'''simple docstring'''
# 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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(
         '''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__		   =						'''CIDAS/clipseg-rd64-refined'''
     SCREAMING_SNAKE_CASE__		   =						'''image_segmenter'''
     SCREAMING_SNAKE_CASE__		   =						CLIPSegForImageSegmentation

     SCREAMING_SNAKE_CASE__		   =						['''image''', '''text''']
     SCREAMING_SNAKE_CASE__		   =						['''image''']







     def __init__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         requires_backends(self     ,			['vision']    )
         super().__init__(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         return self.pre_processor(text=[label]     ,			images=[image]     ,			padding=_lowerCamelCase     ,			return_tensors='pt'    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         with torch.no_grad():
             __UpperCamelCase	:	List[str]		    =  self.model(**_lowerCamelCase    ).logits
         return logits







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  outputs.cpu().detach().numpy()
         __UpperCamelCase	:	Dict		    =  0
         __UpperCamelCase	:	Any		    =  1
         return Image.fromarray((array * 2_5_5).astype(np.uinta    )    )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a=  {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''ViTMSNModel''',
      '''ViTMSNForImageClassification''',
      '''ViTMSNPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



