






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__A		:       str  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				int							    =   "table-transformer"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["past_key_values"]
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   {
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(				self     :						Tuple    ,						A     :						Dict=True    ,						A     :						List[str]=None    ,						A     :						str=3    ,						A     :						int=1_00    ,						A     :						Optional[int]=6    ,						A     :						Union[str, Any]=20_48    ,						A     :						Union[str, Any]=8    ,						A     :						Optional[Any]=6    ,						A     :						Tuple=20_48    ,						A     :						List[Any]=8    ,						A     :						str=0.0    ,						A     :						Optional[int]=0.0    ,						A     :						str=True    ,						A     :						Optional[int]="relu"    ,						A     :						List[Any]=2_56    ,						A     :						Union[str, Any]=0.1    ,						A     :						Tuple=0.0    ,						A     :						int=0.0    ,						A     :						Any=0.02    ,						A     :						Optional[Any]=1.0    ,						A     :						List[Any]=False    ,						A     :						List[Any]="sine"    ,						A     :						Tuple="resnet50"    ,						A     :						Optional[Any]=True    ,						A     :						int=False    ,						A     :						List[Any]=1    ,						A     :						Optional[Any]=5    ,						A     :						Union[str, Any]=2    ,						A     :						Optional[int]=1    ,						A     :						Tuple=1    ,						A     :						List[str]=5    ,						A     :						Optional[Any]=2    ,						A     :						str=0.1    ,						**A     :						int    ,						)  ->  Dict:
							if backbone_config is not None and use_timm_backbone:
									raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

							if not use_timm_backbone:
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
											lowercase_ : Dict							       =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
									elif isinstance(A    ,						A   ):
											lowercase_ : Union[str, Any]							       =   backbone_config.get('''model_type'''   )
											lowercase_ : Any							       =   CONFIG_MAPPING[backbone_model_type]
											lowercase_ : List[str]							       =   config_class.from_dict(A   )
									# set timm attributes to None
									lowercase_       ,					lowercase_       ,					lowercase_ : Dict							       =   None, None, None

							lowercase_ : Union[str, Any]							       =   use_timm_backbone
							lowercase_ : Tuple							       =   backbone_config
							lowercase_ : Dict							       =   num_channels
							lowercase_ : List[str]							       =   num_queries
							lowercase_ : Any							       =   d_model
							lowercase_ : Optional[Any]							       =   encoder_ffn_dim
							lowercase_ : Any							       =   encoder_layers
							lowercase_ : Dict							       =   encoder_attention_heads
							lowercase_ : str							       =   decoder_ffn_dim
							lowercase_ : str							       =   decoder_layers
							lowercase_ : int							       =   decoder_attention_heads
							lowercase_ : Optional[int]							       =   dropout
							lowercase_ : Optional[Any]							       =   attention_dropout
							lowercase_ : str							       =   activation_dropout
							lowercase_ : Tuple							       =   activation_function
							lowercase_ : Any							       =   init_std
							lowercase_ : str							       =   init_xavier_std
							lowercase_ : Union[str, Any]							       =   encoder_layerdrop
							lowercase_ : Optional[Any]							       =   decoder_layerdrop
							lowercase_ : Optional[Any]							       =   encoder_layers
							lowercase_ : Tuple							       =   auxiliary_loss
							lowercase_ : Dict							       =   position_embedding_type
							lowercase_ : Optional[Any]							       =   backbone
							lowercase_ : List[str]							       =   use_pretrained_backbone
							lowercase_ : Any							       =   dilation
							# Hungarian matcher
							lowercase_ : Any							       =   class_cost
							lowercase_ : Optional[Any]							       =   bbox_cost
							lowercase_ : int							       =   giou_cost
							# Loss coefficients
							lowercase_ : Union[str, Any]							       =   mask_loss_coefficient
							lowercase_ : Optional[int]							       =   dice_loss_coefficient
							lowercase_ : Optional[Any]							       =   bbox_loss_coefficient
							lowercase_ : Optional[int]							       =   giou_loss_coefficient
							lowercase_ : Union[str, Any]							       =   eos_coefficient
							super().__init__(is_encoder_decoder=A    ,						**A   )




					@property
					def   A  (				self     :						Any   )  ->  int:
							return self.encoder_attention_heads






					@property
					def   A  (				self     :						List[Any]   )  ->  int:
							return self.d_model





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   version.parse("1.11"							)




					@property
					def   A  (				self     :						Dict   )  ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							        ('''pixel_mask''', {0: '''batch'''}),
							    ]   )




					@property
					def   A  (				self     :						List[str]   )  ->  float:
							return 1e-5






					@property
					def   A  (				self     :						int   )  ->  int:
							return 12





import os
import re
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


_UpperCAmelCase					: List[Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: Dict      =       {"vocab_file": "spiece.model"}

_UpperCAmelCase					: Dict      =       {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

_UpperCAmelCase					: int      =       {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       int	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[Any]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       List[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       List[int]	     =				[]



		def __init__( self				,      A_				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="[SEP]"				,      A_="[MASK]"				,      A_="[CLS]"				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else bos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else eos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else unk_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else pad_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else cls_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else sep_token

				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else mask_token

				UpperCamelCase		=     {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=A_				,      eos_token=A_				,      unk_token=A_				,      pad_token=A_				,      sep_token=A_				,      mask_token=A_				,      cls_token=A_				,      sp_model_kwargs=self.sp_model_kwargs				,      **A_				,      )

				UpperCamelCase		=     vocab_file

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(A_       )



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.sp_model.get_piece_size()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab



		def __getstate__( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				return state



		def __setstate__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     d

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

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



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				return self.sp_model.encode(A_				,      out_type=A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.sp_model.piece_to_id(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.sp_model.IdToPiece(A_       )
				return token



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     ''
				UpperCamelCase		=     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(A_       ) + token
								UpperCamelCase		=     True
								UpperCamelCase		=     []
						else:
								current_sub_tokens.append(A_       )
								UpperCamelCase		=     False
				out_string += self.sp_model.decode(A_       )
				return out_string.strip()



		def __UpperCamelCase ( self				,      A_				,      A_ = False				,      A_ = None				,      A_ = True				,      **A_				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.pop('use_source_tokenizer'				,      A_       )

				UpperCamelCase		=     self.convert_ids_to_tokens(A_				,      skip_special_tokens=A_       )

				# 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
				UpperCamelCase		=     []
				UpperCamelCase		=     []
				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(A_       )       )
										UpperCamelCase		=     []
								sub_texts.append(A_       )
						else:
								current_sub_text.append(A_       )
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(A_       )       )

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						UpperCamelCase		=     re.sub(r' (\[(MASK|SEP)\])'				,      r'\1'				,      ' '.join(A_       )       )
				else:
						UpperCamelCase		=     ''.join(A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     os.path.join(
				    A_				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

				return (out_vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				UpperCamelCase		=     [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""simple docstring"""
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=A_				,      token_ids_a=A_				,      already_has_special_tokens=A_       )

				if token_ids_a is None:
						return [1] + ([0] * len(A_       )) + [1]
				return [1] + ([0] * len(A_       )) + [1] + ([0] * len(A_       )) + [1]



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [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]
