
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[int]     ={'vocab_file': 'vocab.txt'}

_UpperCamelCase:					Any     ={
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

_UpperCamelCase:					Union[str, Any]     ={
    'tau/splinter-base': 512,
    'tau/splinter-base-qass': 512,
    'tau/splinter-large': 512,
    'tau/splinter-large-qass': 512,
}

_UpperCamelCase:					Optional[Any]     ={
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							VOCAB_FILES_NAMES
       UpperCamelCase_				 =							PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_				 =							PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase_				 =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_				 =							SplinterTokenizer



       def __init__(     self					:			str  ,      _lowerCAmelCase					:			Tuple=None  ,      _lowerCAmelCase					:			Optional[Any]=None  ,      _lowerCAmelCase					:			Dict=True  ,      _lowerCAmelCase					:			Dict="[UNK]"  ,      _lowerCAmelCase					:			Tuple="[SEP]"  ,      _lowerCAmelCase					:			Tuple="[PAD]"  ,      _lowerCAmelCase					:			List[Any]="[CLS]"  ,      _lowerCAmelCase					:			Optional[Any]="[MASK]"  ,      _lowerCAmelCase					:			Optional[Any]="[QUESTION]"  ,      _lowerCAmelCase					:			Optional[Any]=True  ,      _lowerCAmelCase					:			Optional[Any]=None  ,      **_lowerCAmelCase					:			Optional[Any]  ,      )					->   Optional[Any]:
         super().__init__(
             _lowerCAmelCase  ,      tokenizer_file=_lowerCAmelCase  ,      do_lower_case=_lowerCAmelCase  ,      unk_token=_lowerCAmelCase  ,      sep_token=_lowerCAmelCase  ,      pad_token=_lowerCAmelCase  ,      cls_token=_lowerCAmelCase  ,      mask_token=_lowerCAmelCase  ,      tokenize_chinese_chars=_lowerCAmelCase  ,      strip_accents=_lowerCAmelCase  ,      additional_special_tokens=(question_token,)  ,      **_lowerCAmelCase  ,      )

         _lowerCAmelCase						=		json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             pre_tok_state.get('lowercase'  ,      _lowerCAmelCase       ) != do_lower_case
             or pre_tok_state.get('strip_accents'  ,      _lowerCAmelCase       ) != strip_accents
         ):
           _lowerCAmelCase						=		getattr(_lowerCAmelCase  ,      pre_tok_state.pop('type'       )       )
           _lowerCAmelCase						=		do_lower_case
           _lowerCAmelCase						=		strip_accents
           _lowerCAmelCase						=		pre_tok_class(**_lowerCAmelCase       )

         _lowerCAmelCase						=		do_lower_case



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[int]:
         return self.convert_tokens_to_ids(self.question_token       )



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]  ,      _lowerCAmelCase					:			List[int]  ,      _lowerCAmelCase					:			Optional[List[int]] = None       )					->   List[int]:
         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         _lowerCAmelCase						=		[self.cls_token_id]
         _lowerCAmelCase						=		[self.sep_token_id]
         _lowerCAmelCase						=		[self.question_token_id] + [self.convert_tokens_to_ids('.'       )]
         if self.padding_side == "right":
           # Input is question-then-context
           return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
           # Input is context-then-question
           return cls + token_ids_a + sep + token_ids_a + question_suffix + sep



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			List[int]  ,      _lowerCAmelCase					:			Optional[List[int]] = None       )					->   List[int]:
         _lowerCAmelCase						=		[self.sep_token_id]
         _lowerCAmelCase						=		[self.cls_token_id]
         _lowerCAmelCase						=		[self.question_token_id] + [self.convert_tokens_to_ids('.'       )]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]

         if self.padding_side == "right":
           # Input is question-then-context
           return len(cls + token_ids_a + question_suffix + sep       ) * [0] + len(token_ids_a + sep       ) * [1]
         else:
           # Input is context-then-question
           return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + question_suffix + sep       ) * [1]






       def SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			str  ,      _lowerCAmelCase					:			Optional[str] = None       )					->   Tuple[str]:
         _lowerCAmelCase						=		self._tokenizer.model.save(_lowerCAmelCase  ,      name=_lowerCAmelCase       )
         return tuple(_lowerCAmelCase       )


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
