"""simple docstring"""






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					: List[str]        =logging.get_logger(__name__)

__lowerCAmelCase					: Optional[Any]        ="""▁"""

__lowerCAmelCase					: int        ={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase					: int        ={
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__lowerCAmelCase					: List[Any]        ={
    """facebook/mbart-large-50-one-to-many-mmt""": 1_0_2_4,
}

# fmt: off
__lowerCAmelCase					: Optional[Any]        =["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[Any]													=			VOCAB_FILES_NAMES
      snake_case__    :     Optional[int]													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     List[Any]													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     List[str]													=			['input_ids', 'attention_mask']

      snake_case__    :     List[int]													=			[]
      snake_case__    :     List[int]													=			[]

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







            """simple docstring"""



            lowercase														=			AddedToken(__lowerCAmelCase	, lstrip=__lowerCAmelCase	, rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase	, __lowerCAmelCase		) else mask_token

            lowercase														=			{} if sp_model_kwargs is None else sp_model_kwargs

            lowercase														=			kwargs.get("""additional_special_tokens"""	, []		)
            kwargs["additional_special_tokens"] += [
                code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
            ]

            super().__init__(
                src_lang=__lowerCAmelCase	, tgt_lang=__lowerCAmelCase	, eos_token=__lowerCAmelCase	, unk_token=__lowerCAmelCase	, sep_token=__lowerCAmelCase	, cls_token=__lowerCAmelCase	, pad_token=__lowerCAmelCase	, mask_token=__lowerCAmelCase	, sp_model_kwargs=self.sp_model_kwargs	, **__lowerCAmelCase	, )

            lowercase														=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(__lowerCAmelCase		)		)
            lowercase														=			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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
            # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

            # Mimic fairseq token-to-id alignment for the first 4 token
            lowercase														=			{"""<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
            lowercase														=			1

            lowercase														=			len(self.sp_model		)
            lowercase														=			{
                code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__lowerCAmelCase		)
            }
            lowercase														=			{v: k for k, v in self.lang_code_to_id.items()}
            lowercase														=			len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

            self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
            lowercase														=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

            lowercase														=			src_lang if src_lang is not None else """en_XX"""
            lowercase														=			self.lang_code_to_id[self._src_lang]
            lowercase														=			tgt_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      @property
      def        A__ (		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        A__ (		self		):







            """simple docstring"""



            return self._src_lang

      @src_lang.setter
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			new_src_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      def __getstate__(		self		):







            """simple docstring"""



            lowercase														=			self.__dict__.copy()
            lowercase														=			None
            return state

      def __setstate__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			d

            # for backward compatibility
            if not hasattr(self	, """sp_model_kwargs"""		):
                  lowercase														=			{}

            lowercase														=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(self.vocab_file		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			{self.convert_ids_to_tokens(__lowerCAmelCase		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowercase														=			self.sp_model.PieceToId(__lowerCAmelCase		)

            # 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        A__ (		self	, __lowerCAmelCase		):







            """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        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			[]
            lowercase														=			""""""
            lowercase														=			False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(__lowerCAmelCase		) + token
                        lowercase														=			True
                        lowercase														=			[]
                  else:
                        current_sub_tokens.append(__lowerCAmelCase		)
                        lowercase														=			False
            out_string += self.sp_model.decode(__lowerCAmelCase		)
            return out_string.strip()

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



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

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

            return (out_vocab_file,)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = False		):







            """simple docstring"""



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

            lowercase														=			[1] * len(self.prefix_tokens		)
            lowercase														=			[1] * len(self.suffix_tokens		)
            if token_ids_a is None:
                  return prefix_ones + ([0] * len(__lowerCAmelCase		)) + suffix_ones
            return prefix_ones + ([0] * len(__lowerCAmelCase		)) + ([0] * len(__lowerCAmelCase		)) + suffix_ones

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = 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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		):







            """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"""		)
            lowercase														=			src_lang
            lowercase														=			self(__lowerCAmelCase	, add_special_tokens=__lowerCAmelCase	, return_tensors=__lowerCAmelCase	, **__lowerCAmelCase		)
            lowercase														=			self.convert_tokens_to_ids(__lowerCAmelCase		)
            lowercase														=			tgt_lang_id
            return inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = "en_XX"	, __lowerCAmelCase = None	, __lowerCAmelCase = "ro_RO"	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			src_lang
            lowercase														=			tgt_lang
            return super().prepare_seqaseq_batch(__lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            return self.set_src_lang_special_tokens(self.src_lang		)

      def        A__ (		self		):







            """simple docstring"""



            return self.set_tgt_lang_special_tokens(self.tgt_lang		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.lang_code_to_id[src_lang]
            lowercase														=			[self.cur_lang_code_id]
            lowercase														=			[self.eos_token_id]







      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.lang_code_to_id[tgt_lang]
            lowercase														=			[self.cur_lang_code_id]
            lowercase														=			[self.eos_token_id]




"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase														=			key.replace("""heads.cmd.mim_head.cls.predictions"""      ,	"""mmm_image_head"""	)
            lowercase														=			key.replace("""heads.cmd.mlm_head.cls.predictions"""      ,	"""mmm_text_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.cls"""      ,	"""itm_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.pooler"""      ,	"""itm_head.pooler"""	)
            lowercase														=			key.replace("""heads.cmd.clip_head.logit_scale"""      ,	"""flava.logit_scale"""	)
            lowercase														=			key.replace("""heads.fairseq_mlm.cls.predictions"""      ,	"""mlm_head"""	)
            lowercase														=			key.replace("""heads.imagenet.mim_head.cls.predictions"""      ,	"""mim_head"""	)
            lowercase														=			key.replace("""mm_text_projection"""      ,	"""flava.text_to_mm_projection"""	)
            lowercase														=			key.replace("""mm_image_projection"""      ,	"""flava.image_to_mm_projection"""	)
            lowercase														=			key.replace("""image_encoder.module"""      ,	"""flava.image_model"""	)
            lowercase														=			key.replace("""text_encoder.module"""      ,	"""flava.text_model"""	)
            lowercase														=			key.replace("""mm_encoder.module.encoder.cls_token"""      ,	"""flava.multimodal_model.cls_token"""	)
            lowercase														=			key.replace("""mm_encoder.module"""      ,	"""flava.multimodal_model"""	)
            lowercase														=			key.replace("""text_projection"""      ,	"""flava.text_projection"""	)
            lowercase														=			key.replace("""image_projection"""      ,	"""flava.image_projection"""	)

            lowercase														=			value.float()

      for key, value in codebook_state_dict.items():
            lowercase														=			value

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

      if os.path.exists(lowerCAmelCase__	):
            lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      else:
            lowercase														=			torch.hub.load_state_dict_from_url(lowerCAmelCase__      ,	map_location="""cpu"""	)

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

      assert torch.allclose(lowerCAmelCase__      ,	lowerCAmelCase__      ,	atol=1e-3	)

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =argparse.ArgumentParser()
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
       parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)



