"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		A	     =					None


A	     =					logging.get_logger(__name__)


A	     =					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

A	     =					{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

A	     =					{
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
A	     =					["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					VOCAB_FILES_NAMES
					__lowerCAmelCase					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCAmelCase					=					PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase					=					["input_ids", "attention_mask"]
					__lowerCAmelCase					=					MBartTokenizer

					__lowerCAmelCase					=					[]
					__lowerCAmelCase					=					[]







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase="<s>"		,     _UpperCAmelCase="</s>"		,     _UpperCAmelCase="</s>"		,     _UpperCAmelCase="<s>"		,     _UpperCAmelCase="<unk>"		,     _UpperCAmelCase="<pad>"		,     _UpperCAmelCase="<mask>"		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase		,     ):
									__a	:  Tuple         =					AddedToken(_UpperCAmelCase		,     lstrip=_UpperCAmelCase		,     rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else mask_token

									super().__init__(
									    vocab_file=_UpperCAmelCase		,     tokenizer_file=_UpperCAmelCase		,     bos_token=_UpperCAmelCase		,     eos_token=_UpperCAmelCase		,     sep_token=_UpperCAmelCase		,     cls_token=_UpperCAmelCase		,     unk_token=_UpperCAmelCase		,     pad_token=_UpperCAmelCase		,     mask_token=_UpperCAmelCase		,     src_lang=_UpperCAmelCase		,     tgt_lang=_UpperCAmelCase		,     additional_special_tokens=_UpperCAmelCase		,     **_UpperCAmelCase		,     )

									__a	:  Any         =					vocab_file
									__a	:  Optional[Any]         =					False if not self.vocab_file else True

									__a	:  Dict         =					FAIRSEQ_LANGUAGE_CODES.copy()

									if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

									self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}       )
									__a	:  Union[str, Any]         =					{
									    lang_code: self.convert_tokens_to_ids(_UpperCAmelCase       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									__a	:  Tuple         =					src_lang if src_lang is not None else '''en_XX'''
									__a	:  int         =					self.convert_tokens_to_ids(self._src_lang       )
									__a	:  int         =					tgt_lang
									self.set_src_lang_special_tokens(self._src_lang       )







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







					@src_lang.setter
					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					new_src_lang
									self.set_src_lang_special_tokens(self._src_lang       )







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




									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







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

									if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):




									if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
									__a	:  List[str]         =					src_lang
									__a	:  Tuple         =					self(_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )
									__a	:  int         =					self.convert_tokens_to_ids(_UpperCAmelCase       )
									__a	:  List[Any]         =					tgt_lang_id
									return inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = "en_XX"		,     _UpperCAmelCase = None		,     _UpperCAmelCase = "ro_RO"		,     **_UpperCAmelCase		,     ):
									__a	:  str         =					src_lang
									__a	:  Union[str, Any]         =					tgt_lang
									return super().prepare_seqaseq_batch(_UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									return self.set_src_lang_special_tokens(self.src_lang       )







					def 	_lowerCamelCase						(  self       ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Any         =					self.convert_tokens_to_ids(_UpperCAmelCase       )
									__a	:  Optional[int]         =					[]
									__a	:  Union[str, Any]         =					[self.eos_token_id, self.cur_lang_code]

									__a	:  int         =					self.convert_ids_to_tokens(self.prefix_tokens       )
									__a	:  List[Any]         =					self.convert_ids_to_tokens(self.suffix_tokens       )

									__a	:  Tuple         =					processors.TemplateProcessing(
									    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,     self.prefix_tokens + self.suffix_tokens       )       )		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Union[str, Any]         =					self.convert_tokens_to_ids(_UpperCAmelCase       )
									__a	:  List[Any]         =					[]
									__a	:  List[Any]         =					[self.eos_token_id, self.cur_lang_code]

									__a	:  str         =					self.convert_ids_to_tokens(self.prefix_tokens       )
									__a	:  List[str]         =					self.convert_ids_to_tokens(self.suffix_tokens       )

									__a	:  Union[str, Any]         =					processors.TemplateProcessing(
									    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,     self.prefix_tokens + self.suffix_tokens       )       )		,     )







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




									if not self.can_save_slow_tokenizer:
													raise ValueError(
													    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
													    '''tokenizer.'''       )

									if not os.path.isdir(_UpperCAmelCase       ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""       )
													return
									__a	:  Tuple         =					os.path.join(
									    _UpperCAmelCase		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

									if os.path.abspath(self.vocab_file       ) != os.path.abspath(_UpperCAmelCase       ):
													copyfile(self.vocab_file		,     _UpperCAmelCase       )

									return (out_vocab_file,)






"""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       )       )