






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_						     =  logging.get_logger(__name__)


lowerCamelCase_						     =  Dict[str, Any]
lowerCamelCase_						     =  List[Prediction]




@add_end_docstrings(a_		)
class 							a_ (						a_		):


							'''simple docstring'''






							def __init__(		self			,			*lowercase_			,			**lowercase_   )					->					Dict:
														'''simple docstring'''

														super().__init__(*lowercase_			,			**lowercase_   )

														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			,			**lowercase_   )					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     {}
														lowerCAmelCase_										=     {}
														if "subtask" in kwargs:
																					lowerCAmelCase_										=     kwargs['subtask']
																					lowerCAmelCase_										=     kwargs['subtask']
														if "threshold" in kwargs:
																					lowerCAmelCase_										=     kwargs['threshold']
														if "mask_threshold" in kwargs:
																					lowerCAmelCase_										=     kwargs['mask_threshold']
														if "overlap_mask_area_threshold" in kwargs:
																					lowerCAmelCase_										=     kwargs['overlap_mask_area_threshold']

														return preprocess_kwargs, {}, postprocess_kwargs

							def __call__(		self			,			lowercase_			,			**lowercase_   )					->					Union[Predictions, List[Prediction]]:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_=None   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     load_image(lowercase_   )
														lowerCAmelCase_										=     [(image.height, image.width)]
														if self.model.config.__class__.__name__ == "OneFormerConfig":
																					if subtask is None:
																												lowerCAmelCase_										=     {}
																					else:
																												lowerCAmelCase_										=     {'task_inputs': [subtask]}
																					lowerCAmelCase_										=     self.image_processor(images=[image]			,			return_tensors='pt'			,			**lowercase_   )
																					lowerCAmelCase_										=     self.tokenizer(
																					    inputs['task_inputs']			,			padding='max_length'			,			max_length=self.model.config.task_seq_len			,			return_tensors=self.framework			,			)['input_ids']
														else:
																					lowerCAmelCase_										=     self.image_processor(images=[image]			,			return_tensors='pt'   )
														lowerCAmelCase_										=     target_size
														return inputs

							def      _lowercase       (		self			,			lowercase_   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     model_inputs.pop('target_size'   )
														lowerCAmelCase_										=     self.model(**lowercase_   )
														lowerCAmelCase_										=     target_size
														return model_outputs






							def      _lowercase       (		self			,			lowercase_			,			lowercase_=None			,			lowercase_=0.9			,			lowercase_=0.5			,			lowercase_=0.5   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     None
														if subtask in {"panoptic", None} and hasattr(self.image_processor			,			'post_process_panoptic_segmentation'   ):
																					lowerCAmelCase_										=     self.image_processor.post_process_panoptic_segmentation
														elif subtask in {"instance", None} and hasattr(self.image_processor			,			'post_process_instance_segmentation'   ):
																					lowerCAmelCase_										=     self.image_processor.post_process_instance_segmentation

														if fn is not None:
																					lowerCAmelCase_										=     fn(
																					    lowercase_			,			threshold=lowercase_			,			mask_threshold=lowercase_			,			overlap_mask_area_threshold=lowercase_			,			target_sizes=model_outputs['target_size']			,			)[0]

																					lowerCAmelCase_										=     []
																					lowerCAmelCase_										=     outputs['segmentation']

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

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

																					lowerCAmelCase_										=     []
																					lowerCAmelCase_										=     outputs.numpy()
																					lowerCAmelCase_										=     np.unique(lowercase_   )

																					for label in labels:
																												lowerCAmelCase_										=     (segmentation == label) * 2_5_5
																												lowerCAmelCase_										=     Image.fromarray(mask.astype(np.uinta   )			,			mode='L'   )
																												lowerCAmelCase_										=     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











import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase_						     =  logging.get_logger(__name__)
lowerCamelCase_						     =  {"""vocab_file""": """spiece.model"""}

lowerCamelCase_						     =  {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

lowerCamelCase_						     =  {
    """google/fnet-base""": 5_1_2,
    """google/fnet-large""": 5_1_2,
}

lowerCamelCase_						     =  """▁"""




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[int]     =    VOCAB_FILES_NAMES
							__a:	Union[str, Any]     =    PRETRAINED_VOCAB_FILES_MAP
							__a:	List[Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a:	Any     =    ['''input_ids''', '''token_type_ids''']

							def __init__(		self			,			lowercase_			,			lowercase_=False			,			lowercase_=True			,			lowercase_=True			,			lowercase_="<unk>"			,			lowercase_="[SEP]"			,			lowercase_="<pad>"			,			lowercase_="[CLS]"			,			lowercase_="[MASK]"			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														lowerCAmelCase_										=     (
														    AddedToken(lowercase_			,			lstrip=lowercase_			,			rstrip=lowercase_			,			normalized=lowercase_   )
														    if isinstance(lowercase_			,			lowercase_   )
														    else mask_token
														)

														lowerCAmelCase_										=     {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    do_lower_case=lowercase_			,			remove_space=lowercase_			,			keep_accents=lowercase_			,			unk_token=lowercase_			,			sep_token=lowercase_			,			pad_token=lowercase_			,			cls_token=lowercase_			,			mask_token=lowercase_			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowercase_			,			)

														lowerCAmelCase_										=     do_lower_case
														lowerCAmelCase_										=     remove_space
														lowerCAmelCase_										=     keep_accents
														lowerCAmelCase_										=     vocab_file

														lowerCAmelCase_										=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(lowercase_   )

							@property
							def      _lowercase       (		self   )					->					Optional[Any]:
														'''simple docstring'''

														return len(self.sp_model   )

