
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 			lowerCamelCase_ (				lowerCamelCase__    ):
				# 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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None    ):
				if config_path is not None:
								lowerCamelCase_						=			FlavaConfig.from_pretrained(lowerCamelCase__    )
				else:
								lowerCamelCase_						=			FlavaConfig()

				lowerCamelCase_						=			FlavaForPreTraining(lowerCamelCase__    ).eval()

				lowerCamelCase_						=			convert_dalle_checkpoint(lowerCamelCase__	, lowerCamelCase__	, save_checkpoint=lowerCamelCase__    )

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

				lowerCamelCase_						=			upgrade_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				hf_model.load_state_dict(lowerCamelCase__    )
				lowerCamelCase_						=			hf_model.state_dict()
				lowerCamelCase_						=			count_parameters(lowerCamelCase__    )
				lowerCamelCase_						=			count_parameters(lowerCamelCase__    ) + count_parameters(lowerCamelCase__    )

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

				hf_model.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
			__A				=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				=parser.parse_args()

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




from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


