




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       (     _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       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		{}

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

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

														lowerCAmelCase   =		value.float()

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

							return upgrade



@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case=None							):
							if config_path is not None:
														lowerCAmelCase   =		FlavaConfig.from_pretrained(_snake_case							)
							else:
														lowerCAmelCase   =		FlavaConfig()

							lowerCAmelCase   =		FlavaForPreTraining(_snake_case							).eval()

							lowerCAmelCase   =		convert_dalle_checkpoint(_snake_case						, _snake_case						, save_checkpoint=_snake_case							)

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

							lowerCAmelCase   =		upgrade_state_dict(_snake_case						, _snake_case							)
							hf_model.load_state_dict(_snake_case							)
							lowerCAmelCase   =		hf_model.state_dict()
							lowerCAmelCase   =		count_parameters(_snake_case							)
							lowerCAmelCase   =		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_						=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_						=parser.parse_args()

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









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



