






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					List[str]									=		{
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					List[str]									=		[
       'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'NllbMoeForConditionalGeneration',
       'NllbMoeModel',
       'NllbMoePreTrainedModel',
       'NllbMoeTop2Router',
       'NllbMoeSparseMLP',
   ]


if TYPE_CHECKING:
   from .configuration_nllb_moe import (
       NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
       NllbMoeConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_nllb_moe import (
          NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
          NllbMoeForConditionalGeneration,
          NllbMoeModel,
          NllbMoePreTrainedModel,
          NllbMoeSparseMLP,
          NllbMoeTopaRouter,
      )


else:
   import sys

   _UpperCamelCase       :					Optional[int]									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












"""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 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  (
        '''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.'''
    )
    __magic_name__					  =  '''CIDAS/clipseg-rd64-refined'''
    __magic_name__					  =  '''image_segmenter'''
    __magic_name__					  =  CLIPSegForImageSegmentation

    __magic_name__					  =  ['''image''', '''text''']
    __magic_name__					  =  ['''image''']





    def __init__(							self			:      str       ,	*A			:      Any       ,	**A			:      Any    ):


      '''simple docstring'''




      requires_backends(self       ,	['vision']    )
      super().__init__(*A       ,	**A    )





    def        lowerCamelCase__   (							self			:      Any       ,	A			:      "Image"       ,	A			:      str    ):


      '''simple docstring'''




      return self.pre_processor(text=[label]       ,	images=[image]       ,	padding=A       ,	return_tensors='pt'    )





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


      '''simple docstring'''




      with torch.no_grad():
        a  :    Optional[Any]       =     self.model(**A    ).logits
      return logits





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


      '''simple docstring'''




      a  :    List[str]       =     outputs.cpu().detach().numpy()
      a  :    List[Any]       =     0
      a  :    str       =     1
      return Image.fromarray((array * 2_5_5).astype(np.uinta    )    )




