




'''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 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				{}

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

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

										snake_case_									=				value.float()

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

					return upgrade


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=None	)		->					Optional[int]:

					'''simple docstring'''

					if config_path is not None:
										snake_case_									=				FlavaConfig.from_pretrained(lowercase_	)
					else:
										snake_case_									=				FlavaConfig()

					snake_case_									=				FlavaForPreTraining(lowercase_	).eval()

					snake_case_									=				convert_dalle_checkpoint(lowercase_,      lowercase_,      save_checkpoint=lowercase_	)

					if os.path.exists(lowercase_	):
										snake_case_									=				torch.load(lowercase_,      map_location='''cpu'''	)
					else:
										snake_case_									=				torch.hub.load_state_dict_from_url(lowercase_,      map_location='''cpu'''	)

					snake_case_									=				upgrade_state_dict(lowercase_,      lowercase_	)
					hf_model.load_state_dict(lowercase_	)
					snake_case_									=				hf_model.state_dict()
					snake_case_									=				count_parameters(lowercase_	)
					snake_case_									=				count_parameters(lowercase_	) + count_parameters(lowercase_	)

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

					hf_model.save_pretrained(lowercase_	)


if __name__ == "__main__":
							a  :				int									=  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')
							a  :				Union[str, Any]									=  parser.parse_args()

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




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


A_       : str         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			'▁'

A_       : int         =			{'vocab_file': 'sentencepiece.bpe.model'}

A_       : int         =			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

A_       : Optional[int]         =			{
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
A_       : Tuple         =			['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__				):


					'''simple docstring'''






					UpperCAmelCase__:   Optional[Any]  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Any  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   Optional[Any]  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   str  							=       ['''input_ids''', '''attention_mask''']

					UpperCAmelCase__:   List[int]  							=       []
					UpperCAmelCase__:   List[int]  							=       []






					def __init__( self    ,       A__    ,       A__="<s>"    ,       A__="</s>"    ,       A__="</s>"    ,       A__="<s>"    ,       A__="<unk>"    ,       A__="<pad>"    ,       A__="<mask>"    ,       A__=None    ,       A__=None    ,       A__=None    ,       A__ = None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						# Mask token behave like a normal word, i.e. include the space before it
						A__							:    Any        =		AddedToken(A__    ,       lstrip=A__    ,       rstrip=A__			) if isinstance(A__    ,       A__			) else mask_token

						A__							:    List[Any]        =		{} if sp_model_kwargs is None else sp_model_kwargs
						A__							:    List[str]        =		legacy_behaviour

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

						A__							:    List[Any]        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(str(A__			)			)
						A__							:    List[str]        =		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
						A__							:    str        =		{"""<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
						A__							:    str        =		1

						A__							:    Optional[int]        =		len(self.sp_model			)
						A__							:    List[Any]        =		{
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A__			)
						}
						A__							:    Tuple        =		{v: k for k, v in self.lang_code_to_id.items()}
						A__							:    Dict        =		len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset

						self.fairseq_tokens_to_ids.update(self.lang_code_to_id			)
						A__							:    Tuple        =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
						A__							:    int        =		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]			)

						A__							:    int        =		src_lang if src_lang is not None else """eng_Latn"""
						A__							:    str        =		self.lang_code_to_id[self._src_lang]
						A__							:    Union[str, Any]        =		tgt_lang
						self.set_src_lang_special_tokens(self._src_lang			)






					def __getstate__( self			):
						A__							:    Tuple        =		self.__dict__.copy()
						A__							:    List[Any]        =		None
						A__							:    Tuple        =		self.sp_model.serialized_model_proto()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    str        =		d

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

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






					@property
					def   __A			( self			):
						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			):
						return self._src_lang






					@src_lang.setter
					def   __A			( self    ,       A__			):
						A__							:    str        =		new_src_lang
						self.set_src_lang_special_tokens(self._src_lang			)






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

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

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






					def   __A			( self    ,       A__    ,       A__ = None			):
						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    ,       A__    ,       A__ = None			):
						A__							:    Dict        =		[self.sep_token_id]
						A__							:    Dict        =		[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   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       **A__			):
						if src_lang is None or tgt_lang is None:
							raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
						A__							:    Optional[int]        =		src_lang
						A__							:    List[Any]        =		self(A__    ,       add_special_tokens=A__    ,       return_tensors=A__    ,       **A__			)
						A__							:    Optional[int]        =		self.convert_tokens_to_ids(A__			)
						A__							:    Optional[int]        =		tgt_lang_id
						return inputs






					def   __A			( self			):
						A__							:    List[str]        =		{self.convert_ids_to_tokens(A__			): i for i in range(self.vocab_size			)}
						vocab.update(self.added_tokens_encoder			)
						return vocab






					def   __A			( self    ,       A__			):
						return self.sp_model.encode(A__    ,       out_type=A__			)






					def   __A			( self    ,       A__			):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						A__							:    List[str]        =		self.sp_model.PieceToId(A__			)

						# 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    ,       A__			):
						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    ,       A__			):
						A__							:    Optional[Any]        =		"""""".join(A__			).replace(A__    ,       """ """			).strip()
						return out_string






					def   __A			( self    ,       A__    ,       A__ = None			):
						if not os.path.isdir(A__			):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
							return
						A__							:    Any        =		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__			) and os.path.isfile(self.vocab_file			):
							copyfile(self.vocab_file    ,       A__			)
						elif not os.path.isfile(self.vocab_file			):
							with open(A__    ,       """wb"""			) as fi:
								A__							:    str        =		self.sp_model.serialized_model_proto()
								fi.write(A__			)

						return (out_vocab_file,)






					def   __A			( self    ,       A__    ,       A__ = "eng_Latn"    ,       A__ = None    ,       A__ = "fra_Latn"    ,       **A__    ,       ):
						A__							:    Any        =		src_lang
						A__							:    List[Any]        =		tgt_lang
						return super().prepare_seqaseq_batch(A__    ,       A__    ,       **A__			)






					def   __A			( self			):
						return self.set_src_lang_special_tokens(self.src_lang			)






					def   __A			( self			):
						return self.set_tgt_lang_special_tokens(self.tgt_lang			)






					def   __A			( self    ,       A__			):
						A__							:    List[str]        =		self.lang_code_to_id[src_lang]
						if self.legacy_behaviour:
							A__							:    Dict        =		[]
							A__							:    str        =		[self.eos_token_id, self.cur_lang_code]
						else:
							A__							:    List[str]        =		[self.cur_lang_code]
							A__							:    Optional[Any]        =		[self.eos_token_id]






					def   __A			( self    ,       A__			):
						A__							:    Union[str, Any]        =		self.lang_code_to_id[lang]
						if self.legacy_behaviour:
							A__							:    Union[str, Any]        =		[]
							A__							:    int        =		[self.eos_token_id, self.cur_lang_code]
						else:
							A__							:    Dict        =		[self.cur_lang_code]
							A__							:    str        =		[self.eos_token_id]



