





"""simple docstring"""






from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_pt_objects import *  # noqa F403
else:
    from .scheduling_consistency_models import CMStochasticIterativeScheduler
    from .scheduling_ddim import DDIMScheduler
    from .scheduling_ddim_inverse import DDIMInverseScheduler
    from .scheduling_ddim_parallel import DDIMParallelScheduler
    from .scheduling_ddpm import DDPMScheduler
    from .scheduling_ddpm_parallel import DDPMParallelScheduler
    from .scheduling_deis_multistep import DEISMultistepScheduler
    from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
    from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
    from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
    from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
    from .scheduling_euler_discrete import EulerDiscreteScheduler
    from .scheduling_heun_discrete import HeunDiscreteScheduler
    from .scheduling_ipndm import IPNDMScheduler
    from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
    from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
    from .scheduling_karras_ve import KarrasVeScheduler
    from .scheduling_pndm import PNDMScheduler
    from .scheduling_repaint import RePaintScheduler
    from .scheduling_sde_ve import ScoreSdeVeScheduler
    from .scheduling_sde_vp import ScoreSdeVpScheduler
    from .scheduling_unclip import UnCLIPScheduler
    from .scheduling_unipc_multistep import UniPCMultistepScheduler
    from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
    from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_flax_objects import *  # noqa F403
else:
    from .scheduling_ddim_flax import FlaxDDIMScheduler
    from .scheduling_ddpm_flax import FlaxDDPMScheduler
    from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
    from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
    from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
    from .scheduling_pndm_flax import FlaxPNDMScheduler
    from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
    from .scheduling_utils_flax import (
        FlaxKarrasDiffusionSchedulers,
        FlaxSchedulerMixin,
        FlaxSchedulerOutput,
        broadcast_to_shape_from_left,
    )


try:
    if not (is_torch_available() and is_scipy_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
    from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
    if not (is_torch_available() and is_torchsde_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
    from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler













"""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 	lowerCAmelCase					(      snake_case							):
       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 							__A (   self    ,  a__    ,  a__     ):
              return self.pre_processor(text=[label]    ,  images=[image]    ,  padding=a__    ,  return_tensors='pt'     )






       def 							__A (   self    ,  a__     ):
              with torch.no_grad():
                     _UpperCAmelCase     =     self.model(**a__     ).logits
              return logits






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     outputs.cpu().detach().numpy()
              _UpperCAmelCase     =     0
              _UpperCAmelCase     =     1
              return Image.fromarray((array * 2_55).astype(np.uinta     )     )






