




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           LayoutLMForMaskedLM,
           LayoutLMForQuestionAnswering,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMModel,
       )




class 		UpperCamelCase__     :

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=13					,  UpperCamelCase__=7					,  UpperCamelCase__=True					,  UpperCamelCase__=True					,  UpperCamelCase__=True					,  UpperCamelCase__=True					,  UpperCamelCase__=99					,  UpperCamelCase__=32					,  UpperCamelCase__=5					,  UpperCamelCase__=4					,  UpperCamelCase__=37					,  UpperCamelCase__="gelu"					,  UpperCamelCase__=0.1					,  UpperCamelCase__=0.1					,  UpperCamelCase__=512					,  UpperCamelCase__=16					,  UpperCamelCase__=2					,  UpperCamelCase__=0.02					,  UpperCamelCase__=3					,  UpperCamelCase__=4					,  UpperCamelCase__=None					,  UpperCamelCase__=1000					,  )					->				int:
           lowerCamelCase       :				Any      			= parent
           lowerCamelCase       :				Union[str, Any]      			= batch_size
           lowerCamelCase       :				int      			= seq_length
           lowerCamelCase       :				List[Any]      			= is_training
           lowerCamelCase       :				Any      			= use_input_mask
           lowerCamelCase       :				List[Any]      			= use_token_type_ids
           lowerCamelCase       :				Optional[int]      			= use_labels
           lowerCamelCase       :				Union[str, Any]      			= vocab_size
           lowerCamelCase       :				Optional[int]      			= hidden_size
           lowerCamelCase       :				List[str]      			= num_hidden_layers
           lowerCamelCase       :				Optional[int]      			= num_attention_heads
           lowerCamelCase       :				Optional[Any]      			= intermediate_size
           lowerCamelCase       :				Union[str, Any]      			= hidden_act
           lowerCamelCase       :				Optional[int]      			= hidden_dropout_prob
           lowerCamelCase       :				Optional[Any]      			= attention_probs_dropout_prob
           lowerCamelCase       :				Optional[int]      			= max_position_embeddings
           lowerCamelCase       :				int      			= type_vocab_size
           lowerCamelCase       :				Optional[Any]      			= type_sequence_label_size
           lowerCamelCase       :				int      			= initializer_range
           lowerCamelCase       :				Union[str, Any]      			= num_labels
           lowerCamelCase       :				str      			= num_choices
           lowerCamelCase       :				int      			= scope
           lowerCamelCase       :				Any      			= range_bbox







    def    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				Optional[Any]      			= ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

           lowerCamelCase       :				Dict      			= ids_tensor([self.batch_size, self.seq_length, 4]					,  self.range_bbox					)
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]					):
                  for j in range(bbox.shape[1]					):
                         if bbox[i, j, 3] < bbox[i, j, 1]:
                                lowerCamelCase       :				Tuple      			= bbox[i, j, 3]
                                lowerCamelCase       :				List[Any]      			= bbox[i, j, 1]
                                lowerCamelCase       :				Tuple      			= t
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                lowerCamelCase       :				int      			= bbox[i, j, 2]
                                lowerCamelCase       :				str      			= bbox[i, j, 0]
                                lowerCamelCase       :				Optional[int]      			= t

           lowerCamelCase       :				Union[str, Any]      			= None
           if self.use_input_mask:
                  lowerCamelCase       :				List[Any]      			= ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2					)

           lowerCamelCase       :				int      			= None
           if self.use_token_type_ids:
                  lowerCamelCase       :				Union[str, Any]      			= ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size					)

           lowerCamelCase       :				Dict      			= None
           lowerCamelCase       :				Union[str, Any]      			= None
           lowerCamelCase       :				List[str]      			= None
           if self.use_labels:
                  lowerCamelCase       :				Union[str, Any]      			= ids_tensor([self.batch_size]					,  self.type_sequence_label_size					)
                  lowerCamelCase       :				Dict      			= ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels					)
                  lowerCamelCase       :				Dict      			= ids_tensor([self.batch_size]					,  self.num_choices					)

           lowerCamelCase       :				Union[str, Any]      			= self.get_config()

           return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def    _lowercase					(   self					)					->				Dict:
           return LayoutLMConfig(
               vocab_size=self.vocab_size					,  hidden_size=self.hidden_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					,  )







