

'''simple docstring'''



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def      a__ (						*a__		):
     __SCREAMING_SNAKE_CASE						     =   torch.cuda.device_count()
     print(F'Device count: {num_processes}'		)
     with patch_environment(
         world_size=a__   ,					master_addr="""127.0.01"""   ,					master_port="""29500"""   ,					mixed_precision=args[1].mixed_precision		):
          __SCREAMING_SNAKE_CASE						     =   PrepareForLaunch(a__   ,					distributed_type="""MULTI_GPU"""		)
          torch.multiprocessing.start_processes(a__   ,					args=a__   ,					nprocs=a__   ,					start_method="""spawn"""		)


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
  # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

  # on-demand GPU
  # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
  UpperCAmelCase				: Tuple         =  rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
  gpu.up_if_not()

  # on-prem GPU
  # gpu = rh.cluster(
  #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
  #       )

  # Set up remote function
  UpperCAmelCase				: Any         =  [
      'pip:./',
      'transformers',
      'datasets',
      'evaluate',
      'tqdm',
      'scipy',
      'scikit-learn',
      'tensorboard',
      'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
  ]
  UpperCAmelCase				: str         =  rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

  # Define train args/config, run train function
  UpperCAmelCase				: Tuple         =  argparse.Namespace(cpu=False, mixed_precision='fp16')
  UpperCAmelCase				: Optional[Any]         =  {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
  launch_train_gpu(config, train_args, stream_logs=True)

  # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
  # gpu.install_packages(reqs)
  # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])









'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 2:
           raise ValueError(
               f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
               """'gated-gelu' or 'relu'"""    )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






