"""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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		(
         "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."
     )
     _UpperCamelCase							:      Optional[int]		 =		"CIDAS/clipseg-rd64-refined"
     _UpperCamelCase							:      Any		 =		"image_segmenter"
     _UpperCamelCase							:      List[str]		 =		CLIPSegForImageSegmentation

     _UpperCamelCase							:      Any		 =		["image", "text"]
     _UpperCamelCase							:      Tuple		 =		["image"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""vision"""]  )
       super().__init__(*a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__  ):
       return self.pre_processor(text=[label]    ,					images=[image]    ,					padding=a__    ,					return_tensors="""pt"""  )






     def    __A  (		self    ,					a__  ):
       with torch.no_grad():
         _lowerCAmelCase						:	int        =					self.model(**a__  ).logits
       return logits






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					outputs.cpu().detach().numpy()
       _lowerCAmelCase						:	str        =					0
       _lowerCAmelCase						:	Optional[Any]        =					1
       return Image.fromarray((array * 255).astype(np.uinta  )  )





"""simple docstring"""




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


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




