





'''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 							lowercase__     (							snake_case_       ):
	lowercase__      =      (
	    """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."""
	)
	lowercase__      =      """CIDAS/clipseg-rd64-refined"""
	lowercase__      =      """image_segmenter"""
	lowercase__      =      CLIPSegForImageSegmentation

	lowercase__      =      ["""image""", """text"""]
	lowercase__      =      ["""image"""]





	def __init__(			self :    List[str]			,*lowerCamelCase__ :    int			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							requires_backends(self			,['vision']			)
							super().__init__(*lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    "Image"			,lowerCamelCase__ :    str			):







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





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''





							with torch.no_grad():
													_UpperCamelCase :		Any			    =       self.model(**lowerCamelCase__			).logits
							return logits





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       outputs.cpu().detach().numpy()
							_UpperCamelCase :		str			    =       0
							_UpperCamelCase :		List[Any]			    =       1
							return Image.fromarray((array * 255).astype(np.uinta			)			)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Dict  	=  _LazyModule(__name__, globals()['__file__'], _import_structure)


