import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   lowercase      (       _UpperCAmelCase       ):
							lowercase__  :    List[str]									=							["image_processor", "tokenizer"]
							lowercase__  :    int									=							"ViTImageProcessor"
							lowercase__  :    Optional[int]									=							("CLIPTokenizer", "CLIPTokenizerFast")




							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   int=None		,							**_UpperCamelCase				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																	    " instead."		,							_UpperCAmelCase		,							)
																	SCREAMING_SNAKE_CASE           =    kwargs.pop("feature_extractor" )

												SCREAMING_SNAKE_CASE           =    image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError("You need to specify an `image_processor`." )
												if tokenizer is None:
																	raise ValueError("You need to specify a `tokenizer`." )

												super().__init__(_UpperCAmelCase		,							_UpperCAmelCase )




							def __call__(  self				:   int		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   Dict=None		,							**_UpperCamelCase				:   Optional[int] )   ->			List[str]:



												'''simple docstring'''

												if text is None and visual_prompt is None and images is None:
																	raise ValueError("You have to specify either text, visual prompt or images." )

												if text is not None and visual_prompt is not None:
																	raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt." )

												if text is not None:
																	SCREAMING_SNAKE_CASE           =    self.tokenizer(_UpperCAmelCase		,							return_tensors=_UpperCAmelCase		,							**_UpperCAmelCase )

												if visual_prompt is not None:
																	SCREAMING_SNAKE_CASE           =    self.image_processor(_UpperCAmelCase		,							return_tensors=_UpperCAmelCase		,							**_UpperCAmelCase )

												if images is not None:
																	SCREAMING_SNAKE_CASE           =    self.image_processor(_UpperCAmelCase		,							return_tensors=_UpperCAmelCase		,							**_UpperCAmelCase )

												if visual_prompt is not None and images is not None:
																	SCREAMING_SNAKE_CASE           =    {
																	    '''pixel_values''': image_features.pixel_values,
																	    '''conditional_pixel_values''': prompt_features.pixel_values,
																	}
																	return encoding
												elif text is not None and images is not None:
																	SCREAMING_SNAKE_CASE           =    image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												elif visual_prompt is not None:
																	SCREAMING_SNAKE_CASE           =    {
																	    '''conditional_pixel_values''': prompt_features.pixel_values,
																	}
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_UpperCAmelCase )		,							tensor_type=_UpperCAmelCase )




							def 		__snake_case(  self				:   Dict		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Dict )   ->			str:



												'''simple docstring'''

												return self.tokenizer.batch_decode(*_UpperCAmelCase		,							**_UpperCAmelCase )




							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   List[str]		,							**_UpperCamelCase				:   int )   ->			Dict:



												'''simple docstring'''

												return self.tokenizer.decode(*_UpperCAmelCase		,							**_UpperCAmelCase )




							@property
							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,							_UpperCAmelCase		,							)
												return self.image_processor_class





							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			str:



												'''simple docstring'''

												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,							_UpperCAmelCase		,							)
												return self.image_processor







# 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      (       a       ):
							lowercase__  :    Optional[Any]									=							(
							    """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__  :    Optional[int]									=							"""CIDAS/clipseg-rd64-refined"""
							lowercase__  :    Tuple									=							"""image_segmenter"""
							lowercase__  :    Optional[Any]									=							CLIPSegForImageSegmentation

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




							def __init__(  self				:   str		,							*_UpperCamelCase				:   str		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												requires_backends(self		,							["vision"] )
												super().__init__(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   "Image"		,							_UpperCamelCase				:   str )   ->			Optional[int]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    outputs.cpu().detach().numpy()
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    1
												return Image.fromarray((array * 255).astype(np.uinta ) )




