




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

				snake_case_           =						["image", "text"]
				snake_case_           =						["image"]


				def __init__(						self       :   List[Any]			,							*lowercase_       :   Any			,							**lowercase_       :   Dict   ):
									requires_backends(self			,							['''vision''']   )
									super().__init__(*lowercase_			,							**lowercase_   )


				def     A_					(						self       :   int			,							lowercase_       :   "Image"			,							lowercase_       :   str   ):
									return self.pre_processor(text=[label]			,							images=[image]			,							padding=lowercase_			,							return_tensors='''pt'''   )


				def     A_					(						self       :   int			,							lowercase_       :   Optional[int]   ):
									with torch.no_grad():
														snake_case_		     =							self.model(**lowercase_   ).logits
									return logits


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[str]   ):
									snake_case_		     =							outputs.cpu().detach().numpy()
									snake_case_		     =							0
									snake_case_		     =							1
									return Image.fromarray((array * 255).astype(np.uinta   )   )







'''simple docstring'''

import argparse
from collections import defaultdict

import yaml


a    :							List[Any]										=    'docs/source/en/_toctree.yml'


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_		     =							defaultdict(__UpperCAmelCase      )
					for doc in model_doc:
										counts[doc["local"]] += 1
					snake_case_		     =							[key for key, value in counts.items() if value > 1]

					snake_case_		     =							[]
					for duplicate_key in duplicates:
										snake_case_		     =							list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}      )
										if len(__UpperCAmelCase      ) > 1:
															raise ValueError(
															    F"{duplicate_key} is present several times in the documentation table of content at "
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''      )
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}      )

					# Add none duplicate-keys
					new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]      )

					# Sort
					return sorted(__UpperCAmelCase, key=lambda __UpperCAmelCase      : s["title"].lower()      )


def 						__magic_name__       (      __UpperCAmelCase=False      )						->   List[Any]:

					'''simple docstring'''

					with open(__UpperCAmelCase, encoding='''utf-8'''      ) as f:
										snake_case_		     =							yaml.safe_load(f.read()      )

					# Get to the API doc
					snake_case_		     =							0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					snake_case_		     =							content[api_idx]['''sections''']

					# Then to the model doc
					snake_case_		     =							0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					snake_case_		     =							api_doc[model_idx]['''sections''']

					snake_case_		     =							[(idx, section) for idx, section in enumerate(__UpperCAmelCase      ) if '''sections''' in section]
					snake_case_		     =							False
					for idx, modality_doc in modalities_docs:
										snake_case_		     =							modality_doc['''sections''']
										snake_case_		     =							clean_model_doc_toc(__UpperCAmelCase      )

										if old_modality_doc != new_modality_doc:
															snake_case_		     =							True
															if overwrite:
																				snake_case_		     =							new_modality_doc

					if diff:
										if overwrite:
															snake_case_		     =							model_doc
															snake_case_		     =							api_doc
															with open(__UpperCAmelCase, '''w''', encoding='''utf-8'''      ) as f:
																				f.write(yaml.dump(__UpperCAmelCase, allow_unicode=__UpperCAmelCase      )      )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''      )


if __name__ == "__main__":
							a    :							Dict										=    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							a    :							Union[str, Any]										=    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)

