





# 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    __snake_case	(							__lowerCamelCase      ):





       '''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			:   List[Any]							,    *A			:   Any							,    **A			:   Optional[int]		):
         requires_backends(self							,    ["""vision"""]		)
         super().__init__(*_SCREAMING_SNAKE_CASE							,    **_SCREAMING_SNAKE_CASE		)




       def 							UpperCAmelCase__			(  self			:   Optional[int]							,    A			:   Tuple							,    A			:   Optional[int]		):
         return self.pre_processor(text=[label]							,    images=[image]							,    padding=_SCREAMING_SNAKE_CASE							,    return_tensors="""pt"""		)




       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   List[Any]		):





         with torch.no_grad():
           __snake_case:						List[Any]			  =						self.model(**_SCREAMING_SNAKE_CASE		).logits
         return logits






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













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






