



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__(_lowerCamelCase      ):

					"""simple docstring"""






					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 :     Optional[Any]				,       *SCREAMING_SNAKE_CASE :     Tuple				,       **SCREAMING_SNAKE_CASE :     Optional[Any]					):
									requires_backends(self				,       ["vision"]					)
									super().__init__(*lowercase_				,       **lowercase_					)







					def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     "Image"				,       SCREAMING_SNAKE_CASE :     str					):
									return self.pre_processor(text=[label]				,       images=[image]				,       padding=lowercase_				,       return_tensors="pt"					)







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     str					):
									with torch.no_grad():
													lowercase__					:   str													=					self.model(**lowercase_					).logits
									return logits







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Optional[int]													=					outputs.cpu().detach().numpy()
									lowercase__					:   Any													=					0
									lowercase__					:   Union[str, Any]													=					1
									return Image.fromarray((array * 255).astype(np.uinta					)					)





from typing import TYPE_CHECKING

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


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




