

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_pegasus import PegasusTokenizer
else:
 _UpperCamelCase 			=		None


_UpperCamelCase 			=		logging.get_logger(__name__)


_UpperCamelCase 			=		"▁"

_UpperCamelCase 			=		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase 			=		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

_UpperCamelCase 			=		{
    "google/pegasus-xsum": 512,
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  VOCAB_FILES_NAMES
 _UpperCamelCase			  =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase			  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase			  =  PegasusTokenizer
 _UpperCamelCase			  =  ["""input_ids""", """attention_mask"""]
 def __init__(							self      ,							A_=None      ,							A_=None      ,							A_="<pad>"      ,							A_="</s>"      ,							A_="<unk>"      ,							A_="<mask_2>"      ,							A_="<mask_1>"      ,							A_=None      ,							A_=103      ,							**A_      ,							)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					offset

       if additional_special_tokens is not None:
             if not isinstance(A_      ,							A_					):
                   raise TypeError(
                       f"""additional_special_tokens should be of type {type(A_					)}, but is"""
                       f""" {type(A_					)}"""					)

             __lowerCAmelCase					:						Union[str, Any]					=					(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f"""<unk_{i}>""" for i in range(len(A_					)      ,							self.offset - 1					)
             ]

             if len(set(A_					)					) != len(A_					):
                   raise ValueError(
                       '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                       f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""					)
             __lowerCAmelCase					:						Union[str, Any]					=					additional_special_tokens_extended
       else:
             __lowerCAmelCase					:						Any					=					[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f"""<unk_{i}>""" for i in range(2      ,							self.offset					)]

       super().__init__(
           A_      ,							tokenizer_file=A_      ,							pad_token=A_      ,							eos_token=A_      ,							unk_token=A_      ,							mask_token=A_      ,							mask_token_sent=A_      ,							offset=A_      ,							additional_special_tokens=A_      ,							**A_      ,							)
       __lowerCAmelCase					:						List[str]					=					vocab_file
       __lowerCAmelCase					:						List[Any]					=					False if not self.vocab_file else True
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					set(self.all_special_ids					)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens					) + 3					)					):
             raise ValueError(
                 '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
                 f""" {len(self.additional_special_tokens					)} additional_special_tokens, but got {all_special_ids}"""					)

       return [1 if x in all_special_ids else 0 for x in seq]
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = False					)					->List[int]:



       '''simple docstring'''





       if already_has_special_tokens:
             return self._special_token_mask(A_					)
       elif token_ids_a is None:
             return self._special_token_mask(A_					) + [1]
       else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=None					)					->List[int]:



       '''simple docstring'''





       if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Tuple[str]:



       '''simple docstring'''





       if not self.can_save_slow_tokenizer:
             raise ValueError(
                 '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                 '''tokenizer.'''					)

       if not os.path.isdir(A_					):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
             return
       __lowerCAmelCase					:						Dict					=					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_					):
             copyfile(self.vocab_file      ,							A_					)

       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


_UpperCamelCase 			=		logging.get_logger(__name__)

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







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							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_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



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

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



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


