






import datasets

from .evaluate import evaluate


snake_case__   :						int													=   """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

snake_case__   :						Union[str, Any]													=   """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

snake_case__   :						Any													=   """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> 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'}]
    >>> 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'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""


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






  """simple docstring"""





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













import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case__   :						Dict													=   1_6
snake_case__   :						List[str]													=   3_2






def      _snake_case     (__lowercase     ,   __lowercase = 16):
   UpperCamelCase_						=  AutoTokenizer.from_pretrained('bert-base-cased')
   UpperCamelCase_						=  load_dataset('glue'     ,   'mrpc')

   def tokenize_function(__lowercase):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase_						=  tokenizer(examples['sentence1']     ,   examples['sentence2']     ,   truncation=__lowercase     ,   max_length=__lowercase)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCamelCase_						=  datasets.map(
          __lowercase     ,   batched=__lowercase     ,   remove_columns=['idx', 'sentence1', 'sentence2']     ,   )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   UpperCamelCase_						=  tokenized_datasets.rename_column('label'     ,   'labels')

   def collate_fn(__lowercase):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase_						=  128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCamelCase_						=  16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase_						=  8
      else:
         UpperCamelCase_						=  None

      return tokenizer.pad(
          __lowercase     ,   padding='longest'     ,   max_length=__lowercase     ,   pad_to_multiple_of=__lowercase     ,   return_tensors='pt'     ,   )

   # Instantiate dataloaders.
   UpperCamelCase_						=  DataLoader(
       tokenized_datasets['train']     ,   shuffle=__lowercase     ,   collate_fn=__lowercase     ,   batch_size=__lowercase)
   UpperCamelCase_						=  DataLoader(
       tokenized_datasets['validation']     ,   shuffle=__lowercase     ,   collate_fn=__lowercase     ,   batch_size=__lowercase)

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 snake_case__   :						List[str]													=   mocked_dataloaders  # noqa: F811






def      _snake_case     (__lowercase     ,   __lowercase):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,   __lowercase) == "1":
      UpperCamelCase_						=  2
   # New Code #
   UpperCamelCase_						=  int(args.gradient_accumulation_steps)
   # Initialize accelerator
   UpperCamelCase_						=  Accelerator(
       cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=__lowercase)
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`')
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase_						=  config['lr']
   UpperCamelCase_						=  int(config['num_epochs'])
   UpperCamelCase_						=  int(config['seed'])
   UpperCamelCase_						=  int(config['batch_size'])

   UpperCamelCase_						=  evaluate.load('glue'     ,   'mrpc')

   set_seed(__lowercase)
   UpperCamelCase_		,   UpperCamelCase_						=  get_dataloaders(__lowercase     ,   __lowercase)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase_						=  AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,   return_dict=__lowercase)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCamelCase_						=  model.to(accelerator.device)

   # Instantiate optimizer
   UpperCamelCase_						=  AdamW(params=model.parameters()     ,   lr=__lowercase)

   # Instantiate scheduler
   UpperCamelCase_						=  get_linear_schedule_with_warmup(
       optimizer=__lowercase     ,   num_warmup_steps=100     ,   num_training_steps=(len(__lowercase) * num_epochs)     ,   )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  accelerator.prepare(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # Now we train the model
   for epoch in range(__lowercase):
      model.train()
      for step, batch in enumerate(__lowercase):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         # New code #
         # We use the new `accumulate` context manager to perform gradient accumulation
         # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
         with accelerator.accumulate(__lowercase):
            UpperCamelCase_						=  model(**__lowercase)
            UpperCamelCase_						=  output.loss
            accelerator.backward(__lowercase)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(__lowercase):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         with torch.no_grad():
            UpperCamelCase_						=  model(**__lowercase)
         UpperCamelCase_						=  outputs.logits.argmax(dim=-1)
         UpperCamelCase_		,   UpperCamelCase_						=  accelerator.gather_for_metrics((predictions, batch['labels']))
         metric.add_batch(
             predictions=__lowercase     ,   references=__lowercase     ,   )

      UpperCamelCase_						=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""     ,   __lowercase)






def      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Simple example of training script.')
   parser.add_argument(
       '--mixed_precision'     ,   type=__lowercase     ,   default=__lowercase     ,   choices=['no', 'fp16', 'bf16', 'fp8']     ,   help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'     ,   )
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'     ,   type=__lowercase     ,   default=1     ,   help='The number of minibatches to be ran before gradients are accumulated.'     ,   )
   parser.add_argument('--cpu'     ,   action='store_true'     ,   help='If passed, will train on the CPU.')
   UpperCamelCase_						=  parser.parse_args()
   UpperCamelCase_						=  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(__lowercase     ,   __lowercase)


if __name__ == "__main__":
 main()





