






"""simple docstring"""
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Any      =      ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			UpperCAmelCase_  :				int      =      (
			    ("layer.", "layer_"),
			    ("word_embeddings.weight", "word_embeddings"),
			    ("position_embeddings.weight", "position_embeddings"),
			    ("token_type_embeddings.weight", "token_type_embeddings"),
			    (".", "/"),
			    ("LayerNorm/weight", "LayerNorm/gamma"),
			    ("LayerNorm/bias", "LayerNorm/beta"),
			    ("weight", "kernel"),
			)

			if not os.path.isdir(A__		):
						os.makedirs(A__		)

			UpperCAmelCase_  :				Optional[Any]      =      model.state_dict()

			def to_tf_var_name(A__		):
						for patt, repl in iter(A__		):
									UpperCAmelCase_  :				Dict      =      name.replace(A__			,A__		)
						return F"""bert/{name}"""

			def create_tf_var(A__			,A__			,A__		):
						UpperCAmelCase_  :				Union[str, Any]      =      tf.dtypes.as_dtype(tensor.dtype		)
						UpperCAmelCase_  :				Optional[Any]      =      tf.get_variable(dtype=A__			,shape=tensor.shape			,name=A__			,initializer=tf.zeros_initializer()		)
						session.run(tf.variables_initializer([tf_var]		)		)
						session.run(A__		)
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									UpperCAmelCase_  :				Optional[int]      =      to_tf_var_name(A__		)
									UpperCAmelCase_  :				str      =      state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose		):
												UpperCAmelCase_  :				Union[str, Any]      =      torch_tensor.T
									UpperCAmelCase_  :				Dict      =      create_tf_var(tensor=A__			,name=A__			,session=A__		)
									tf.keras.backend.set_value(A__			,A__		)
									UpperCAmelCase_  :				str      =      session.run(A__		)
									print(F"""Successfully created {tf_name}: {np.allclose(A__			,A__		)}"""		)

						UpperCAmelCase_  :				List[str]      =      tf.train.Saver(tf.trainable_variables()		)
						saver.save(A__			,os.path.join(A__			,model_name.replace("-"			,"_"		) + ".ckpt"		)		)

def        snake_case   (     A__=None		):
			UpperCAmelCase_  :				Union[str, Any]      =      argparse.ArgumentParser()
			parser.add_argument("--model_name"			,type=A__			,required=A__			,help="model name e.g. bert-base-uncased"		)
			parser.add_argument(
			    "--cache_dir"			,type=A__			,default=A__			,required=A__			,help="Directory containing pytorch model"		)
			parser.add_argument("--pytorch_model_path"			,type=A__			,required=A__			,help="/path/to/<pytorch-model-name>.bin"		)
			parser.add_argument("--tf_cache_dir"			,type=A__			,required=A__			,help="Directory in which to save tensorflow model"		)
			UpperCAmelCase_  :				Any      =      parser.parse_args(A__		)

			UpperCAmelCase_  :				List[str]      =      BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name			,state_dict=torch.load(args.pytorch_model_path		)			,cache_dir=args.cache_dir			,)

			convert_pytorch_checkpoint_to_tf(model=A__			,ckpt_dir=args.tf_cache_dir			,model_name=args.model_name		)


if __name__ == "__main__":
			main()











"""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
			lowerCamelCase_       			=   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''')
			lowerCamelCase_			,	lowerCamelCase_       			=   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''')
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowerCamelCase_       			=   rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowerCamelCase_       			=   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)



