
"""simple docstring"""


import datasets

from .evaluate import evaluate


_lowercase							      = '''\
@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}
}
'''

_lowercase							      = '''
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.
'''

_lowercase							      = '''
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  lowerCAmelCase_ (					datasets.Metric			):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Any     ,A_   :					Tuple					)   ->      Any:
							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

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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__A				=1_6
__A				=3_2





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 1_6    ):
				lowerCamelCase_						=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				lowerCamelCase_						=			load_dataset("glue"	, "mrpc"    )

				def tokenize_function(lowerCamelCase__    ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase_						=			tokenizer(examples["sentence1"]	, examples["sentence2"]	, truncation=lowerCamelCase__	, max_length=lowerCamelCase__    )
								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():
								lowerCamelCase_						=			datasets.map(
								    lowerCamelCase__	, batched=lowerCamelCase__	, 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
				lowerCamelCase_						=			tokenized_datasets.rename_column("label"	, "labels"    )

				def collate_fn(lowerCamelCase__    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase_						=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCamelCase_						=			1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_						=			8
								else:
												lowerCamelCase_						=			None

								return tokenizer.pad(
								    lowerCamelCase__	, padding="longest"	, max_length=lowerCamelCase__	, pad_to_multiple_of=lowerCamelCase__	, return_tensors="pt"	, )

				# Instantiate dataloaders.
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["train"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["validation"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )

				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

			__A				=mocked_dataloaders  # noqa: F811





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"	, lowerCamelCase__    ) == "1":
								lowerCamelCase_						=			2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCamelCase_						=			Accelerator(
								    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with="all"	, project_dir=args.project_dir    )
				else:
								lowerCamelCase_						=			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_						=			config["lr"]
				lowerCamelCase_						=			int(config["num_epochs"]    )
				lowerCamelCase_						=			int(config["seed"]    )
				lowerCamelCase_						=			int(config["batch_size"]    )
				set_seed(lowerCamelCase__    )

				lowerCamelCase_	,    lowerCamelCase_						=			get_dataloaders(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			evaluate.load("glue"	, "mrpc"    )

				# If the batch size is too big we use gradient accumulation
				lowerCamelCase_						=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCamelCase_						=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCamelCase_						=			MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase_						=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	, return_dict=lowerCamelCase__    )

				# 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).
				lowerCamelCase_						=			model.to(accelerator.device    )

				# Instantiate optimizer
				lowerCamelCase_						=			AdamW(params=model.parameters()	, lr=lowerCamelCase__    )

				# Instantiate scheduler
				lowerCamelCase_						=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__    ) * num_epochs) // gradient_accumulation_steps	, )

				# 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.
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			accelerator.prepare(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCamelCase_						=			os.path.split(lowerCamelCase__    )[-1].split("."    )[0]
								accelerator.init_trackers(lowerCamelCase__	, lowerCamelCase__    )

				# Now we train the model
				for epoch in range(lowerCamelCase__    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCamelCase_						=			0
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCamelCase_						=			loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase__    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								lowerCamelCase_						=			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	, lowerCamelCase__    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase__    ),
												        "epoch": epoch,
												    }	, step=lowerCamelCase__	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"	, type=lowerCamelCase__	, default=lowerCamelCase__	, 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."	, )
				parser.add_argument("--cpu"	, action="store_true"	, help="If passed, will train on the CPU."    )
				parser.add_argument(
				    "--with_tracking"	, action="store_true"	, help="Whether to load in all available experiment trackers from the environment and use them for logging."	, )
				parser.add_argument(
				    "--project_dir"	, type=lowerCamelCase__	, default="logs"	, help="Location on where to store experiment tracking logs` and relevent project information"	, )
				lowerCamelCase_						=			parser.parse_args()
				lowerCamelCase_						=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			main()


