






"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						__magic_name__            =     [
						    "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:int   ):

						'''simple docstring'''






						__magic_name__      ,    __magic_name__            =     emb.weight.shape
						__magic_name__            =     nn.Linear(__lowerCamelCase ,						__lowerCamelCase ,						bias=__lowerCamelCase   )
						__magic_name__            =     emb.weight.data
						return lin_layer






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






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

						__magic_name__            =     MaMaaaConfig(
						    vocab_size=__lowerCamelCase ,						max_position_embeddings=1_0_2_4 ,						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" ,						)

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

						return model


if __name__ == "__main__":
			lowercase    			=       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.''')
			lowercase    			=       parser.parse_args()
			lowercase    			=       convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
			model.save_pretrained(args.pytorch_dump_folder_path)














"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



