


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


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

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {"bert_for_seq_generation": 5_12}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		VOCAB_FILES_NAMES
    __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__        =		[]
    __magic_name__        =		['input_ids', 'attention_mask']
    def __init__(	self      , __snake_case      , __snake_case="<s>"      , __snake_case="</s>"      , __snake_case="<unk>"      , __snake_case="<pad>"      , __snake_case="<::::>"      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    {} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=__snake_case      , eos_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , sep_token=__snake_case      , sp_model_kwargs=self.sp_model_kwargs      , **__snake_case      , )

         snake_case					=    vocab_file

         snake_case					=    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.sp_model.get_piece_size()
    def 		a_      (	self   ):
         snake_case					=    {self.convert_ids_to_tokens(__snake_case   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab
    def __getstate__(	self   ):
         snake_case					=    self.__dict__.copy()
         snake_case					=    None
         return state
    def __setstate__(	self      , __snake_case   ):
         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.Load(self.vocab_file   )
    def 		a_      (	self      , __snake_case   ):
         return self.sp_model.encode(__snake_case      , out_type=__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         return self.sp_model.piece_to_id(__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self.sp_model.IdToPiece(__snake_case   )
         return token
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    []
         snake_case					=    ''''''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__snake_case   ) + token
                   snake_case					=    []
              else:
                   current_sub_tokens.append(__snake_case   )
         out_string += self.sp_model.decode(__snake_case   )
         return out_string.strip()




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

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

         return (out_vocab_file,)








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         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(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # 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=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




