
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a														=		{"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		["""FNetTokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		["""FNetTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """FNetForMaskedLM""",
				    """FNetForMultipleChoice""",
				    """FNetForNextSentencePrediction""",
				    """FNetForPreTraining""",
				    """FNetForQuestionAnswering""",
				    """FNetForSequenceClassification""",
				    """FNetForTokenClassification""",
				    """FNetLayer""",
				    """FNetModel""",
				    """FNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet import FNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet_fast import FNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fnet import (
								    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FNetForMaskedLM,
								    FNetForMultipleChoice,
								    FNetForNextSentencePrediction,
								    FNetForPreTraining,
								    FNetForQuestionAnswering,
								    FNetForSequenceClassification,
								    FNetForTokenClassification,
								    FNetLayer,
								    FNetModel,
								    FNetPreTrainedModel,
								)


else:
				import sys

				_a														=		_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    _UpperCAmelCase(   lowerCamelCase     ):
		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    ,		*__a    ,		**__a)	->	Any:






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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






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



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






