
"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


A__   :					Tuple							=   logging.get_logger(__name__)


A__   :					Optional[int]							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
A__   :					Dict							=   {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

A__   :					str							=   {
    'RUCAIBox/mvp': 1_0_2_4,
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_   =		MvpTokenizer
       def __init__(     self ,				A_=None ,				A_=None ,				A_=None ,				A_="replace" ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				A_=False ,				A_=True ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(
             A_ ,				A_ ,				tokenizer_file=A_ ,				errors=A_ ,				bos_token=A_ ,				eos_token=A_ ,				sep_token=A_ ,				cls_token=A_ ,				unk_token=A_ ,				pad_token=A_ ,				mask_token=A_ ,				add_prefix_space=A_ ,				trim_offsets=A_ ,				**A_ ,				)

         _lowercase:			List[str]       =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
         if pre_tok_state.get('''add_prefix_space''' ,				A_       ) != add_prefix_space:
           _lowercase:			str       =     getattr(A_ ,				pre_tok_state.pop('''type'''       )       )
           _lowercase:			int       =     add_prefix_space
           _lowercase:			str       =     pre_tok_class(**A_       )

         _lowercase:			Optional[int]       =     add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         _lowercase:			Optional[int]       =     '''post_processor'''
         _lowercase:			Any       =     getattr(self.backend_tokenizer ,				A_ ,				A_       )
         if tokenizer_component_instance:
           _lowercase:			Any       =     json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             _lowercase:			Any       =     tuple(state['''sep''']       )
           if "cls" in state:
             _lowercase:			Any       =     tuple(state['''cls''']       )

           _lowercase:			List[str]       =     False

           if state.get('''add_prefix_space''' ,				A_       ) != add_prefix_space:
             _lowercase:			str       =     add_prefix_space
             _lowercase:			List[Any]       =     True

           if state.get('''trim_offsets''' ,				A_       ) != trim_offsets:
             _lowercase:			str       =     trim_offsets
             _lowercase:			Optional[Any]       =     True

           if changes_to_apply:
             _lowercase:			List[Any]       =     getattr(A_ ,				state.pop('''type'''       )       )
             _lowercase:			Any       =     component_class(**A_       )
             setattr(self.backend_tokenizer ,				A_ ,				A_       )
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self._mask_token is None:
           if self.verbose:
             logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
         return str(self._mask_token       )
       @mask_token.setter
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Tuple       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else value
         _lowercase:			List[str]       =     value
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     kwargs.get('''is_split_into_words''' ,				A_       )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

         return super()._batch_encode_plus(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			List[str]       =     kwargs.get('''is_split_into_words''' ,				A_       )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

         return super()._encode_plus(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         _lowercase:			str       =     self._tokenizer.model.save(A_ ,				name=A_       )
         return tuple(A_       )
       def 							lowercase_							(     self ,				A_ ,				A_=None       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			List[str]       =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			int       =     [self.sep_token_id]
         _lowercase:			str       =     [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]


"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A__   :					Dict							=   logging.get_logger(__name__)

A__   :					int							=   {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''dpt'''
       def __init__(     self ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.0 ,				A_=0.0 ,				A_=0.02 ,				A_=1E-12 ,				A_=384 ,				A_=16 ,				A_=3 ,				A_=False ,				A_=True ,				A_=[2, 5, 8, 11] ,				A_="project" ,				A_=[4, 2, 1, 0.5] ,				A_=[96, 192, 384, 768] ,				A_=256 ,				A_=-1 ,				A_=False ,				A_=True ,				A_=0.4 ,				A_=255 ,				A_=0.1 ,				A_=[1, 1024, 24, 24] ,				A_=[0, 1] ,				A_=None ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Union[str, Any]       =     hidden_size
         _lowercase:			str       =     is_hybrid

         if self.is_hybrid:
           if backbone_config is None:
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }
             _lowercase:			List[str]       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             _lowercase:			str       =     backbone_config
           else:
             raise ValueError(
                 f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

           _lowercase:			Dict       =     backbone_featmap_shape
           _lowercase:			Optional[int]       =     neck_ignore_stages

           if readout_type != "project":
             raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''       )
         else:
           _lowercase:			Any       =     None
           _lowercase:			str       =     None
           _lowercase:			Optional[Any]       =     []

         _lowercase:			Tuple       =     num_hidden_layers
         _lowercase:			Optional[int]       =     num_attention_heads
         _lowercase:			Any       =     intermediate_size
         _lowercase:			List[Any]       =     hidden_act
         _lowercase:			Union[str, Any]       =     hidden_dropout_prob
         _lowercase:			Tuple       =     attention_probs_dropout_prob
         _lowercase:			Dict       =     initializer_range
         _lowercase:			Dict       =     layer_norm_eps
         _lowercase:			Any       =     image_size
         _lowercase:			Optional[int]       =     patch_size
         _lowercase:			Optional[int]       =     num_channels
         _lowercase:			int       =     qkv_bias
         _lowercase:			List[str]       =     backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
           raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''       )
         _lowercase:			Union[str, Any]       =     readout_type
         _lowercase:			Optional[Any]       =     reassemble_factors
         _lowercase:			List[str]       =     neck_hidden_sizes
         _lowercase:			Tuple       =     fusion_hidden_size
         _lowercase:			int       =     head_in_index
         _lowercase:			Optional[int]       =     use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         _lowercase:			int       =     use_auxiliary_head
         _lowercase:			Dict       =     auxiliary_loss_weight
         _lowercase:			List[Any]       =     semantic_loss_ignore_index
         _lowercase:			Dict       =     semantic_classifier_dropout



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[int]       =     copy.deepcopy(self.__dict__       )

         if output["backbone_config"] is not None:
           _lowercase:			List[str]       =     self.backbone_config.to_dict()

         _lowercase:			List[Any]       =     self.__class__.model_type
         return output
