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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[
			    "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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__					,    lowercase__   					=			emb.weight.shape
			lowercase__   					=			nn.Linear(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					bias=SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			emb.weight.data
			return lin_layer



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None					):
			lowercase__   					=			{}
			for old_key in state_dict.keys():
						lowercase__   					=			old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												lowercase__   					=			key.replace("moe_layer.experts.0"  ,					f'''ffn.experts.expert_{expert_idx}'''					)
									else:
												lowercase__   					=			key.replace("moe_layer.experts."  ,					"ffn.experts.expert_"					)
						if "gate" in key:
									lowercase__   					=			key.replace(".moe_layer.gate.wg"  ,					".ffn.router.classifier"					)
						if "fc2" and "experts" not in key:
									lowercase__   					=			key.replace(".fc2."  ,					".ffn.fc2."					)
						if "fc1" and "experts" not in key:
									lowercase__   					=			key.replace(".fc1."  ,					".ffn.fc1."					)
						if ".encoder_attn." in key:
									lowercase__   					=			key.replace(".encoder_attn."  ,					".cross_attention."					)
						if "encoder_attn_layer_norm" in key:
									lowercase__   					=			key.replace("encoder_attn_layer_norm"  ,					"cross_attention_layer_norm"					)
						if "final_layer_norm" in key:
									lowercase__   					=			key.replace("final_layer_norm"  ,					"ff_layer_norm"					)
						lowercase__   					=			state_dict[old_key]
			return new_dict



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME					):
			lowercase__   					=			[]
			lowercase__   					=			0
			os.makedirs(SCREAMING_SNAKE_CASE_  ,					exist_ok=SCREAMING_SNAKE_CASE_					)

			for expert in range(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			switch_checkpoint_path + f'''-rank-{expert}.pt'''
						if os.path.isfile(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			torch.load(SCREAMING_SNAKE_CASE_					)["model"]
									remove_ignore_keys_(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			rename_fairseq_keys(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			os.path.join(
									    SCREAMING_SNAKE_CASE_  ,					weights_name.replace(".bin"  ,					f'''-{len(SCREAMING_SNAKE_CASE_					)+1:05d}-of-???.bin'''					)					)
									torch.save(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
									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(SCREAMING_SNAKE_CASE_					)[0]].dtype					)

    # Add the last block
			lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					weights_name.replace(".bin"  ,					f'''-{len(SCREAMING_SNAKE_CASE_					)+1:05d}-of-???.bin'''					)					)
			lowercase__   					=			torch.load(switch_checkpoint_path + "-shared.pt"					)["model"]
			remove_ignore_keys_(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			rename_fairseq_keys(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			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(SCREAMING_SNAKE_CASE_					) == 1:
						lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						torch.save(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			# Otherwise, let's build the index
			lowercase__   					=			{}
			for idx, shard in enumerate(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			weights_name.replace(".bin"  ,					f'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_					):05d}.bin'''					)
						lowercase__   					=			os.path.join(SCREAMING_SNAKE_CASE_  ,					weights_name.replace(".bin"  ,					f'''-{idx+1:05d}-of-???.bin'''					)					)
						os.rename(SCREAMING_SNAKE_CASE_  ,					os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)					)
						for key in shard:
									lowercase__   					=			shard_file

    # Add the metadata
			lowercase__   					=			{"total_size": total_size}
			lowercase__   					=			{"metadata": metadata, "weight_map": weight_map}

			with open(os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)  ,					"w"  ,					encoding="utf-8"					) as f:
						lowercase__   					=			json.dumps(SCREAMING_SNAKE_CASE_  ,					indent=2  ,					sort_keys=SCREAMING_SNAKE_CASE_					) + "\n"
						f.write(SCREAMING_SNAKE_CASE_					)

			return metadata, index


if __name__ == "__main__":
							lowercase_						=       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.""",
							)
							lowercase_						=       parser.parse_args()
							lowercase_			,      lowercase_						=       shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							lowercase_						=       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)
							lowercase_						=       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

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

    # Transformer Blocks #
							lowercase_						=       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"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       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"]:
																																			lowercase_						=       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"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       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)




