
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch

class 		lowercase_							(							unittest.TestCase					):

 @patch("""sys.argv"""  ,						["""fakeprogrampath""", """env"""]  )
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        import transformers.commands.transformers_cli

        with CaptureStd() as cs:
               transformers.commands.transformers_cli.main()
        self.assertIn("""Python version"""  ,						cs.out  )
        self.assertIn("""Platform"""  ,						cs.out  )
        self.assertIn("""Using distributed or parallel set-up in script?"""  ,						cs.out  )

 @is_pt_tf_cross_test
 @patch(
     """sys.argv"""  ,						["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]  )
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        import transformers.commands.transformers_cli

        shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""  ,						ignore_errors=__UpperCamelCase  )  # cleans potential past runs
        transformers.commands.transformers_cli.main()

        # The original repo has no TF weights -- if they exist, they were created by the CLI
        self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""  )  )

 @require_torch
 @patch("""sys.argv"""  ,						["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]  )
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        import transformers.commands.transformers_cli

        # # remove any previously downloaded model to start clean
        shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""  ,						ignore_errors=__UpperCamelCase  )

        # run the command
        transformers.commands.transformers_cli.main()

        # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
        self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""  )  )
        self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""  )  )
        self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""  )  )

 @require_torch
 @patch(
     """sys.argv"""  ,						[
         """fakeprogrampath""",
         """download""",
         """hf-internal-testing/test_dynamic_model_with_tokenizer""",
         """--trust-remote-code""",
         """--cache-dir""",
         """/tmp""",
     ]  ,						)
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        import transformers.commands.transformers_cli

        # # remove any previously downloaded model to start clean
        shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""  ,						ignore_errors=__UpperCamelCase  )

        # run the command
        transformers.commands.transformers_cli.main()

        # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
        self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""  )  )
        self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""  )  )
        self.assertTrue(
            os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""  )  )





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							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  ):
              UpperCamelCase_											=							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
      _A							=       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
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

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



