





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






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


A_												=     logging.get_logger(__name__)

A_												=     {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "longformer"





					def __init__( self	,  _lowerCAmelCase = 512	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 1	,  _lowerCAmelCase = 0	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 3_0522	,  _lowerCAmelCase = 768	,  _lowerCAmelCase = 12	,  _lowerCAmelCase = 12	,  _lowerCAmelCase = 3072	,  _lowerCAmelCase = "gelu"	,  _lowerCAmelCase = 0.1	,  _lowerCAmelCase = 0.1	,  _lowerCAmelCase = 512	,  _lowerCAmelCase = 2	,  _lowerCAmelCase = 0.02	,  _lowerCAmelCase = 1E-12	,  _lowerCAmelCase = False	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       attention_window
										lowerCamelCase__       =       sep_token_id
										lowerCamelCase__       =       bos_token_id
										lowerCamelCase__       =       eos_token_id
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       onnx_export

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = "default"	,  _lowerCAmelCase = None	):
										super().__init__(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       True





					@property
					def 					__magic_name__ ( self	):
										if self.task == "multiple-choice":
															lowerCamelCase__       =       {0: "batch", 1: "choice", 2: "sequence"}
										else:
															lowerCamelCase__       =       {0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("global_attention_mask", dynamic_axis),
										    ]	)





					@property
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       super().outputs
										if self.task == "default":
															lowerCamelCase__       =       {0: "batch"}
										return outputs





					@property
					def 					__magic_name__ ( self	):
										return 1E-4





					@property
					def 					__magic_name__ ( self	):
										# needs to be >= 14 to support tril operator
										return max(super().default_onnx_opset	,  14	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = -1	,  _lowerCAmelCase = False	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       super().generate_dummy_inputs(
										    preprocessor=_lowerCAmelCase	,  batch_size=_lowerCAmelCase	,  seq_length=_lowerCAmelCase	,  is_pair=_lowerCAmelCase	,  framework=_lowerCAmelCase	)
										import torch

										# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
										# makes the export fail randomly
										lowerCamelCase__       =       torch.zeros_like(inputs["input_ids"]	)
										# make every second token global
										lowerCamelCase__       =       1

										return inputs

