





"""simple docstring"""




import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
		from PIL import Image


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Tuple    =(
							    "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
							    "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
							    "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
							)
							lowerCamelCase  :   int    ="CIDAS/clipseg-rd64-refined"
							lowerCamelCase  :   Union[str, Any]    ="image_segmenter"
							lowerCamelCase  :   Dict    =CLIPSegForImageSegmentation

							lowerCamelCase  :   Tuple    =["image", "text"]
							lowerCamelCase  :   Optional[int]    =["image"]






							def __init__(  self ,   *lowerCAmelCase__ ,   **lowerCAmelCase__ )      ->					Any:
								requires_backends(self ,   ["vision"] )
								super().__init__(*_lowerCAmelCase ,   **_lowerCAmelCase )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[str]:
								return self.pre_processor(text=[label] ,   images=[image] ,   padding=_lowerCAmelCase ,   return_tensors="pt" )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Dict:
								with torch.no_grad():
									a :						Any      =							self.model(**_lowerCAmelCase ).logits
								return logits






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int:
								a :						Dict      =							outputs.cpu().detach().numpy()
								a :						Optional[Any]      =							0
								a :						Dict      =							1
								return Image.fromarray((array * 255).astype(np.uinta ) )









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


