'''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


__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Any    =			{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class       __SCREAMING_SNAKE_CASE	(						__lowerCAmelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   '''table-transformer'''
     UpperCAmelCase__       :						int      =   ['''past_key_values''']
     UpperCAmelCase__       :						Dict      =   {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }

     def __init__(					self						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=3						,   lowerCamelCase=100						,   lowerCamelCase=6						,   lowerCamelCase=2_048						,   lowerCamelCase=8						,   lowerCamelCase=6						,   lowerCamelCase=2_048						,   lowerCamelCase=8						,   lowerCamelCase=0.0						,   lowerCamelCase=0.0						,   lowerCamelCase=True						,   lowerCamelCase="relu"						,   lowerCamelCase=256						,   lowerCamelCase=0.1						,   lowerCamelCase=0.0						,   lowerCamelCase=0.0						,   lowerCamelCase=0.02						,   lowerCamelCase=1.0						,   lowerCamelCase=False						,   lowerCamelCase="sine"						,   lowerCamelCase="resnet50"						,   lowerCamelCase=True						,   lowerCamelCase=False						,   lowerCamelCase=1						,   lowerCamelCase=5						,   lowerCamelCase=2						,   lowerCamelCase=1						,   lowerCamelCase=1						,   lowerCamelCase=5						,   lowerCamelCase=2						,   lowerCamelCase=0.1						,   **lowerCamelCase						,   ):
       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."	)
           _snake_case     		=				CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
         elif isinstance(lowerCAmelCase_						,   lowerCAmelCase_	):
           _snake_case     		=				backbone_config.get("model_type"	)
           _snake_case     		=				CONFIG_MAPPING[backbone_model_type]
           _snake_case     		=				config_class.from_dict(lowerCAmelCase_	)
         # set timm attributes to None
         _snake_case		,		_snake_case		,		_snake_case     		=				None, None, None

       _snake_case     		=				use_timm_backbone
       _snake_case     		=				backbone_config
       _snake_case     		=				num_channels
       _snake_case     		=				num_queries
       _snake_case     		=				d_model
       _snake_case     		=				encoder_ffn_dim
       _snake_case     		=				encoder_layers
       _snake_case     		=				encoder_attention_heads
       _snake_case     		=				decoder_ffn_dim
       _snake_case     		=				decoder_layers
       _snake_case     		=				decoder_attention_heads
       _snake_case     		=				dropout
       _snake_case     		=				attention_dropout
       _snake_case     		=				activation_dropout
       _snake_case     		=				activation_function
       _snake_case     		=				init_std
       _snake_case     		=				init_xavier_std
       _snake_case     		=				encoder_layerdrop
       _snake_case     		=				decoder_layerdrop
       _snake_case     		=				encoder_layers
       _snake_case     		=				auxiliary_loss
       _snake_case     		=				position_embedding_type
       _snake_case     		=				backbone
       _snake_case     		=				use_pretrained_backbone
       _snake_case     		=				dilation
       # Hungarian matcher
       _snake_case     		=				class_cost
       _snake_case     		=				bbox_cost
       _snake_case     		=				giou_cost
       # Loss coefficients
       _snake_case     		=				mask_loss_coefficient
       _snake_case     		=				dice_loss_coefficient
       _snake_case     		=				bbox_loss_coefficient
       _snake_case     		=				giou_loss_coefficient
       _snake_case     		=				eos_coefficient
       super().__init__(is_encoder_decoder=lowerCAmelCase_						,   **lowerCAmelCase_	)

     @property
     def 	UpperCamelCase(					self	):
       return self.encoder_attention_heads






     @property
     def 	UpperCamelCase(					self	):
       return self.d_model







class       __SCREAMING_SNAKE_CASE	(						__lowerCAmelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ("pixel_mask", {0: "batch"}),
           ]	)

     @property
     def 	UpperCamelCase(					self	):
       return 1e-5






     @property
     def 	UpperCamelCase(					self	):
       return 12









'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)

__magic_name__   :						str    =			"""▁"""

__magic_name__   :						int    =			{"""vocab_file""": """sentencepiece.bpe.model"""}

__magic_name__   :						List[str]    =			{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

__magic_name__   :						Union[str, Any]    =			{
    """facebook/xglm-564M""": 2_048,
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						str      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Any      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :						Any      =   ['''input_ids''', '''attention_mask''']

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				{} if sp_model_kwargs is None else sp_model_kwargs

       # Compatibility with the original tokenizer
       _snake_case     		=				7
       _snake_case     		=				[F'''<madeupword{i}>''' for i in range(self.num_madeup_words	)]

       _snake_case     		=				kwargs.get("additional_special_tokens"						,   []	)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]

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

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(lowerCamelCase	)	)
       _snake_case     		=				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'

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

       # Mimic fairseq token-to-id alignment for the first 4 token
       _snake_case     		=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

       _snake_case     		=				len(self.sp_model	)
       _snake_case     		=				{F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
       self.fairseq_tokens_to_ids.update(lowerCamelCase	)

       _snake_case     		=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(					self	):
       _snake_case     		=				self.__dict__.copy()
       _snake_case     		=				None
       _snake_case     		=				self.sp_model.serialized_model_proto()
       return state

     def __setstate__(					self						,   lowerCamelCase	):
       _snake_case     		=				d

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

       _snake_case     		=				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):

       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       _snake_case     		=				[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       _snake_case     		=				[self.sep_token_id]

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

     @property
     def 	UpperCamelCase(					self	):
       return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words

     def 	UpperCamelCase(					self	):
       _snake_case     		=				{self.convert_ids_to_tokens(lowerCamelCase	): i for i in range(self.vocab_size	)}
       vocab.update(self.added_tokens_encoder	)
       return vocab

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.sp_model.encode(lowerCamelCase						,   out_type=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       _snake_case     		=				self.sp_model.PieceToId(lowerCamelCase	)

       # 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						,   lowerCamelCase	):
       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						,   lowerCamelCase	):
       _snake_case     		=				"".join(lowerCamelCase	).replace(lowerCamelCase						,   " "	).strip()
       return out_string






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

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

       return (out_vocab_file,)