							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     {self.convert_ids_to_tokens(lowercase_   ): i for i in range(self.vocab_size   )}
														vocab.update(self.added_tokens_encoder   )
														return vocab

							def __getstate__(		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     self.__dict__.copy()
														lowerCAmelCase_										=     None
														return state

							def __setstate__(		self			,			lowercase_   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     d

														# for backward compatibility
														if not hasattr(self			,			'sp_model_kwargs'   ):
																					lowerCAmelCase_										=     {}

														lowerCAmelCase_										=     spm.SentencePieceProcessor(**self.sp_model_kwargs   )
														self.sp_model.Load(self.vocab_file   )

							def      _lowercase       (		self			,			lowercase_   )					->					str:
														'''simple docstring'''

														if self.remove_space:
																					lowerCAmelCase_										=     ' '.join(inputs.strip().split()   )
														else:
																					lowerCAmelCase_										=     inputs
														lowerCAmelCase_										=     outputs.replace('``'			,			'"'   ).replace('\'\''			,			'"'   )

														if not self.keep_accents:
																					lowerCAmelCase_										=     unicodedata.normalize('NFKD'			,			lowercase_   )
																					lowerCAmelCase_										=     ''.join([c for c in outputs if not unicodedata.combining(lowercase_   )]   )
														if self.do_lower_case:
																					lowerCAmelCase_										=     outputs.lower()

														return outputs

							def      _lowercase       (		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.preprocess_text(lowercase_   )
														lowerCAmelCase_										=     self.sp_model.encode(lowercase_			,			out_type=lowercase_   )
														lowerCAmelCase_										=     []
														for piece in pieces:
																					if len(lowercase_   ) > 1 and piece[-1] == str(','   ) and piece[-2].isdigit():
																												lowerCAmelCase_										=     self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_			,			''   )   )
																												if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																			if len(cur_pieces[0]   ) == 1:
																																										lowerCAmelCase_										=     cur_pieces[1:]
																																			else:
																																										lowerCAmelCase_										=     cur_pieces[0][1:]
																												cur_pieces.append(piece[-1]   )
																												new_pieces.extend(lowercase_   )
																					else:
																												new_pieces.append(lowercase_   )

														return new_pieces

							def      _lowercase       (		self			,			lowercase_   )					->					Tuple:
														'''simple docstring'''

														return self.sp_model.PieceToId(lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														return self.sp_model.IdToPiece(lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     []
														lowerCAmelCase_										=     ''
														lowerCAmelCase_										=     False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												if not prev_is_special:
																																			out_string += " "
																												out_string += self.sp_model.decode(lowercase_   ) + token
																												lowerCAmelCase_										=     True
																												lowerCAmelCase_										=     []
																					else:
																												current_sub_tokens.append(lowercase_   )
																												lowerCAmelCase_										=     False
														out_string += self.sp_model.decode(lowercase_   )
														return out_string.strip()

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = False			,			lowercase_ = None			,			lowercase_ = True			,			**lowercase_			,			)					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     kwargs.pop('use_source_tokenizer'			,			lowercase_   )

														lowerCAmelCase_										=     self.convert_ids_to_tokens(lowercase_			,			skip_special_tokens=lowercase_   )

														# To avoid mixing byte-level and unicode for byte-level BPT
														# we need to build string separately for added tokens and byte-level tokens
														# cf. https://github.com/huggingface/transformers/issues/1133
														lowerCAmelCase_										=     []
														lowerCAmelCase_										=     []
														for token in filtered_tokens:
																					if skip_special_tokens and token in self.all_special_ids:
																												continue
																					if token in self.added_tokens_encoder:
																												if current_sub_text:
																																			sub_texts.append(self.convert_tokens_to_string(lowercase_   )   )
																																			lowerCAmelCase_										=     []
																												sub_texts.append(lowercase_   )
																					else:
																												current_sub_text.append(lowercase_   )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowercase_   )   )

														# Mimic the behavior of the Rust tokenizer:
														# No space after <unk>
														if spaces_between_special_tokens:
																					lowerCAmelCase_										=     re.sub(R'(<unk>) '			,			R'\1'			,			' '.join(lowercase_   )   )
														else:
																					lowerCAmelCase_										=     ''.join(lowercase_   )

														lowerCAmelCase_										=     (
														    clean_up_tokenization_spaces
														    if clean_up_tokenization_spaces is not None
														    else self.clean_up_tokenization_spaces
														)
														if clean_up_tokenization_spaces:
																					lowerCAmelCase_										=     self.clean_up_tokenization(lowercase_   )
																					return clean_text
														else:
																					return text

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     [self.sep_token_id]
														lowerCAmelCase_										=     [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = False   )					->					List[int]:
														'''simple docstring'''

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowercase_			,			token_ids_a=lowercase_			,			already_has_special_tokens=lowercase_   )

														if token_ids_a is not None:
																					return [1] + ([0] * len(lowercase_   )) + [1] + ([0] * len(lowercase_   )) + [1]
														return [1] + ([0] * len(lowercase_   )) + [1]

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     [self.sep_token_id]
														lowerCAmelCase_										=     [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   ) * [0] + len(token_ids_a + sep   ) * [1]






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					Tuple[str]:
														'''simple docstring'''

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

														if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase_   ) and os.path.isfile(self.vocab_file   ):
																					copyfile(self.vocab_file			,			lowercase_   )
														elif not os.path.isfile(self.vocab_file   ):
																					with open(lowercase_			,			'wb'   ) as fi:
																												lowerCAmelCase_										=     self.sp_model.serialized_model_proto()
																												fi.write(lowercase_   )

														return (out_vocab_file,)



