from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
    from .features import FeatureType


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						List[str]
    a__	:						Optional[str]						=      None
    # Automatically constructed
    a__	:						ClassVar[str]						=      "dict"
    a__	:						ClassVar[Any]						=      None
    a__	:						str						=      field(default="""Translation"""						,	init=UpperCAmelCase_						,	repr=UpperCAmelCase_ )







    def __call__(	self)       ->	List[Any]:
        return pa.struct({lang: pa.string() for lang in sorted(self.languages)})







    def 			UpperCamelCase__   (	self)       ->	Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Value

        return {k: Value('''string''') for k in sorted(self.languages)}


@dataclass
class 	lowerCamelCase_     :






    '''simple docstring'''






    a__	:						Optional[List]						=      None
    a__	:						Optional[int]						=      None
    a__	:						Optional[str]						=      None
    # Automatically constructed
    a__	:						ClassVar[str]						=      "dict"
    a__	:						ClassVar[Any]						=      None
    a__	:						str						=      field(default="""TranslationVariableLanguages"""						,	init=UpperCAmelCase_						,	repr=UpperCAmelCase_ )







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :int			  =		sorted(set(self.languages)) if self.languages else None
        __UpperCamelCase     :List[Any]			  =		len(self.languages) if self.languages else None







    def __call__(	self)       ->	Tuple:
        return pa.struct({'''language''': pa.list_(pa.string()), '''translation''': pa.list_(pa.string())})







    def 			UpperCamelCase__   (	self , __lowercase)       ->	int:
        __UpperCamelCase     :Any			  =		set(self.languages)
        if self.languages and set(__lowercase) - lang_set:
            raise ValueError(
                f"""Some languages in example ({', '.join(sorted(set(__lowercase) - lang_set))}) are not in valid set ({', '.join(__lowercase)}).""")

        # Convert dictionary into tuples, splitting out cases where there are
        # multiple translations for a single language.
        __UpperCamelCase     :List[Any]			  =		[]
        for lang, text in translation_dict.items():
            if isinstance(__lowercase , __lowercase):
                translation_tuples.append((lang, text))
            else:
                translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		zip(*sorted(__lowercase))

        return {"language": languages, "translation": translations}







    def 			UpperCamelCase__   (	self)       ->	Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Sequence, Value

        return {
            "language": Sequence(Value('''string''')),
            "translation": Sequence(Value('''string''')),
        }







"""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 lowercase_  (   _snake_case						):
							# encoder.embeddings are double copied in original FLAVA
							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()						)

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Any           =   {}

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

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

														SCREAMING_SNAKE_CASE__						:       Tuple           =   value.float()

							for key, value in codebook_state_dict.items():
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value

							return upgrade

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case=None						):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   FlavaConfig.from_pretrained(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   FlavaConfig()

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FlavaForPreTraining(_snake_case						).eval()

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   convert_dalle_checkpoint(_snake_case   ,_snake_case   ,save_checkpoint=_snake_case						)

							if os.path.exists(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.load(_snake_case   ,map_location="""cpu"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.hub.load_state_dict_from_url(_snake_case   ,map_location="""cpu"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   upgrade_state_dict(_snake_case   ,_snake_case						)
							hf_model.load_state_dict(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Any           =   hf_model.state_dict()
							SCREAMING_SNAKE_CASE__						:       Any           =   count_parameters(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       str           =   count_parameters(_snake_case						) + count_parameters(_snake_case						)

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

							hf_model.save_pretrained(_snake_case						)


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

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




