



'''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 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					(
							    '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.'
							)
							lowerCAmelCase       :    List[str]   				=					'CIDAS/clipseg-rd64-refined'
							lowerCAmelCase       :    Optional[int]   				=					'image_segmenter'
							lowerCAmelCase       :    Union[str, Any]   				=					CLIPSegForImageSegmentation

							lowerCAmelCase       :    str   				=					['image', 'text']
							lowerCAmelCase       :    int   				=					['image']







							def __init__(							self					:       Tuple	,*_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       Any		):
									requires_backends(self	,['vision']		)
									super().__init__(*_UpperCAmelCase	,**_UpperCAmelCase		)







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







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[Any]		):
									with torch.no_grad():
											_a     :      int        =							self.model(**_UpperCAmelCase		).logits
									return logits







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Dict        =							outputs.cpu().detach().numpy()
									_a     :      List[str]        =							0
									_a     :      int        =							1
									return Image.fromarray((array * 255).astype(np.uinta		)		)









'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




