

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


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

lowerCAmelCase     :   str										=  '▁'

lowerCAmelCase     :   str										=  {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase     :   List[str]										=  {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCAmelCase     :   Union[str, Any]										=  {
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
lowerCAmelCase     :   Union[str, Any]										=  ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Any       =  VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :      str       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :      Tuple       =  ['''input_ids''', '''attention_mask''']

  SCREAMING_SNAKE_CASE    :      List[int]       =  []
  SCREAMING_SNAKE_CASE    :      List[int]       =  []

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE="<s>" ,					_SCREAMING_SNAKE_CASE="</s>" ,					_SCREAMING_SNAKE_CASE="</s>" ,					_SCREAMING_SNAKE_CASE="<s>" ,					_SCREAMING_SNAKE_CASE="<unk>" ,					_SCREAMING_SNAKE_CASE="<pad>" ,					_SCREAMING_SNAKE_CASE="<mask>" ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     AddedToken(_SCREAMING_SNAKE_CASE ,					lstrip=_SCREAMING_SNAKE_CASE ,					rstrip=_SCREAMING_SNAKE_CASE					) if isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) else mask_token

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {} if sp_model_kwargs is None else sp_model_kwargs
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     legacy_behaviour

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE ,					eos_token=_SCREAMING_SNAKE_CASE ,					unk_token=_SCREAMING_SNAKE_CASE ,					sep_token=_SCREAMING_SNAKE_CASE ,					cls_token=_SCREAMING_SNAKE_CASE ,					pad_token=_SCREAMING_SNAKE_CASE ,					mask_token=_SCREAMING_SNAKE_CASE ,					tokenizer_file=_SCREAMING_SNAKE_CASE ,					src_lang=_SCREAMING_SNAKE_CASE ,					tgt_lang=_SCREAMING_SNAKE_CASE ,					additional_special_tokens=_SCREAMING_SNAKE_CASE ,					sp_model_kwargs=self.sp_model_kwargs ,					legacy_behaviour=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE ,					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(str(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
        # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

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

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

        SCREAMING_SNAKE_CASE_				:  int           =     len(self.sp_model					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE					)
        }
        SCREAMING_SNAKE_CASE_				:  Any           =     {v: k for k, v in self.lang_code_to_id.items()}
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        SCREAMING_SNAKE_CASE_				:  str           =     list(self.lang_code_to_id.keys()					)

        if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              self._additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in self._additional_special_tokens]					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     src_lang if src_lang is not None else 'eng_Latn'
        SCREAMING_SNAKE_CASE_				:  int           =     self.lang_code_to_id[self._src_lang]
        SCREAMING_SNAKE_CASE_				:  Any           =     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang					)

  def __getstate__(				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.__dict__.copy()
        SCREAMING_SNAKE_CASE_				:  str           =     None
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.sp_model.serialized_model_proto()
        return state

  def __setstate__(				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     d

        # for backward compatibility
        if not hasattr(self ,					'sp_model_kwargs'					):
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {}

        SCREAMING_SNAKE_CASE_				:  List[str]           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self._src_lang

  @src_lang.setter
  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = False					):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [1] * len(self.prefix_tokens					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [1] * len(self.suffix_tokens					)
        if token_ids_a is None:
              return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE					)) + suffix_ones
        return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE					)) + ([0] * len(_SCREAMING_SNAKE_CASE					)) + suffix_ones

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     [self.sep_token_id]
        SCREAMING_SNAKE_CASE_				:  Any           =     [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]

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if src_lang is None or tgt_lang is None:
              raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
        SCREAMING_SNAKE_CASE_				:  Any           =     src_lang
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self(_SCREAMING_SNAKE_CASE ,					add_special_tokens=_SCREAMING_SNAKE_CASE ,					return_tensors=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     tgt_lang_id
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,					out_type=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        SCREAMING_SNAKE_CASE_				:  str           =     self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE					)

        # 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 ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        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 ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ''.join(_SCREAMING_SNAKE_CASE					).replace(_SCREAMING_SNAKE_CASE ,					' '					).strip()
        return out_string

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





        """simple docstring"""







        if not os.path.isdir(_SCREAMING_SNAKE_CASE					):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
              return
        SCREAMING_SNAKE_CASE_				:  Dict           =     os.path.join(
            _SCREAMING_SNAKE_CASE ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

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

        return (out_vocab_file,)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = "eng_Latn" ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = "fra_Latn" ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     src_lang
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tgt_lang
        return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.set_src_lang_special_tokens(self.src_lang					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.set_tgt_lang_special_tokens(self.tgt_lang					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.lang_code_to_id[src_lang]
        if self.legacy_behaviour:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     []
              SCREAMING_SNAKE_CASE_				:  int           =     [self.eos_token_id, self.cur_lang_code]
        else:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [self.cur_lang_code]
              SCREAMING_SNAKE_CASE_				:  Dict           =     [self.eos_token_id]



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.lang_code_to_id[lang]
        if self.legacy_behaviour:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     []
              SCREAMING_SNAKE_CASE_				:  List[str]           =     [self.eos_token_id, self.cur_lang_code]
        else:
              SCREAMING_SNAKE_CASE_				:  str           =     [self.cur_lang_code]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self.eos_token_id]









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCAmelCase     :   str						     =  {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  '''git_vision_model'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=224 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE="quick_gelu" ,					_SCREAMING_SNAKE_CASE=1e-5 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Any           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Dict           =     patch_size
        SCREAMING_SNAKE_CASE_				:  Any           =     image_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  Any           =     attention_dropout
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  Tuple           =     hidden_act



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  str           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get('model_type'					) == "git":
              SCREAMING_SNAKE_CASE_				:  str           =     config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls ,					'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  '''git'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3_0522 ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=1024 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1e-12 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE="absolute" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=101 ,					_SCREAMING_SNAKE_CASE=102 ,					_SCREAMING_SNAKE_CASE=None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE ,					eos_token_id=_SCREAMING_SNAKE_CASE ,					pad_token_id=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        if vision_config is None:
              SCREAMING_SNAKE_CASE_				:  Any           =     {}
              logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     GitVisionConfig(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  Any           =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_				:  Tuple           =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_				:  int           =     max_position_embeddings
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  int           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  List[str]           =     position_embedding_type
        SCREAMING_SNAKE_CASE_				:  List[str]           =     use_cache
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tie_word_embeddings
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     num_image_with_embedding

        SCREAMING_SNAKE_CASE_				:  Tuple           =     bos_token_id
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     eos_token_id



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  int           =     self.__class__.model_type
        return output






