
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__   ):
   _SCREAMING_SNAKE_CASE				:							Optional[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."
   )
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				"CIDAS/clipseg-rd64-refined"
   _SCREAMING_SNAKE_CASE				:							Any     	=				"image_segmenter"
   _SCREAMING_SNAKE_CASE				:							str     	=				CLIPSegForImageSegmentation

   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				["image", "text"]
   _SCREAMING_SNAKE_CASE				:							Any     	=				["image"]
   def __init__(self			: Optional[int] ,    *snake_case_			: Optional[int] ,    **snake_case_			: Union[str, Any]       ):
          requires_backends(self ,    ['''vision''']       )
          super().__init__(*_lowerCamelCase ,    **_lowerCamelCase       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: "Image" ,    snake_case_			: str       ):
          return self.pre_processor(text=[label] ,    images=[image] ,    padding=_lowerCamelCase ,    return_tensors='''pt'''       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Optional[int]       ):







          with torch.no_grad():
                 __a			:  Optional[int]		    =  self.model(**_lowerCamelCase       ).logits
          return logits







   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: Optional[int]       ):
          __a			:  int		    =  outputs.cpu().detach().numpy()
          __a			:  Tuple		    =  0
          __a			:  List[str]		    =  1
          return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )


# Copyright 2023 The HuggingFace 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
