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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE							:Tuple							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:int							 =				{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

SCREAMING_SNAKE_CASE							:Dict							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Any							,A					: str							,A					: str="<s>"							,A					: str="</s>"							,A					: Any="</s>"							,A					: List[str]="<s>"							,A					: Optional[Any]="<unk>"							,A					: Tuple="<pad>"							,A					: Optional[Any]="<mask>"							,A					: Optional[Dict[str, Any]] = None							,**A					: Any							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= vocab_file
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )

					__A   			= {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					__A   			= len(self.sp_model ) - 1
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[int]							,A					: Optional[List[int]] = None ):

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: List[str]							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = 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 )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: List[str]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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]






	@property
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					return len(self.sp_model )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: str							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: int ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					__A   			= self.sp_model.PieceToId(A )

					return spm_id if spm_id else self.unk_token_id






	def 				UpperCamelCase_  (       self					: Dict							,A					: Dict ):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(A )






	def 				UpperCamelCase_  (       self					: Any							,A					: Optional[Any] ):
					__A   			= []
					__A   			= ""
					__A   			= 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(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: int ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: str							,A					: Dict ):
					__A   			= d

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

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







	def 				UpperCamelCase_  (       self					: Tuple							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= 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   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)


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			(	a_						)		->	List[str]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

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

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

								__A   			= value.float()

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

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

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

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				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')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

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