





import copy

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


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

lowercase    : List[str]    	=  {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class  __A(						__UpperCAmelCase ):
     __A	  =  "deta"
     __A	  =  {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(      self,       A=None,       A=900,       A=2048,       A=6,       A=2048,       A=8,       A=6,       A=1024,       A=8,       A=0.0,       A=True,       A="relu",       A=256,       A=0.1,       A=0.0,       A=0.0,       A=0.02,       A=1.0,       A=True,       A=False,       A="sine",       A=5,       A=4,       A=4,       A=True,       A=300,       A=True,       A=True,       A=1,       A=5,       A=2,       A=1,       A=1,       A=5,       A=2,       A=0.1,       A=0.25,       **A,       ):





            """simple docstring"""

            if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                   _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']      )
            else:
                   if isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.pop('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )

            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						max_position_embeddings
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            # deformable attributes
            _UpperCamelCase              =						num_feature_levels
            _UpperCamelCase              =						encoder_n_points
            _UpperCamelCase              =						decoder_n_points
            _UpperCamelCase              =						two_stage
            _UpperCamelCase              =						two_stage_num_proposals
            _UpperCamelCase              =						with_box_refine
            _UpperCamelCase              =						assign_first_stage
            if two_stage is True and with_box_refine is False:
                   raise ValueError('''If two_stage is True, with_box_refine must be True.'''      )
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            _UpperCamelCase              =						focal_alpha
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output













import json
import os
from typing import Optional, Tuple

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


lowercase    : str    	=  logging.get_logger(__name__)

lowercase    : Tuple    	=  {"""vocab_file""": """vocab.json"""}

lowercase    : int    	=  {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

lowercase    : int    	=  {"""mgp-str""": 27}

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(      self,       A,       A="[GO]",       A="[GO]",       A="[s]",       A="[GO]",       **A      ):





            """simple docstring"""

            super().__init__(
                unk_token=A,       bos_token=A,       eos_token=A,       pad_token=A,       **A,       )

            with open(A,       encoding='''utf-8'''      ) as vocab_handle:
                   _UpperCamelCase              =						json.load(A      )
            _UpperCamelCase              =						{v: k for k, v in self.vocab.items()}

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return len(self.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return dict(self.vocab,       **self.added_tokens_encoder      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[]
            for s in text:
                   char_tokens.extend(A      )
            return char_tokens

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.vocab.get(A,       self.vocab.get(self.unk_token      )      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.decoder.get(A      )



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

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

            with open(A,       '''w''',       encoding='''utf-8'''      ) as f:
                   f.write(json.dumps(self.vocab,       indent=2,       sort_keys=A,       ensure_ascii=A      ) + '''\n'''      )

            return (vocab_file,)






