"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase    :							Any													=						{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__lowerCAmelCase    :							Optional[Any]													=						[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			__lowerCAmelCase    :							str													=						_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       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "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."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




