'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_A								=   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_A								=   logging.get_logger(__name__)


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    '''maskformer'''
   A							:       List[str]     =    {'''hidden_size''': '''mask_feature_size'''}
   A							:       Dict     =    ['''resnet''', '''swin''']
   A							:       Optional[Any]     =    ['''detr''']
   def __init__(self       ,				_a = 256       ,				_a = 256       ,				_a = 0.1       ,				_a = False       ,				_a = None       ,				_a = None       ,				_a = 0.02       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 1.0       ,				_a = 20.0       ,				_a = None       ,				**_a       ,				)    ->     int:
        if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             lowercase_					:       List[Any]     				=					SwinConfig(
                 image_size=384       ,				in_channels=3       ,				patch_size=4       ,				embed_dim=128       ,				depths=[2, 2, 18, 2]       ,				num_heads=[4, 8, 16, 32]       ,				window_size=12       ,				drop_path_rate=0.3       ,				out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,				)

        if isinstance(_a       ,				_a				):
             lowercase_					:       Dict     				=					backbone_config.pop('model_type'				)
             lowercase_					:       Tuple     				=					CONFIG_MAPPING[backbone_model_type]
             lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                 f'''Supported model types: {",".join(self.backbones_supported				)}'''				)

        if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             lowercase_					:       str     				=					DetrConfig()
        else:
             # verify that the decoder is supported
             lowercase_					:       List[Any]     				=					(
                 decoder_config.pop('model_type'				) if isinstance(_a       ,				_a				) else decoder_config.model_type
             )
             if decoder_type not in self.decoders_supported:
                  raise ValueError(
                      f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                      f''' {",".join(self.decoders_supported				)}'''				)
             if isinstance(_a       ,				_a				):
                  lowercase_					:       List[Any]     				=					CONFIG_MAPPING[decoder_type]
                  lowercase_					:       Tuple     				=					config_class.from_dict(_a				)

        lowercase_					:       Optional[Any]     				=					backbone_config
        lowercase_					:       int     				=					decoder_config
        # main feature dimension for the model
        lowercase_					:       List[Any]     				=					fpn_feature_size
        lowercase_					:       Tuple     				=					mask_feature_size
        # initializer
        lowercase_					:       Optional[int]     				=					init_std
        lowercase_					:       List[str]     				=					init_xavier_std
        # Hungarian matcher && loss
        lowercase_					:       int     				=					cross_entropy_weight
        lowercase_					:       Optional[int]     				=					dice_weight
        lowercase_					:       Dict     				=					mask_weight
        lowercase_					:       Tuple     				=					use_auxiliary_loss
        lowercase_					:       int     				=					no_object_weight
        lowercase_					:       Union[str, Any]     				=					output_auxiliary_logits

        lowercase_					:       List[Any]     				=					self.decoder_config.encoder_attention_heads
        lowercase_					:       Tuple     				=					self.decoder_config.num_hidden_layers
        super().__init__(**_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     Union[str, Any]:
        return cls(
            backbone_config=_a       ,				decoder_config=_a       ,				**_a       ,				)





   def 		_lowerCamelCase (self				)    ->     Dict[str, any]:
        lowercase_					:       Optional[int]     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Union[str, Any]     				=					self.backbone_config.to_dict()
        lowercase_					:       Optional[Any]     				=					self.decoder_config.to_dict()
        lowercase_					:       Dict     				=					self.__class__.model_type
        return output





'''simple docstring'''




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A								=   logging.get_logger(__name__)

_A								=   '▁'

_A								=   {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_A								=   {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_A								=   {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_A								=   {
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

_A								=   {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    ["input_ids"]

   A							:       Optional[Any]     =    VOCAB_FILES_NAMES
   A							:       Dict     =    PRETRAINED_INIT_CONFIGURATION
   A							:       int     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       List[str]     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       Any     =    RESOURCE_FILES_NAMES
   def __init__(self       ,				_a       ,				_a=None       ,				_a=False       ,				_a="utf8"       ,				_a="[UNK]"       ,				_a="[SEP]"       ,				_a="[PAD]"       ,				_a="[CLS]"       ,				_a="[MASK]"       ,				_a = None       ,				**_a       ,				)    ->     None:
        # Mask token behave like a normal word, i.e. include the space before it and
        # is included in the raw text, there should be a match in a non-normalized sentence.

        lowercase_					:       Optional[Any]     				=					{} if sp_model_kwargs is None else sp_model_kwargs
        super().__init__(
            do_lower_case=_a       ,				unk_token=_a       ,				sep_token=_a       ,				pad_token=_a       ,				cls_token=_a       ,				mask_token=_a       ,				vocab_file=_a       ,				encoding=_a       ,				sp_model_kwargs=self.sp_model_kwargs       ,				**_a       ,				)
        lowercase_					:       int     				=					do_lower_case
        lowercase_					:       str     				=					sentencepiece_model_ckpt
        lowercase_					:       Tuple     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_a				)

        # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
        if vocab_file is not None:
             lowercase_					:       Union[str, Any]     				=					self.load_vocab(filepath=_a				)
        else:
             lowercase_					:       Any     				=					{self.sp_model.id_to_piece(_a				): id for id in range(self.sp_model.get_piece_size()				)}
        lowercase_					:       Optional[int]     				=					{v: k for k, v in self.vocab.items()}
   def 		_lowerCamelCase (self       ,				_a				)    ->     Any:
        if text is None:
             return None

        lowercase_					:       Dict     				=					self.tokenize(_a				)
        lowercase_				,lowercase_					:       Dict     				=					'', []

        for i, ch in enumerate(_a				):
             if ch in self.SP_CHAR_MAPPING:
                  lowercase_					:       str     				=					self.SP_CHAR_MAPPING.get(_a				)
             else:
                  lowercase_					:       List[str]     				=					unicodedata.normalize('NFKC'       ,				_a				)
             if self.is_whitespace(_a				):
                  continue
             normalized_text += ch
             char_mapping.extend([i] * len(_a				)				)

        lowercase_				,lowercase_				,lowercase_					:       int     				=					normalized_text, [], 0

        if self.do_lower_case:
             lowercase_					:       Optional[Any]     				=					text.lower()

        for token in split_tokens:
             if token[:1] == "▁":
                  lowercase_					:       Tuple     				=					token[1:]
             lowercase_					:       List[str]     				=					text[offset:].index(_a				) + offset
             lowercase_					:       Any     				=					start + len(_a				)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)				)
             lowercase_					:       Tuple     				=					end
        return token_mapping
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return len(self.vocab				)
   def 		_lowerCamelCase (self				)    ->     int:
        return dict(self.vocab       ,				**self.added_tokens_encoder				)
   def __getstate__(self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					self.__dict__.copy()
        lowercase_					:       List[str]     				=					None
        return state
   def __setstate__(self       ,				_a				)    ->     Any:
        lowercase_					:       Optional[int]     				=					d

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

        lowercase_					:       Optional[int]     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.sentencepiece_model_ckpt				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     str:
        return "".join((self.SP_CHAR_MAPPING.get(_a       ,				_a				) for c in text)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=False       ,				_a=64       ,				_a=0.1				)    ->     Optional[int]:

        if self.sp_model_kwargs.get('enable_sampling'				) is True:
             lowercase_					:       Optional[int]     				=					True
        if self.sp_model_kwargs.get('alpha'				) is not None:
             lowercase_					:       Union[str, Any]     				=					self.sp_model_kwargs.get('alpha'				)
        if self.sp_model_kwargs.get('nbest_size'				) is not None:
             lowercase_					:       Optional[int]     				=					self.sp_model_kwargs.get('nbest_size'				)

        if not enable_sampling:
             lowercase_					:       Optional[Any]     				=					self.sp_model.EncodeAsPieces(_a				)
        else:
             lowercase_					:       List[Any]     				=					self.sp_model.SampleEncodeAsPieces(_a       ,				_a       ,				_a				)
        lowercase_					:       int     				=					[]
        for pi, piece in enumerate(_a				):
             if piece == SPIECE_UNDERLINE:
                  if not pieces[pi + 1].startswith(_a				) and pi != 0:
                       new_pieces.append(_a				)
                       continue
                  else:
                       continue
             lowercase_					:       Optional[Any]     				=					0
             for i, chunk in enumerate(_a				):
                  if chunk == SPIECE_UNDERLINE:
                       continue
                  if self.is_ch_char(_a				) or self.is_punct(_a				):
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       new_pieces.append(_a				)
                       lowercase_					:       List[Any]     				=					i + 1
                  elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       Optional[Any]     				=					i
                  elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       int     				=					i
             if len(_a				) > lst_i:
                  new_pieces.append(piece[lst_i:]				)
        return new_pieces
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Optional[Any]     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(_a				)
        lowercase_					:       Tuple     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        return self.vocab.get(_a       ,				self.vocab.get(self.unk_token				)				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        return self.reverse_vocab.get(_a       ,				self.unk_token				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Any:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase_					:       Tuple     				=					[self.cls_token_id]
        lowercase_					:       int     				=					[self.sep_token_id]
        return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Optional[Any]:
        if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

        return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None       ,				_a=False				)    ->     Union[str, Any]:

        if already_has_special_tokens:
             if token_ids_a is not None:
                  raise ValueError(
                      'You should not supply a second sequence if the provided sequence of '
                      'ids is already formatted with special tokens for the model.'				)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
             return [1] + ([0] * len(_a				)) + [1, 1] + ([0] * len(_a				)) + [1]
        return [1] + ([0] * len(_a				)) + [1]
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
        if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(_a				) + 2) * [0]

        # [CLS] A [SEP] [SEP] B [SEP]
        return [0] * (len(_a				) + 1) + [1] * (len(_a				) + 3)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if "\u4e00" <= char <= "\u9fff":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if char in ",;:.?!~，；：。？！《》【】":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
        if len(_a				) == 1:
             lowercase_					:       Optional[Any]     				=					unicodedata.category(_a				)
             if cat == "Zs":
                  return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     Tuple:
        lowercase_					:       List[Any]     				=					{}
        with io.open(_a       ,				'r'       ,				encoding='utf-8'				) as f:
             for index, line in enumerate(_a				):
                  lowercase_					:       Dict     				=					line.rstrip('\n'				)
                  lowercase_					:       Dict     				=					int(_a				)

        return token_to_idx





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        lowercase_					:       Tuple     				=					0
        if os.path.isdir(_a				):
             lowercase_					:       Any     				=					os.path.join(
                 _a       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
        else:
             lowercase_					:       Tuple     				=					(filename_prefix + '-' if filename_prefix else '') + save_directory
        with open(_a       ,				'w'       ,				encoding='utf-8'				) as writer:
             for token, token_index in sorted(self.vocab.items()       ,				key=lambda _a				: kv[1]				):
                  if index != token_index:
                       logger.warning(
                           f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                           ' Please check that the vocabulary is not corrupted!'				)
                       lowercase_					:       Tuple     				=					token_index
                  writer.write(token + '\n'				)
                  index += 1

        lowercase_					:       List[str]     				=					os.path.join(_a       ,				'sentencepiece.bpe.model'				)
        with open(_a       ,				'wb'				) as fi:
             lowercase_					:       Tuple     				=					self.sp_model.serialized_model_proto()
             fi.write(_a				)

        return (vocab_file,)




