

import os
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


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

lowerCAmelCase     :   Union[str, Any]						     =  '▁'

lowerCAmelCase     :   Union[str, Any]						     =  {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase     :   str						     =  {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase     :   List[Any]						     =  {
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Tuple       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      Tuple       =  PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      List[str]       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  ['''input_ids''', '''attention_mask''']

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE="<s>" ,					_SCREAMING_SNAKE_CASE="</s>" ,					_SCREAMING_SNAKE_CASE="</s>" ,					_SCREAMING_SNAKE_CASE="<s>" ,					_SCREAMING_SNAKE_CASE="<unk>" ,					_SCREAMING_SNAKE_CASE="<pad>" ,					_SCREAMING_SNAKE_CASE="<mask>" ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     AddedToken(_SCREAMING_SNAKE_CASE ,					lstrip=_SCREAMING_SNAKE_CASE ,					rstrip=_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) else mask_token

        SCREAMING_SNAKE_CASE_				:  Dict           =     {} if sp_model_kwargs is None else sp_model_kwargs

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

        SCREAMING_SNAKE_CASE_				:  int           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(str(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        SCREAMING_SNAKE_CASE_				:  List[str]           =     {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        SCREAMING_SNAKE_CASE_				:  Dict           =     1

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     len(self.sp_model					) + self.fairseq_offset
        SCREAMING_SNAKE_CASE_				:  Any           =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}

  def __getstate__(				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.__dict__.copy()
        SCREAMING_SNAKE_CASE_				:  Dict           =     None
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.sp_model.serialized_model_proto()
        return state

  def __setstate__(				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     d

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

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        SCREAMING_SNAKE_CASE_				:  Any           =     [self.cls_token_id]
        SCREAMING_SNAKE_CASE_				:  Dict           =     [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = False					):





        """simple docstring"""







        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_SCREAMING_SNAKE_CASE ,					token_ids_a=_SCREAMING_SNAKE_CASE ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE					)

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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     [self.sep_token_id]
        SCREAMING_SNAKE_CASE_				:  Any           =     [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]

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return len(self.sp_model					) + self.fairseq_offset + 1  # Add the <mask> token

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,					out_type=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE					)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     ''.join(_SCREAMING_SNAKE_CASE					).replace(_SCREAMING_SNAKE_CASE ,					' '					).strip()
        return out_string



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        if not os.path.isdir(_SCREAMING_SNAKE_CASE					):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
              return
        SCREAMING_SNAKE_CASE_				:  Any           =     os.path.join(
            _SCREAMING_SNAKE_CASE ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

        if os.path.abspath(self.vocab_file					) != os.path.abspath(_SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
              copyfile(self.vocab_file ,					_SCREAMING_SNAKE_CASE					)
        elif not os.path.isfile(self.vocab_file					):
              with open(_SCREAMING_SNAKE_CASE ,					'wb'					) as fi:
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE					)

        return (out_vocab_file,)









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


lowerCAmelCase     :   Dict						     =  logging.get_logger(__name__)

lowerCAmelCase     :   str						     =  {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  '''table-transformer'''
  SCREAMING_SNAKE_CASE    :      Any       =  ['''past_key_values''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  {
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=100 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=256 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1.0 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="sine" ,					_SCREAMING_SNAKE_CASE="resnet50" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        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.'					)
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     CONFIG_MAPPING['resnet'](out_features=['stage4']					)
              elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone_config.get('model_type'					)
                    SCREAMING_SNAKE_CASE_				:  str           =     CONFIG_MAPPING[backbone_model_type]
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     config_class.from_dict(_SCREAMING_SNAKE_CASE					)
              # set timm attributes to None
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None, None, None

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

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder_attention_heads



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.d_model

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  version.parse('''1.11''')

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-5



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 12






