




"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a            =							logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE__    (    lowercase_    ):
						_a			  =  ['pixel_values']


						def __init__(							self :	Tuple  ,  lowerCAmelCase :	bool = True  ,  lowerCAmelCase :	int = 32  ,  lowerCAmelCase :	List[Any]=PILImageResampling.BILINEAR  ,  lowerCAmelCase :	bool = True  ,  **lowerCAmelCase :	Optional[Any]  ,  ):
										lowerCAmelCase              = do_resize
										lowerCAmelCase              = do_rescale
										lowerCAmelCase              = size_divisor
										lowerCAmelCase              = resample
										super().__init__(**SCREAMING_SNAKE_CASE_    )


						def      __lowercase       (							self :	int  ,  lowerCAmelCase :	np.ndarray  ,  lowerCAmelCase :	int  ,  lowerCAmelCase :	Tuple  ,  lowerCAmelCase :	Optional[ChannelDimension] = None  ,  **lowerCAmelCase :	Union[str, Any]    ):
										lowerCAmelCase              = get_image_size(SCREAMING_SNAKE_CASE_    )
										# Rounds the height and width down to the closest multiple of size_divisor
										lowerCAmelCase              = height // size_divisor * size_divisor
										lowerCAmelCase              = width // size_divisor * size_divisor
										lowerCAmelCase              = resize(SCREAMING_SNAKE_CASE_  ,  (new_h, new_w)  ,  resample=SCREAMING_SNAKE_CASE_  ,  data_format=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )
										return image


						def      __lowercase       (							self :	str  ,  lowerCAmelCase :	np.ndarray  ,  lowerCAmelCase :	float  ,  lowerCAmelCase :	Optional[ChannelDimension] = None  ,  **lowerCAmelCase :	Union[str, Any]    ):
										return rescale(image=SCREAMING_SNAKE_CASE_  ,  scale=SCREAMING_SNAKE_CASE_  ,  data_format=SCREAMING_SNAKE_CASE_  ,  **SCREAMING_SNAKE_CASE_    )




						def      __lowercase       (							self :	str  ,  lowerCAmelCase :	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,  lowerCAmelCase :	Optional[bool] = None  ,  lowerCAmelCase :	Optional[int] = None  ,  lowerCAmelCase :	List[str]=None  ,  lowerCAmelCase :	Optional[bool] = None  ,  lowerCAmelCase :	Optional[Union[TensorType, str]] = None  ,  lowerCAmelCase :	ChannelDimension = ChannelDimension.FIRST  ,  **lowerCAmelCase :	Dict  ,  ):
										lowerCAmelCase              = do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase              = do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase              = size_divisor if size_divisor is not None else self.size_divisor
										lowerCAmelCase              = resample if resample is not None else self.resample

										if do_resize and size_divisor is None:
														raise ValueError("""size_divisor is required for resizing"""    )

										lowerCAmelCase              = make_list_of_images(SCREAMING_SNAKE_CASE_    )

										if not valid_images(SCREAMING_SNAKE_CASE_    ):
														raise ValueError("""Invalid image(s)"""    )

										# All transformations expect numpy arrays.
										lowerCAmelCase              = [to_numpy_array(SCREAMING_SNAKE_CASE_    ) for img in images]

										if do_resize:
														lowerCAmelCase              = [self.resize(SCREAMING_SNAKE_CASE_  ,  size_divisor=SCREAMING_SNAKE_CASE_  ,  resample=SCREAMING_SNAKE_CASE_    ) for image in images]

										if do_rescale:
														lowerCAmelCase              = [self.rescale(SCREAMING_SNAKE_CASE_  ,  scale=1 / 255    ) for image in images]

										lowerCAmelCase              = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ) for image in images]

										lowerCAmelCase              = {'pixel_values': images}
										return BatchFeature(data=SCREAMING_SNAKE_CASE_  ,  tensor_type=SCREAMING_SNAKE_CASE_    )










"""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       UpperCamelCase__   (	lowercase_  ):




      """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				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        requires_backends(self    ,			['vision']       )
        super().__init__(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      "Image"    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= self.model(**SCREAMING_SNAKE_CASE_       ).logits
        return logits





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= outputs.cpu().detach().numpy()
        lowerCAmelCase_   :				Optional[Any]								= 0
        lowerCAmelCase_   :				Optional[Any]								= 1
        return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )





