


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""pixel_values"""]


     def __init__(	self  ,       __A = True  ,       __A = None  ,       __A = PILImageResampling.BILINEAR  ,       __A = True  ,       __A = None  ,       __A = True  ,       __A = 1 / 255  ,       __A = True  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        super().__init__(**__A    )
        __a					=  size if size is not None else {"""shortest_edge""": 256}
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __a					=  get_size_dict(__A    )
        __a					=  do_resize
        __a					=  size
        __a					=  resample
        __a					=  do_center_crop
        __a					=  crop_size
        __a					=  do_rescale
        __a					=  rescale_factor
        __a					=  do_normalize
        __a					=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a					=  image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = PILImageResampling.BICUBIC  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        if "shortest_edge" not in size:
           raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
        __a					=  get_resize_output_image_size(__A  ,       size=size["""shortest_edge"""]  ,       default_to_square=__A    )
        return resize(__A  ,       size=__A  ,       resample=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        __a					=  get_size_dict(__A    )
        return center_crop(__A  ,       size=(size["""height"""], size["""width"""])  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A = None  ,       **__A    ):
        return rescale(__A  ,       scale=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = None  ,       **__A  ,       ):
        return normalize(__A  ,       mean=__A  ,       std=__A  ,       data_format=__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = ChannelDimension.FIRST  ,       **__A  ,       ):
        __a					=  do_resize if do_resize is not None else self.do_resize
        __a					=  size if size is not None else self.size
        __a					=  get_size_dict(__A  ,       default_to_square=__A    )
        __a					=  resample if resample is not None else self.resample
        __a					=  do_center_crop if do_center_crop is not None else self.do_center_crop
        __a					=  crop_size if crop_size is not None else self.crop_size
        __a					=  get_size_dict(__A    )
        __a					=  do_rescale if do_rescale is not None else self.do_rescale
        __a					=  rescale_factor if rescale_factor is not None else self.rescale_factor
        __a					=  do_normalize if do_normalize is not None else self.do_normalize
        __a					=  image_mean if image_mean is not None else self.image_mean
        __a					=  image_std if image_std is not None else self.image_std

        __a					=  make_list_of_images(__A    )

        if not valid_images(__A    ):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""    )

        if do_resize and size is None:
           raise ValueError("""Size must be specified if do_resize is True."""    )

        if do_center_crop and crop_size is None:
           raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

        if do_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

        if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

        # All transformations expect numpy arrays.
        __a					=  [to_numpy_array(__A    ) for image in images]

        if do_resize:
           __a					=  [self.resize(image=__A  ,       size=__A  ,       resample=__A    ) for image in images]

        if do_center_crop:
           __a					=  [self.center_crop(image=__A  ,       size=__A    ) for image in images]

        if do_rescale:
           __a					=  [self.rescale(image=__A  ,       scale=__A    ) for image in images]

        if do_normalize:
           __a					=  [self.normalize(image=__A  ,       mean=__A  ,       std=__A    ) for image in images]

        __a					=  [to_channel_dimension_format(__A  ,       __A    ) for image in images]

        __a					=  {"""pixel_values""": images}
        return BatchFeature(data=__A  ,       tensor_type=__A    )







# 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    )    )



