



import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
    from transformers import MarkupLMFeatureExtractor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Union[str, Any]						     =     parent






       def  snake_case_	(      self	)					->		List[Any]:
          return {}






def 		UpperCamelCase     (       )     ->					Optional[Any]:
   UpperCamelCase   :  str						     =     '<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

   UpperCamelCase   :  Any						     =     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]



@require_bsa
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : int       					=  MarkupLMFeatureExtractor if is_bsa_available() else None
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Any						     =     MarkupLMFeatureExtractionTester(self	)
       @property
       def  snake_case_	(      self	)					->		int:
          return self.feature_extract_tester.prepare_feat_extract_dict()






       def  snake_case_	(      self	)					->		Union[str, Any]:
          # Initialize feature_extractor
          UpperCamelCase   :  int						     =     self.feature_extraction_class()

          # Test not batched input
          UpperCamelCase   :  Optional[Any]						     =     get_html_strings()[0]
          UpperCamelCase   :  Any						     =     feature_extractor(SCREAMING_SNAKE_CASE_	)

          # fmt: off
          UpperCamelCase   :  Any						     =     [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
          UpperCamelCase   :  Union[str, Any]						     =     [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
          # fmt: on

          self.assertEqual(encoding.nodes,      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(encoding.xpaths,      SCREAMING_SNAKE_CASE_	)

          # Test batched
          UpperCamelCase   :  List[str]						     =     get_html_strings()
          UpperCamelCase   :  Tuple						     =     feature_extractor(SCREAMING_SNAKE_CASE_	)

          # fmt: off
          UpperCamelCase   :  str						     =     expected_nodes + [['My First Heading', 'My first paragraph.']]
          UpperCamelCase   :  Tuple						     =     expected_xpaths + [['/html/body/h1', '/html/body/p']]

          self.assertEqual(len(encoding.nodes	),      2	)
          self.assertEqual(len(encoding.xpaths	),      2	)

          self.assertEqual(encoding.nodes,      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(encoding.xpaths,      SCREAMING_SNAKE_CASE_	)








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
    __UpperCAmelCase	       =	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''')
    __UpperCAmelCase       ,  __UpperCAmelCase	       =	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''')
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
        )
    else:
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    __UpperCAmelCase	       =	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)



