


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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 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							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """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   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


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


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


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



