





"""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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->Optional[Any]:






	'''simple docstring'''






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    List[str]					) ->Any:






	'''simple docstring'''






	a      :		str	=				{}

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

		a      :		int	=				key.replace("heads.cmd.mim_head.cls.predictions"    ,       "mmm_image_head"					)
		a      :		Optional[Any]	=				key.replace("heads.cmd.mlm_head.cls.predictions"    ,       "mmm_text_head"					)
		a      :		Union[str, Any]	=				key.replace("heads.cmd.itm_head.cls"    ,       "itm_head"					)
		a      :		str	=				key.replace("heads.cmd.itm_head.pooler"    ,       "itm_head.pooler"					)
		a      :		Union[str, Any]	=				key.replace("heads.cmd.clip_head.logit_scale"    ,       "flava.logit_scale"					)
		a      :		Optional[Any]	=				key.replace("heads.fairseq_mlm.cls.predictions"    ,       "mlm_head"					)
		a      :		Optional[Any]	=				key.replace("heads.imagenet.mim_head.cls.predictions"    ,       "mim_head"					)
		a      :		str	=				key.replace("mm_text_projection"    ,       "flava.text_to_mm_projection"					)
		a      :		List[str]	=				key.replace("mm_image_projection"    ,       "flava.image_to_mm_projection"					)
		a      :		Optional[Any]	=				key.replace("image_encoder.module"    ,       "flava.image_model"					)
		a      :		Union[str, Any]	=				key.replace("text_encoder.module"    ,       "flava.text_model"					)
		a      :		Tuple	=				key.replace("mm_encoder.module.encoder.cls_token"    ,       "flava.multimodal_model.cls_token"					)
		a      :		List[str]	=				key.replace("mm_encoder.module"    ,       "flava.multimodal_model"					)
		a      :		Optional[int]	=				key.replace("text_projection"    ,       "flava.text_projection"					)
		a      :		Any	=				key.replace("image_projection"    ,       "flava.image_projection"					)

		a      :		int	=				value.float()

	for key, value in codebook_state_dict.items():
		a      :		Union[str, Any]	=				value

	return upgrade






@torch.no_grad()
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    Dict    ,       _lowercase						:    Union[str, Any]    ,       _lowercase						:    int=None					) ->Dict:






	'''simple docstring'''






	if config_path is not None:
		a      :		List[Any]	=				FlavaConfig.from_pretrained(_lowercase					)
	else:
		a      :		List[Any]	=				FlavaConfig()

	a      :		str	=				FlavaForPreTraining(_lowercase					).eval()

	a      :		Optional[Any]	=				convert_dalle_checkpoint(_lowercase    ,       _lowercase    ,       save_checkpoint=_lowercase					)

	if os.path.exists(_lowercase					):
		a      :		Union[str, Any]	=				torch.load(_lowercase    ,       map_location="cpu"					)
	else:
		a      :		List[Any]	=				torch.hub.load_state_dict_from_url(_lowercase    ,       map_location="cpu"					)

	a      :		Dict	=				upgrade_state_dict(_lowercase    ,       _lowercase					)
	hf_model.load_state_dict(_lowercase					)
	a      :		Dict	=				hf_model.state_dict()
	a      :		List[Any]	=				count_parameters(_lowercase					)
	a      :		str	=				count_parameters(_lowercase					) + count_parameters(_lowercase					)

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

	hf_model.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: 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''')
		a						: Tuple     	=			parser.parse_args()

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









"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

a						: List[Any]     	=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a						: str     	=			{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

a						: Tuple     	=			{'''allegro/herbert-base-cased''': 514}
a						: Optional[int]     	=			{}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					Dict      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Dict      					=PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					Optional[Any]      					=HerbertTokenizer






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__="</s>"      ,					**lowerCAmelCase__      ,					)	-> Optional[int]:
								super().__init__(
								    lowerCAmelCase__      ,					lowerCAmelCase__      ,					tokenizer_file=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep

								return cls + token_ids_a + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

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






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Dict	=				[self.sep_token_id]
								a      :		Tuple	=				[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      ) * [0] + len(token_ids_a + sep      ) * [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								a      :		List[str]	=				self._tokenizer.model.save(lowerCAmelCase__      ,					name=lowerCAmelCase__      )
								return tuple(lowerCAmelCase__      )


