
import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE							:List[str]							 =				'\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				'\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				'\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the CUAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION  )
class 							UpperCAmelCase      (  datasets.Metric  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Tuple ):
					return datasets.MetricInfo(
					    description=_DESCRIPTION							,citation=_CITATION							,inputs_description=_KWARGS_DESCRIPTION							,features=datasets.Features(
					        {
					            "predictions": {
					                "id": datasets.Value("string" ),
					                "prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
					            },
					            "references": {
					                "id": datasets.Value("string" ),
					                "answers": datasets.features.Sequence(
					                    {
					                        "text": datasets.Value("string" ),
					                        "answer_start": datasets.Value("int32" ),
					                    } ),
					            },
					        } )							,codebase_urls=["https://www.atticusprojectai.org/cuad"]							,reference_urls=["https://www.atticusprojectai.org/cuad"]							,)







	def 				UpperCamelCase_  (       self					: Dict							,A					: Optional[int]							,A					: Dict ):
					__A   			= {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
					__A   			= [
					    {
					        "paragraphs": [
					            {
					                "qas": [
					                    {
					                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
					                        "id": ref["id"],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					__A   			= evaluate(dataset=A							,predictions=A )
					return score




'''simple docstring'''
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)
