






'''simple docstring'''







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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


UpperCamelCase_					=				logging.get_logger(__name__)

UpperCamelCase_					=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase_					=				{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class 					__UpperCAmelCase (		__A	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=False  ,		_UpperCAmelCase=True  ,		_UpperCAmelCase=False  ,		_UpperCAmelCase="<s>"  ,		_UpperCAmelCase="</s>"  ,		_UpperCAmelCase="<unk>"  ,		_UpperCAmelCase="<sep>"  ,		_UpperCAmelCase="<pad>"  ,		_UpperCAmelCase="<cls>"  ,		_UpperCAmelCase="<mask>"  ,		_UpperCAmelCase=["<eop>", "<eod>"]  ,		**_UpperCAmelCase  ,		):
             UpperCAmelCase__	:   Dict												=	AddedToken(__snake_case  ,		lstrip=__snake_case  ,		rstrip=__snake_case						) if isinstance(__snake_case  ,		__snake_case						) else mask_token

             super().__init__(
                 vocab_file=__snake_case  ,		tokenizer_file=__snake_case  ,		do_lower_case=__snake_case  ,		remove_space=__snake_case  ,		keep_accents=__snake_case  ,		bos_token=__snake_case  ,		eos_token=__snake_case  ,		unk_token=__snake_case  ,		sep_token=__snake_case  ,		pad_token=__snake_case  ,		cls_token=__snake_case  ,		mask_token=__snake_case  ,		additional_special_tokens=__snake_case  ,		**__snake_case  ,		)

             UpperCAmelCase__	:   Tuple												=	3
             UpperCAmelCase__	:   List[str]												=	do_lower_case
             UpperCAmelCase__	:   List[str]												=	remove_space
             UpperCAmelCase__	:   List[Any]												=	keep_accents
             UpperCAmelCase__	:   List[Any]												=	vocab_file
             UpperCAmelCase__	:   Any												=	False if not self.vocab_file else True

             try:
                    import jieba
             except ModuleNotFoundError as error:
                    raise error.__class__(
                        '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                        '''See https://pypi.org/project/jieba/ for installation.'''						)
             UpperCAmelCase__	:   str												=	jieba
             UpperCAmelCase__	:   Tuple												=	str.maketrans(''' \n'''  ,		'''\u2582\u2583'''						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None						):
             UpperCAmelCase__	:   Any												=	[self.sep_token_id]
             UpperCAmelCase__	:   List[Any]												=	[self.cls_token_id]
             if token_ids_a is None:
                    return token_ids_a + sep + cls
             return token_ids_a + sep + token_ids_a + sep + cls




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase = None						):
             UpperCAmelCase__	:   List[str]												=	[self.sep_token_id]
             UpperCAmelCase__	:   Dict												=	[2]

             if token_ids_a is None:
                    return len(token_ids_a + sep						) * [0] + cls_segment_id
             return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1] + cls_segment_id




      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(__snake_case						):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
                    return
             UpperCAmelCase__	:   Optional[Any]												=	os.path.join(
                 __snake_case  ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

             return (out_vocab_file,)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Tuple												=	[
                 " ".join([x.translate(self.translator						) for x in self.jieba.cut(__snake_case  ,		cut_all=__snake_case						)]						)
                 for text in batch_text_or_text_pairs
             ]
             return super()._batch_encode_plus(__snake_case  ,		*__snake_case  ,		**__snake_case						)




      def 							lowerCamelCase     (					self  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Any												=	super()._decode(*__snake_case  ,		**__snake_case						)
             UpperCAmelCase__	:   List[Any]												=	text.replace(''' '''  ,		''''''						).replace('''\u2582'''  ,		''' '''						).replace('''\u2583'''  ,		'''\n'''						)
             return text






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


__lowerCAmelCase			:Optional[Any]	 =     logging.get_logger(__name__)


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case			,    **__snake_case					)

							if self.framework == "tf":
												raise ValueError(f"""The {self.__class__} is only available in PyTorch."""					)

							requires_backends(self			,    "vision"					)
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)







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


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

							return super().__call__(__snake_case			,    **__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= load_image(__snake_case					)
							_snake_case							:       Tuple												= [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	_snake_case							:       List[Any]												= {}
												else:
																	_snake_case							:       int												= {"task_inputs": [subtask]}
												_snake_case							:       Tuple												= self.image_processor(images=[image]			,    return_tensors="pt"			,    **__snake_case					)
												_snake_case							:       Union[str, Any]												= self.tokenizer(
												    inputs["task_inputs"]			,    padding="max_length"			,    max_length=self.model.config.task_seq_len			,    return_tensors=self.framework			,    )["input_ids"]
							else:
												_snake_case							:       str												= self.image_processor(images=[image]			,    return_tensors="pt"					)
							_snake_case							:       Optional[int]												= target_size
							return inputs







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


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

							if fn is not None:
												_snake_case							:       Optional[Any]												= fn(
												    __snake_case			,    threshold=__snake_case			,    mask_threshold=__snake_case			,    overlap_mask_area_threshold=__snake_case			,    target_sizes=model_outputs["target_size"]			,    )[0]

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

							elif subtask in {"semantic", None} and hasattr(self.image_processor			,    "post_process_semantic_segmentation"					):
												_snake_case							:       Tuple												= self.image_processor.post_process_semantic_segmentation(
												    __snake_case			,    target_sizes=model_outputs["target_size"]					)[0]

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= self.model.config.idalabel[label]
																	annotation.append({"score": None, "label": label, "mask": mask}					)
							else:
												raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model					)}"""					)
							return annotation