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				str:
           lowerCamelCase       :				Any      			= LayoutLMModel(config=UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           lowerCamelCase       :				Any      			= model(UpperCamelCase__					,  UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					)
           lowerCamelCase       :				Union[str, Any]      			= model(UpperCamelCase__					,  UpperCamelCase__					,  token_type_ids=UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= model(UpperCamelCase__					,  UpperCamelCase__					)
           self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)
           self.parent.assertEqual(result.pooler_output.shape					,  (self.batch_size, self.hidden_size)					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				int:
           lowerCamelCase       :				Any      			= LayoutLMForMaskedLM(config=UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           lowerCamelCase       :				Tuple      			= model(UpperCamelCase__					,  UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				List[str]:
           lowerCamelCase       :				Any      			= self.num_labels
           lowerCamelCase       :				Dict      			= LayoutLMForSequenceClassification(UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           lowerCamelCase       :				str      			= model(
               UpperCamelCase__					,  UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Dict:
           lowerCamelCase       :				Tuple      			= self.num_labels
           lowerCamelCase       :				Any      			= LayoutLMForTokenClassification(config=UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           lowerCamelCase       :				Optional[int]      			= model(UpperCamelCase__					,  UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=UpperCamelCase__					)
           self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels)					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Tuple:
           lowerCamelCase       :				Optional[int]      			= LayoutLMForQuestionAnswering(config=UpperCamelCase__					)
           model.to(UpperCamelCase__					)
           model.eval()
           lowerCamelCase       :				Optional[int]      			= model(
               UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  start_positions=UpperCamelCase__					,  end_positions=UpperCamelCase__					,  )
           self.parent.assertEqual(result.start_logits.shape					,  (self.batch_size, self.seq_length)					)
           self.parent.assertEqual(result.end_logits.shape					,  (self.batch_size, self.seq_length)					)







    def    _lowercase					(   self					)					->				str:
           lowerCamelCase       :				Union[str, Any]      			= self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				(
               lowerCamelCase
           )			,				
           )       :				List[Any]      			= config_and_inputs
           lowerCamelCase       :				int      			= {
               "input_ids": input_ids,
               "bbox": bbox,
               "token_type_ids": token_type_ids,
               "attention_mask": input_mask,
           }
           return config, inputs_dict




@require_torch
class 		UpperCamelCase__     (lowerCAmelCase__   ,    lowerCAmelCase__   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[Any]     					=				(
        (
            LayoutLMModel,
            LayoutLMForMaskedLM,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    lowerCamelCase_				:     Any     					=				(
        {
            """document-question-answering""": LayoutLMForQuestionAnswering,
            """feature-extraction""": LayoutLMModel,
            """fill-mask""": LayoutLMForMaskedLM,
            """text-classification""": LayoutLMForSequenceClassification,
            """token-classification""": LayoutLMForTokenClassification,
            """zero-shot""": LayoutLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase_				:     Any     					=				True







    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				Any      			= LayoutLMModelTester(self					)
           lowerCamelCase       :				Tuple      			= ConfigTester(self					,  config_class=UpperCamelCase__					,  hidden_size=37					)







    def    _lowercase					(   self					)					->				List[str]:
           self.config_tester.run_common_tests()







    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				List[Any]      			= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__					)







    def    _lowercase					(   self					)					->				List[Any]:
           lowerCamelCase       :				List[str]      			= self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  lowerCamelCase       :				Optional[Any]      			= type
                  self.model_tester.create_and_check_model(*UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				List[str]      			= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Dict:
           lowerCamelCase       :				Union[str, Any]      			= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				int      			= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				List[Any]      			= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__					)







