
"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__UpperCAmelCase       =  logging.get_logger(__name__)


__UpperCAmelCase       =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
__UpperCAmelCase       =  {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

__UpperCAmelCase       =  {
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :List[str]				 =						VOCAB_FILES_NAMES
			UpperCAmelCase_  :Optional[Any]				 =						PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_  :int				 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_  :Optional[Any]				 =						["input_ids", "attention_mask"]
			UpperCAmelCase_  :int				 =						BartTokenizer






			def __init__(					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A="replace"       ,   __A="<s>"       ,   __A="</s>"       ,   __A="</s>"       ,   __A="<s>"       ,   __A="<unk>"       ,   __A="<pad>"       ,   __A="<mask>"       ,   __A=False       ,   __A=True       ,   **__A       ,   )			->     Any:
						super().__init__(
						    __A       ,   __A       ,   tokenizer_file=__A       ,   errors=__A       ,   bos_token=__A       ,   eos_token=__A       ,   sep_token=__A       ,   cls_token=__A       ,   unk_token=__A       ,   pad_token=__A       ,   mask_token=__A       ,   add_prefix_space=__A       ,   trim_offsets=__A       ,   **__A       ,   )

						lowerCAmelCase_    :Dict			     =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
						if pre_tok_state.get("""add_prefix_space"""       ,   __A       ) != add_prefix_space:
									lowerCAmelCase_    :Optional[Any]			     =  getattr(__A       ,   pre_tok_state.pop("""type"""       )       )
									lowerCAmelCase_    :Optional[int]			     =  add_prefix_space
									lowerCAmelCase_    :Any			     =  pre_tok_class(**__A       )

						lowerCAmelCase_    :int			     =  add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						lowerCAmelCase_    :Dict			     =  """post_processor"""
						lowerCAmelCase_    :Optional[Any]			     =  getattr(self.backend_tokenizer       ,   __A       ,   __A       )
						if tokenizer_component_instance:
									lowerCAmelCase_    :List[str]			     =  json.loads(tokenizer_component_instance.__getstate__()       )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
												lowerCAmelCase_    :str			     =  tuple(state["""sep"""]       )
									if "cls" in state:
												lowerCAmelCase_    :Any			     =  tuple(state["""cls"""]       )

									lowerCAmelCase_    :Dict			     =  False

									if state.get("""add_prefix_space"""       ,   __A       ) != add_prefix_space:
												lowerCAmelCase_    :int			     =  add_prefix_space
												lowerCAmelCase_    :Dict			     =  True

									if state.get("""trim_offsets"""       ,   __A       ) != trim_offsets:
												lowerCAmelCase_    :Tuple			     =  trim_offsets
												lowerCAmelCase_    :str			     =  True

									if changes_to_apply:
												lowerCAmelCase_    :Tuple			     =  getattr(__A       ,   state.pop("""type"""       )       )
												lowerCAmelCase_    :int			     =  component_class(**__A       )
												setattr(self.backend_tokenizer       ,   __A       ,   __A       )






			@property
			def  __lowerCAmelCase (					self       )			->     str:
						if self._mask_token is None:
									if self.verbose:
												logger.error("""Using mask_token, but it is not set yet."""       )
									return None
						return str(self._mask_token       )






			@mask_token.setter
			def  __lowerCAmelCase (					self       ,   __A       )			->     str:
						lowerCAmelCase_    :List[str]			     =  AddedToken(__A       ,   lstrip=__A       ,   rstrip=__A       ) if isinstance(__A       ,   __A       ) else value
						lowerCAmelCase_    :Optional[int]			     =  value






			def  __lowerCAmelCase (					self       ,   *__A       ,   **__A       )			->     BatchEncoding:
						lowerCAmelCase_    :Optional[int]			     =  kwargs.get("""is_split_into_words"""       ,   __A       )

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    """to use it with pretokenized inputs."""       )

						return super()._batch_encode_plus(*__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   *__A       ,   **__A       )			->     BatchEncoding:
						lowerCAmelCase_    :Optional[Any]			     =  kwargs.get("""is_split_into_words"""       ,   __A       )

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    """to use it with pretokenized inputs."""       )

						return super()._encode_plus(*__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       )			->     Tuple[str]:
						lowerCAmelCase_    :Tuple			     =  self._tokenizer.model.save(__A       ,   name=__A       )
						return tuple(__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A=None       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       )			->     List[int]:
						lowerCAmelCase_    :Any			     =  [self.sep_token_id]
						lowerCAmelCase_    :Optional[int]			     =  [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]











"""simple docstring"""
import copy
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:   List[str]								=		logging.get_logger(__name__)

A:   Dict								=		{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						'conditional_detr'
   __lowerCAmelCase  :   Union[str, Any]						=						['past_key_values']
   __lowerCAmelCase  :   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=300      ,     _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=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=1      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=5      ,     _SCREAMING_SNAKE_CASE=2      ,     _SCREAMING_SNAKE_CASE=0.25      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Tuple:





    '''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."""						)
      UpperCAmelCase	:					Tuple	=	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
     elif isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					str	=	backbone_config.get("""model_type"""						)
      UpperCAmelCase	:					int	=	CONFIG_MAPPING[backbone_model_type]
      UpperCAmelCase	:					Union[str, Any]	=	config_class.from_dict(_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.encoder_attention_heads

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.d_model




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	copy.deepcopy(self.__dict__						)
    if self.backbone_config is not None:
     UpperCAmelCase	:					Union[str, Any]	=	self.backbone_config.to_dict()
    UpperCAmelCase	:					Dict	=	self.__class__.model_type
    return output



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						version.parse('1.11' )

   @property
   def 	SCREAMING_SNAKE_CASE     (   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 	SCREAMING_SNAKE_CASE     (   self						)  ->      float:





    '''simple docstring'''




    return 1E-5




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return 12
