCase	(       self		,      a		,      a = "pil"		,      a = None		,      )	-> Union[str, Any]:
             if not isinstance(a		,      torch.Tensor							):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(a							)}. We only support pytorch tensor"""							)
             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Optional[Any]          =     (
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    return image

             if do_denormalize is None:
                    lowercase__      :		List[str]          =     [self.config.do_normalize] * image.shape[0]

             lowercase__      :		Tuple          =     torch.stack(
                 [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

             if output_type == "pt":
                    return image

             lowercase__      :		Union[str, Any]          =     self.pt_to_numpy(a							)

             if output_type == "np":
                    return image

             if output_type == "pil":
                    return self.numpy_to_pil(a							)





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   int			  =  CONFIG_NAME


      @register_to_config
      def __init__(       self		,      a = True		,      a = 8		,      a = "lanczos"		,      a = True		,      )	-> int:
             super().__init__()


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> List[str]:
             if images.ndim == 3:
                    lowercase__      :		Union[str, Any]          =     images[None, ...]
             lowercase__      :		Any          =     (images * 2_5_5).round().astype('uint8'							)
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    lowercase__      :		str          =     [Image.fromarray(image.squeeze()		,      mode='L'							) for image in images]
             else:
                    lowercase__      :		Optional[int]          =     [Image.fromarray(image[:, :, :3]							) for image in images]

             return pil_images


      @staticmethod
      def 		_UpperCAmelCase	(       a							)	-> int:
             return image[:, :, 1] * 2**8 + image[:, :, 2]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[Any]:
             if images.ndim == 3:
                    lowercase__      :		Tuple          =     images[None, ...]
             lowercase__      :		Optional[Any]          =     images[:, :, :, 3:]
             if images.shape[-1] == 6:
                    lowercase__      :		Tuple          =     (images_depth * 2_5_5).round().astype('uint8'							)
                    lowercase__      :		Union[str, Any]          =     [
                        Image.fromarray(self.rgblike_to_depthmap(a							)		,      mode='I;16'							) for image_depth in images_depth
                    ]
             elif images.shape[-1] == 4:
                    lowercase__      :		List[str]          =     (images_depth * 65_535.0).astype(np.uintaa							)
                    lowercase__      :		List[Any]          =     [Image.fromarray(a		,      mode='I;16'							) for image_depth in images_depth]
             else:
                    raise Exception('Not supported'							)

             return pil_images


      def 		_UpperCAmelCase	(       self		,      a		,      a = "pil"		,      a = None		,      )	-> Tuple:
             if not isinstance(a		,      torch.Tensor							):
                    raise ValueError(
                        f"""Input for postprocessing is in incorrect format: {type(a							)}. We only support pytorch tensor"""							)
             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Union[str, Any]          =     (
                        f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		List[str]          =     'np'

             if do_denormalize is None:
                    lowercase__      :		Dict          =     [self.config.do_normalize] * image.shape[0]

             lowercase__      :		Tuple          =     torch.stack(
                 [self.denormalize(image[i]							) if do_denormalize[i] else image[i] for i in range(image.shape[0]							)]							)

             lowercase__      :		Optional[Any]          =     self.pt_to_numpy(a							)

             if output_type == "np":
                    if image.shape[-1] == 6:
                           lowercase__      :		Optional[int]          =     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]		,      axis=0							)
                    else:
                           lowercase__      :		List[Any]          =     image[:, :, :, 3:]
                    return image[:, :, :, :3], image_depth

             if output_type == "pil":
                    return self.numpy_to_pil(a							), self.numpy_to_depth(a							)
             else:
                    raise Exception(f"""This type {output_type} is not supported"""							)


"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output

