'''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


__UpperCAmelCase												=logging.get_logger(__name__)

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




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							="conditional_detr"
     lowerCamelCase  :					Dict							=["past_key_values"]
     lowerCamelCase  :					List[Any]							={
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(			self		:		Optional[Any]  ,    a		:		Optional[int]=True  ,    a		:		str=None  ,    a		:		Union[str, Any]=3  ,    a		:		int=3_00  ,    a		:		int=6  ,    a		:		Optional[int]=20_48  ,    a		:		str=8  ,    a		:		Optional[Any]=6  ,    a		:		Any=20_48  ,    a		:		Dict=8  ,    a		:		List[str]=0.0  ,    a		:		Any=0.0  ,    a		:		List[str]=True  ,    a		:		Tuple="relu"  ,    a		:		int=2_56  ,    a		:		int=0.1  ,    a		:		Optional[int]=0.0  ,    a		:		List[Any]=0.0  ,    a		:		List[str]=0.02  ,    a		:		str=1.0  ,    a		:		List[str]=False  ,    a		:		Any="sine"  ,    a		:		List[str]="resnet50"  ,    a		:		Dict=True  ,    a		:		Dict=False  ,    a		:		int=2  ,    a		:		Optional[int]=5  ,    a		:		Dict=2  ,    a		:		Any=1  ,    a		:		Dict=1  ,    a		:		int=2  ,    a		:		str=5  ,    a		:		str=2  ,    a		:		Union[str, Any]=0.25  ,    **a		:		int  ,    ):





         """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          =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
             elif isinstance(a  ,    a   ):
                 __lowerCamelCase          =  backbone_config.get('''model_type'''   )
                 __lowerCamelCase          =  CONFIG_MAPPING[backbone_model_type]
                 __lowerCamelCase          =  config_class.from_dict(a   )

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






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return self.d_model






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  copy.deepcopy(self.__dict__   )
         if self.backbone_config is not None:
             __lowerCamelCase          =  self.backbone_config.to_dict()
         __lowerCamelCase          =  self.__class__.model_type
         return output




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[str]							=version.parse("1.11"			)






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """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		:		Tuple   ):





         """simple docstring"""



         return 1e-5






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return 12

'''simple docstring'''




import os
import re
import warnings
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_ta import TaTokenizer
else:
  __UpperCAmelCase												=None


__UpperCAmelCase												=logging.get_logger(__name__)

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

__UpperCAmelCase												={
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCAmelCase												={
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Optional[Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Dict							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					int							=["input_ids", "attention_mask"]
     lowerCamelCase  :					Tuple							=TaTokenizer

     lowerCamelCase  :					List[int]							=[]






     def __init__(			self		:		int  ,    a		:		List[str]=None  ,    a		:		List[str]=None  ,    a		:		Dict="</s>"  ,    a		:		Optional[int]="<unk>"  ,    a		:		Any="<pad>"  ,    a		:		Optional[Any]=1_00  ,    a		:		List[Any]=None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         if extra_ids > 0 and additional_special_tokens is None:
             __lowerCamelCase          =  [f"""<extra_id_{i}>""" for i in range(a   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             __lowerCamelCase          =  len(set(filter(lambda a   : bool('''extra_id_''' in str(a   )   )  ,    a   )   )   )
             if extra_tokens != extra_ids:
                 raise ValueError(
                     f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''   )

         super().__init__(
             a  ,    tokenizer_file=a  ,    eos_token=a  ,    unk_token=a  ,    pad_token=a  ,    extra_ids=a  ,    additional_special_tokens=a  ,    **a  ,    )

         __lowerCamelCase          =  vocab_file
         __lowerCamelCase          =  False if not self.vocab_file else True
         __lowerCamelCase          =  extra_ids






     @staticmethod
     def        SCREAMING_SNAKE_CASE__       (			a		:		Optional[int]  ,    a		:		List[str]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             __lowerCamelCase          =  TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
             if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
             elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                     f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                     f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''  ,    a  ,    )

         return max_model_length






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """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          =  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   )
             logger.info(f"""Copy vocab file to {out_vocab_file}"""   )

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
         else:
             __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.eos_token_id]

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return list(
             set(filter(lambda a   : bool(re.search(R'''<extra_id_\d+>'''  ,    a   )   ) is not None  ,    self.additional_special_tokens   )   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         return [self.convert_tokens_to_ids(a   ) for token in self.get_sentinel_tokens()]
