




'''simple docstring'''


import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Tuple:
	UpperCAmelCase_		:      Dict       	=					[
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    'decoder.output_projection.weight',
	    '_float_tensor',
	    'encoder.embed_positions._float_tensor',
	    'decoder.embed_positions._float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					emb.weight.shape
	UpperCAmelCase_		:      str       	=					nn.Linear(UpperCamelCase   ,UpperCamelCase   ,bias=UpperCamelCase  )
	UpperCAmelCase_		:      int       	=					emb.weight.data
	return lin_layer







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None  )    ->    List[str]:
	UpperCAmelCase_		:      Optional[Any]       	=					{}
	for old_key in state_dict.keys():
		UpperCAmelCase_		:      Optional[int]       	=					old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				UpperCAmelCase_		:      Any       	=					key.replace('moe_layer.experts.0'   ,f"""ffn.experts.expert_{expert_idx}"""  )
			else:
				UpperCAmelCase_		:      Union[str, Any]       	=					key.replace('moe_layer.experts.'   ,'ffn.experts.expert_'  )
		if "gate" in key:
			UpperCAmelCase_		:      List[Any]       	=					key.replace('.moe_layer.gate.wg'   ,'.ffn.router.classifier'  )
		if "fc2" and "experts" not in key:
			UpperCAmelCase_		:      List[Any]       	=					key.replace('.fc2.'   ,'.ffn.fc2.'  )
		if "fc1" and "experts" not in key:
			UpperCAmelCase_		:      Dict       	=					key.replace('.fc1.'   ,'.ffn.fc1.'  )
		if ".encoder_attn." in key:
			UpperCAmelCase_		:      Union[str, Any]       	=					key.replace('.encoder_attn.'   ,'.cross_attention.'  )
		if "encoder_attn_layer_norm" in key:
			UpperCAmelCase_		:      Tuple       	=					key.replace('encoder_attn_layer_norm'   ,'cross_attention_layer_norm'  )
		if "final_layer_norm" in key:
			UpperCAmelCase_		:      Tuple       	=					key.replace('final_layer_norm'   ,'ff_layer_norm'  )
		UpperCAmelCase_		:      Tuple       	=					state_dict[old_key]
	return new_dict







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase = WEIGHTS_NAME  )    ->    Dict:
	UpperCAmelCase_		:      Any       	=					[]
	UpperCAmelCase_		:      Optional[int]       	=					0
	os.makedirs(UpperCamelCase   ,exist_ok=UpperCamelCase  )

	for expert in range(UpperCamelCase  ):
		UpperCAmelCase_		:      int       	=					switch_checkpoint_path + f"""-rank-{expert}.pt"""
		if os.path.isfile(UpperCamelCase  ):
			UpperCAmelCase_		:      List[str]       	=					torch.load(UpperCamelCase  )['model']
			remove_ignore_keys_(UpperCamelCase  )
			UpperCAmelCase_		:      Optional[Any]       	=					rename_fairseq_keys(UpperCamelCase   ,UpperCamelCase  )
			UpperCAmelCase_		:      Union[str, Any]       	=					os.path.join(
			    UpperCamelCase   ,weights_name.replace('.bin'   ,f"""-{len(UpperCamelCase  )+1:05d}-of-???.bin"""  )  )
			torch.save(UpperCamelCase   ,UpperCamelCase  )
			sharded_state_dicts.append(expert_state.keys()  )
			total_size += sum([value.numel() for key, value in expert_state.items()]  ) * dtype_byte_size(
			    expert_state[list(UpperCamelCase  )[0]].dtype  )

    # Add the last block
	UpperCAmelCase_		:      Dict       	=					os.path.join(UpperCamelCase   ,weights_name.replace('.bin'   ,f"""-{len(UpperCamelCase  )+1:05d}-of-???.bin"""  )  )
	UpperCAmelCase_		:      List[str]       	=					torch.load(switch_checkpoint_path + '-shared.pt'  )['model']
	remove_ignore_keys_(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					rename_fairseq_keys(UpperCamelCase   ,UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					shared_weights['decoder.embed_tokens.weight']
	sharded_state_dicts.append(shared_weights.keys()  )

	# If we only have the shared weights (dummy model/experts saved on the same file)
	if len(UpperCamelCase  ) == 1:
		UpperCAmelCase_		:      List[str]       	=					os.path.join(UpperCamelCase   ,UpperCamelCase  )
		torch.save(UpperCamelCase   ,UpperCamelCase  )
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(UpperCamelCase   ,UpperCamelCase  )
	# Otherwise, let's build the index
	UpperCAmelCase_		:      Union[str, Any]       	=					{}
	for idx, shard in enumerate(UpperCamelCase  ):
		UpperCAmelCase_		:      Tuple       	=					weights_name.replace('.bin'   ,f"""-{idx+1:05d}-of-{len(UpperCamelCase  ):05d}.bin"""  )
		UpperCAmelCase_		:      Dict       	=					os.path.join(UpperCamelCase   ,weights_name.replace('.bin'   ,f"""-{idx+1:05d}-of-???.bin"""  )  )
		os.rename(UpperCamelCase   ,os.path.join(UpperCamelCase   ,UpperCamelCase  )  )
		for key in shard:
			UpperCAmelCase_		:      Optional[Any]       	=					shard_file

    # Add the metadata
	UpperCAmelCase_		:      Tuple       	=					{'total_size': total_size}
	UpperCAmelCase_		:      Dict       	=					{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(UpperCamelCase   ,UpperCamelCase  )   ,'w'   ,encoding='utf-8'  ) as f:
		UpperCAmelCase_		:      int       	=					json.dumps(UpperCamelCase   ,indent=2   ,sort_keys=UpperCamelCase  ) + '\n'
		f.write(UpperCamelCase  )

	return metadata, index


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--nllb_moe_checkpoint_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
						    type=str,
						    required=False,
						    help="Path to a directory containing a folder per layer. Follows the original Google format.",
						)
						parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
						    type=str,
						    required=False,
						    help="Path to the output pytorch model.",
						)
						lowerCAmelCase__										= parser.parse_args()
						lowerCAmelCase__							,     lowerCAmelCase__										= shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						lowerCAmelCase__										= NllbMoeConfig.from_pretrained(
						    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						lowerCAmelCase__										= NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






