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




"""simple docstring"""

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

UpperCAmelCase__					=logging.get_logger(__name__)





class  lowerCamelCase__    (		_a     ):
      a			:  Optional[int]           =		"""vision-encoder-decoder"""
      a			:  Dict           =		True
      def __init__(		self						:						Dict   ,							**A_						:						List[Any]				):






       '''simple docstring'''


       super().__init__(**A_				)
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'''A configuraton of type {self.model_type} cannot be instantiated because '''
            F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''				)

       __lowercase						=						kwargs.pop("""encoder"""				)
       __lowercase						=						encoder_config.pop("""model_type"""				)
       __lowercase						=						kwargs.pop("""decoder"""				)
       __lowercase						=						decoder_config.pop("""model_type"""				)

       __lowercase						=						AutoConfig.for_model(A_   ,							**A_				)
       __lowercase						=						AutoConfig.for_model(A_   ,							**A_				)
       __lowercase						=						True
      @classmethod
      def SCREAMING_SNAKE_CASE_						(		cls						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						PretrainedConfig   ,							**A_						:						Any				):






       '''simple docstring'''


       logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""				)
       __lowercase						=						True
       __lowercase						=						True

       return cls(encoder=encoder_config.to_dict()   ,							decoder=decoder_config.to_dict()   ,							**A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						copy.deepcopy(self.__dict__				)
       __lowercase						=						self.encoder.to_dict()
       __lowercase						=						self.decoder.to_dict()
       __lowercase						=						self.__class__.model_type
       return output






class  lowerCamelCase__    (		_a     ):
      a			:  Dict           =		version.parse("""1.11"""     )
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]				)
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}				)






class  lowerCamelCase__    (		_a     ):
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						OrderedDict()
       __lowercase						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       __lowercase						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       __lowercase						=						{0: """batch""", 1: """encoder_sequence"""}

       return common_inputs






      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						"PreTrainedTokenizerBase"   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional["TensorType"] = None   ,							):






       '''simple docstring'''


       import torch

       __lowercase						=						OrderedDict()

       __lowercase						=						super().generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       __lowercase				,		__lowercase						=						dummy_input["""input_ids"""].shape
       __lowercase						=						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowercase						=						dummy_input.pop("""input_ids"""				)
       __lowercase						=						dummy_input.pop("""attention_mask"""				)
       __lowercase						=						torch.zeros(A_				)

       return common_inputs






class  lowerCamelCase__    (		_a     ):
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						PretrainedConfig				):






       '''simple docstring'''


       return VisionEncoderDecoderEncoderOnnxConfig(A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						PretrainedConfig   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"				):






       '''simple docstring'''


       __lowercase						=						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(A_   ,							A_				)
