





"""simple docstring"""






from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowerCAmelCase_			=							logging.get_logger(__name__)



class 	lowerCAmelCase					:
       lowerCAmelCase__						 =  42
       lowerCAmelCase__						 =  None






       @staticmethod
       def 							__A (   ):
              raise NotImplementedError






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  **a__     ):
              raise NotImplementedError






       def 							__A (   self    ,  a__     ):
              raise NotImplementedError






       def 							__A (   self     ):
              if not self.is_available():
                     raise RuntimeError(
                         f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."""     )






       @classmethod
       def 							__A (   cls     ):
              return f"""`pip install {cls.pip_package or cls.name}`"""



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """optuna"""






       @staticmethod
       def 							__A (   ):
              return is_optuna_available()






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  **a__     ):
              return run_hp_search_optuna(a__    ,  a__    ,  a__    ,  **a__     )






       def 							__A (   self    ,  a__     ):
              return default_hp_space_optuna(a__     )



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """ray"""
       lowerCAmelCase__						 =  """'ray[tune]'"""






       @staticmethod
       def 							__A (   ):
              return is_ray_available()






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  **a__     ):
              return run_hp_search_ray(a__    ,  a__    ,  a__    ,  **a__     )






       def 							__A (   self    ,  a__     ):
              return default_hp_space_ray(a__     )



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """sigopt"""






       @staticmethod
       def 							__A (   ):
              return is_sigopt_available()






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  **a__     ):
              return run_hp_search_sigopt(a__    ,  a__    ,  a__    ,  **a__     )






       def 							__A (   self    ,  a__     ):
              return default_hp_space_sigopt(a__     )



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """wandb"""






       @staticmethod
       def 							__A (   ):
              return is_wandb_available()






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  **a__     ):
              return run_hp_search_wandb(a__    ,  a__    ,  a__    ,  **a__     )






       def 							__A (   self    ,  a__     ):
              return default_hp_space_wandb(a__     )


lowerCAmelCase_			=							{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(SCREAMING_SNAKE_CASE							) > 0:
              _UpperCAmelCase     =     available_backends[0].name
              if len(SCREAMING_SNAKE_CASE							) > 1:
                     logger.info(
                         F"""{len(SCREAMING_SNAKE_CASE							)} hyperparameter search backends available. Using {name} as the default."""							)
              return name
       raise RuntimeError(
           'No hyperparameter search backend available.\n'
           + '\n'.join(
               F""" - To install {backend.name} run {backend.pip_install()}"""
               for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()							)							)













"""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     )     )






