



'''simple docstring'''







import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





def     snake_case__			(*lowerCAmelCase_						):
    UpperCamelCase__	     =      torch.cuda.device_count()
    print(F'Device count: {num_processes}'						)
    with patch_environment(
        world_size=lowerCAmelCase_       ,	master_addr="127.0.01"       ,	master_port="29500"       ,	mixed_precision=args[1].mixed_precision						):
        UpperCamelCase__	     =      PrepareForLaunch(lowerCAmelCase_       ,	distributed_type="MULTI_GPU"						)
        torch.multiprocessing.start_processes(lowerCAmelCase_       ,	args=lowerCAmelCase_       ,	nprocs=lowerCAmelCase_       ,	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
      lowercase__  :	List[Any]            =      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
      lowercase__  :	Dict            =      [
          'pip:./',
          'transformers',
          'datasets',
          'evaluate',
          'tqdm',
          'scipy',
          'scikit-learn',
          'tensorboard',
          'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
      ]
      lowercase__  :	List[str]            =      rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

      # Define train args/config, run train function
      lowercase__  :	Union[str, Any]            =      argparse.Namespace(cpu=False, mixed_precision='fp16')
      lowercase__  :	List[Any]            =      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      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'''







import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      from transformers.models.electra.modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
      )

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=13  ,    __UpperCamelCase=7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=99  ,    __UpperCamelCase=24  ,    __UpperCamelCase=32  ,    __UpperCamelCase=5  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=5_12  ,    __UpperCamelCase=16  ,    __UpperCamelCase=2  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=4  ,    ):
       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      seq_length
       UpperCamelCase__	     =      is_training
       UpperCamelCase__	     =      use_attention_mask
       UpperCamelCase__	     =      use_token_type_ids
       UpperCamelCase__	     =      use_labels
       UpperCamelCase__	     =      vocab_size
       UpperCamelCase__	     =      hidden_size
       UpperCamelCase__	     =      embedding_size
       UpperCamelCase__	     =      num_hidden_layers
       UpperCamelCase__	     =      num_attention_heads
       UpperCamelCase__	     =      intermediate_size
       UpperCamelCase__	     =      hidden_act
       UpperCamelCase__	     =      hidden_dropout_prob
       UpperCamelCase__	     =      attention_probs_dropout_prob
       UpperCamelCase__	     =      max_position_embeddings
       UpperCamelCase__	     =      type_vocab_size
       UpperCamelCase__	     =      type_sequence_label_size
       UpperCamelCase__	     =      initializer_range
       UpperCamelCase__	     =      num_choices


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size			)

       UpperCamelCase__	     =      None
       if self.use_attention_mask:
           UpperCamelCase__	     =      random_attention_mask([self.batch_size, self.seq_length]			)

       UpperCamelCase__	     =      None
       if self.use_token_type_ids:
           UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size			)

       UpperCamelCase__	     =      ElectraConfig(
           vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    embedding_size=self.embedding_size  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    intermediate_size=self.intermediate_size  ,    hidden_act=self.hidden_act  ,    hidden_dropout_prob=self.hidden_dropout_prob  ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    type_vocab_size=self.type_vocab_size  ,    initializer_range=self.initializer_range  ,    )

       return config, input_ids, token_type_ids, attention_mask







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.prepare_config_and_inputs()
       UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      config_and_inputs
       UpperCamelCase__	     =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict

@require_flax
class    lowerCamelCase_				(						lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  Dict					=						True

   A			:  Optional[Any]					=						(
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlaxElectraModelTester(self			)







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_class_name in self.all_model_classes:
           if model_class_name == FlaxElectraForMaskedLM:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-generator"			)
           else:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-discriminator"			)
           UpperCamelCase__	     =      model(np.ones((1, 1)			)			)
           self.assertIsNotNone(__UpperCamelCase			)




