


"""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      a__ (				__lowercase   )	->	int:
				# 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      a__ (				__lowercase     ,			__lowercase   )	->	int:
				_A					  =			{}

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

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

								_A					  =			value.float()

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

				return upgrade






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=None   )	->	Any:
				if config_path is not None:
								_A					  =			FlavaConfig.from_pretrained(__lowercase   )
				else:
								_A					  =			FlavaConfig()

				_A					  =			FlavaForPreTraining(__lowercase   ).eval()

				_A					  =			convert_dalle_checkpoint(__lowercase     ,			__lowercase     ,			save_checkpoint=__lowercase   )

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

				_A					  =			upgrade_state_dict(__lowercase     ,			__lowercase   )
				hf_model.load_state_dict(__lowercase   )
				_A					  =			hf_model.state_dict()
				_A					  =			count_parameters(__lowercase   )
				_A					  =			count_parameters(__lowercase   ) + count_parameters(__lowercase   )

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

				hf_model.save_pretrained(__lowercase   )


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)



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser(
		    description=(
		        "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
		        " Distillation"
		    )
		)
		parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
		parser.add_argument("--model_name", default="roberta-large", type=str)
		parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
		parser.add_argument("--vocab_transform", action="store_true")
		a_       							=    parser.parse_args()

		if args.model_type == "roberta":
				a_       							=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a_       							=    "roberta"
		elif args.model_type == "gpt2":
				a_       							=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a_       							=    "transformer"

		a_       							=    model.state_dict()
		a_       							=    {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						a_       							=    state_dict[f'''{prefix}.{param_name}''']
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						a_       							=    f'''{prefix}.embeddings.{w}.weight'''
						a_       							=    state_dict[param_name]
				for w in ["weight", "bias"]:
						a_       							=    f'''{prefix}.embeddings.LayerNorm.{w}'''
						a_       							=    state_dict[param_name]

    # Transformer Blocks #
		a_       							=    0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				if args.model_type == "gpt2":
						for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
								for w in ["weight", "bias"]:
										a_       							=    state_dict[
										    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						a_       							=    state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
				else:
						for layer in [
						    "attention.self.query",
						    "attention.self.key",
						    "attention.self.value",
						    "attention.output.dense",
						    "attention.output.LayerNorm",
						    "intermediate.dense",
						    "output.dense",
						    "output.LayerNorm",
						]:
								for w in ["weight", "bias"]:
										a_       							=    state_dict[
										    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
										]
				std_idx += 1

		# Language Modeling Head ###s
		if args.model_type == "roberta":
				for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
						a_       							=    state_dict[f'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								a_       							=    state_dict[f'''lm_head.dense.{w}''']
								a_       							=    state_dict[f'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						a_       							=    state_dict[f'''{prefix}.ln_f.{w}''']
				a_       							=    state_dict["lm_head.weight"]

		print(f'''N layers selected for distillation: {std_idx}''')
		print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)