"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A	     =					logging.get_logger(__name__)


A	     =					{
    '''nielsr/canine-s''': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
A	     =					1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
A	     =					0

A	     =					0XE000
A	     =					0XE001
A	     =					0XE002
A	     =					0XE003
A	     =					0XE004

# Maps special codepoints to human-readable names.
A	     =					{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: '''[CLS]''',
    SEP: '''[SEP]''',
    BOS: '''[BOS]''',
    MASK: '''[MASK]''',
    PAD: '''[PAD]''',
    RESERVED: '''[RESERVED]''',
}

# Maps special codepoint human-readable names to their codepoint values.
A	     =					{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}




class 			__lowercase     (     snake_case_							):







					'''simple docstring'''




					__lowerCAmelCase					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







					def __init__(  self		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=chr(_UpperCAmelCase       )		,     _UpperCAmelCase=False		,     _UpperCAmelCase=2048		,     **_UpperCAmelCase		,     ):
									__a	:  Any         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else bos_token
									__a	:  Tuple         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else eos_token
									__a	:  Optional[int]         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else sep_token
									__a	:  str         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else cls_token
									__a	:  Dict         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									__a	:  Any         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else mask_token

									super().__init__(
									    bos_token=_UpperCAmelCase		,     eos_token=_UpperCAmelCase		,     sep_token=_UpperCAmelCase		,     cls_token=_UpperCAmelCase		,     pad_token=_UpperCAmelCase		,     mask_token=_UpperCAmelCase		,     add_prefix_space=_UpperCAmelCase		,     model_max_length=_UpperCAmelCase		,     **_UpperCAmelCase		,     )

									# Creates a mapping for looking up the IDs of special symbols.
									__a	:  Union[str, Any]         =					{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
													__a	:  int         =					codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									__a	:  Optional[Any]         =					{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									__a	:  Dict         =					UNICODE_VOCAB_SIZE
									__a	:  Union[str, Any]         =					len(self._special_codepoints       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self._unicode_vocab_size







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return list(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									try:
													return ord(_UpperCAmelCase       )
									except TypeError:
													raise ValueError(f"""invalid token: \'{token}\'"""       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									try:
													if index in SPECIAL_CODEPOINTS:
																	return SPECIAL_CODEPOINTS[index]
													return chr(_UpperCAmelCase       )
									except TypeError:
													raise ValueError(f"""invalid id: {index}"""       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return "".join(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									__a	:  Dict         =					[self.sep_token_id]
									__a	:  Optional[Any]         =					[self.cls_token_id]

									__a	:  Optional[int]         =					cls + token_ids_a + sep
									if token_ids_a is not None:
													result += token_ids_a + sep
									return result







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False       ):
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_UpperCAmelCase		,     token_ids_a=_UpperCAmelCase		,     already_has_special_tokens=_UpperCAmelCase       )

									__a	:  Optional[int]         =					[1] + ([0] * len(_UpperCAmelCase       )) + [1]
									if token_ids_a is not None:
													result += ([0] * len(_UpperCAmelCase       )) + [1]
									return result







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									__a	:  Any         =					[self.sep_token_id]
									__a	:  Dict         =					[self.cls_token_id]

									__a	:  List[str]         =					len(cls + token_ids_a + sep       ) * [0]
									if token_ids_a is not None:
													result += len(token_ids_a + sep       ) * [1]
									return result







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None       ):
									return ()






"""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 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''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					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







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