
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    "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(__lowerCAmelCase       ,				__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


			lowerCAmelCase_      ,    lowerCAmelCase_         =      emb.weight.shape
			lowerCAmelCase_         =      nn.Linear(__lowerCAmelCase       ,				__lowerCAmelCase       ,				bias=__lowerCAmelCase							)
			lowerCAmelCase_         =      emb.weight.data
			return lin_layer






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			lowerCAmelCase_         =      mam_aaa["args"] or mam_aaa["cfg"]["model"]
			lowerCAmelCase_         =      mam_aaa["model"]
			remove_ignore_keys_(__lowerCAmelCase							)
			lowerCAmelCase_         =      state_dict["encoder.embed_tokens.weight"].shape[0]

			lowerCAmelCase_         =      MaMaaaConfig(
			    vocab_size=__lowerCAmelCase       ,				max_position_embeddings=1024       ,				encoder_layers=args.encoder_layers       ,				decoder_layers=args.decoder_layers       ,				encoder_attention_heads=args.encoder_attention_heads       ,				decoder_attention_heads=args.decoder_attention_heads       ,				encoder_ffn_dim=args.encoder_ffn_embed_dim       ,				decoder_ffn_dim=args.decoder_ffn_embed_dim       ,				d_model=args.encoder_embed_dim       ,				encoder_layerdrop=args.encoder_layerdrop       ,				decoder_layerdrop=args.decoder_layerdrop       ,				dropout=args.dropout       ,				attention_dropout=args.attention_dropout       ,				activation_dropout=args.activation_dropout       ,				activation_function="relu"       ,				)

			lowerCAmelCase_         =      state_dict["decoder.embed_tokens.weight"]
			lowerCAmelCase_         =      MaMaaaForConditionalGeneration(__lowerCAmelCase							)
			model.model.load_state_dict(__lowerCAmelCase       ,				strict=__lowerCAmelCase							)
			lowerCAmelCase_         =      make_linear_from_emb(model.model.shared							)

			return model


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
			parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			_A  =	parser.parse_args()
			_A  =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
			model.save_pretrained(args.pytorch_dump_folder_path)





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A  =	logging.get_logger(__name__)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "decoder.project_in_dim.weight": "decoder.project_in.weight",
			    "decoder.project_out_dim.weight": "decoder.project_out.weight",
			    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
			    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      list(sd.keys()							)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCAmelCase_         =      sd[key]
									# We split QKV in separate Q,K,V

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

									lowerCAmelCase_         =      q
									lowerCAmelCase_         =      k
									lowerCAmelCase_         =      v
									del sd[key]

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check results
			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