    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
    def    _lowercase					(   self					)					->				Tuple:
           pass




def 					A				(				)     ->			str:
       # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
       # fmt: off
       lowerCamelCase       :				str      			= torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]			,device=_SCREAMING_SNAKE_CASE   )  # noqa: E231
       lowerCamelCase       :				str      			= torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]			,device=_SCREAMING_SNAKE_CASE   )  # noqa: E231
       lowerCamelCase       :				Union[str, Any]      			= torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]			,device=_SCREAMING_SNAKE_CASE   )  # noqa: E231
       lowerCamelCase       :				str      			= torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]			,device=_SCREAMING_SNAKE_CASE   )  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       lowerCamelCase       :				Optional[int]      			= torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]			,device=_SCREAMING_SNAKE_CASE   )  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class 		UpperCamelCase__     (unittest.TestCase					):

    '''simple docstring'''



    @slow
    def    _lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase       :				List[str]      			= LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"					).to(UpperCamelCase__					)

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCamelCase       :				Dict      			= model(input_ids=UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					)

           # test the sequence output on [0, :3, :3]
           lowerCamelCase       :				Any      			= torch.tensor(
               [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]					,  device=UpperCamelCase__					,  )

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,  UpperCamelCase__					,  atol=1e-3					)					)

           # test the pooled output on [1, :3]
           lowerCamelCase       :				str      			= torch.tensor([-0.6580, -0.0214, 0.8552]					,  device=UpperCamelCase__					)

           self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					,  UpperCamelCase__					,  atol=1e-3					)					)







    @slow
    def    _lowercase					(   self					)					->				List[str]:
           # initialize model with randomly initialized sequence classification head
           lowerCamelCase       :				Dict      			= LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"					,  num_labels=2					).to(
               UpperCamelCase__					)

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Any      			= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCamelCase       :				Optional[Any]      			= model(
               input_ids=UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=torch.tensor([1, 1]					,  device=UpperCamelCase__					)					,  )

           # test whether we get a loss as a scalar
           lowerCamelCase       :				Tuple      			= outputs.loss
           lowerCamelCase       :				Any      			= torch.Size([]					)
           self.assertEqual(loss.shape					,  UpperCamelCase__					)

           # test the shape of the logits
           lowerCamelCase       :				Tuple      			= outputs.logits
           lowerCamelCase       :				List[Any]      			= torch.Size((2, 2)					)
           self.assertEqual(logits.shape					,  UpperCamelCase__					)







    @slow
    def    _lowercase					(   self					)					->				str:
           # initialize model with randomly initialized token classification head
           lowerCamelCase       :				Dict      			= LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"					,  num_labels=13					).to(
               UpperCamelCase__					)

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCamelCase       :				List[Any]      			= model(
               input_ids=UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					,  labels=UpperCamelCase__					)

           # test the loss calculation to be around 2.65
           # expected_loss = torch.tensor(2.65, device=torch_device)

           # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
           # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

           # test the shape of the logits
           lowerCamelCase       :				Optional[Any]      			= outputs.logits
           lowerCamelCase       :				Any      			= torch.Size((2, 25, 13)					)
           self.assertEqual(logits.shape					,  UpperCamelCase__					)







    @slow
    def    _lowercase					(   self					)					->				Optional[int]:
           # initialize model with randomly initialized token classification head
           lowerCamelCase       :				Any      			= LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"					).to(UpperCamelCase__					)

           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Tuple      			= prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCamelCase       :				str      			= model(input_ids=UpperCamelCase__					,  bbox=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  token_type_ids=UpperCamelCase__					)

           # test the shape of the logits
           lowerCamelCase       :				Dict      			= torch.Size((2, 25)					)
           self.assertEqual(outputs.start_logits.shape					,  UpperCamelCase__					)
           self.assertEqual(outputs.end_logits.shape					,  UpperCamelCase__					)












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

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

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'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'])






