






"""simple docstring"""






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def 					_A			(__a    ,				__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    for param, grad_param in zip(model_a.parameters()    ,				model_b.parameters()							):
        if not param.requires_grad:
            continue
        if not did_step:
            # Grads should not be in sync
            assert (
                torch.allclose(param.grad    ,				grad_param.grad							) is False
            ), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
        else:
            # Grads should be in sync
            assert (
                torch.allclose(param.grad    ,				grad_param.grad							) is True
            ), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a=True							) ->       List[Any]:
    """simple docstring"""






    model.train()
    SCREAMING_SNAKE_CASE_      :  str    						=		model(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		F.mse_loss(__a    ,				target.to(output.device							)							)
    if not do_backward:
        loss /= accelerator.gradient_accumulation_steps
        loss.backward()
    else:
        accelerator.backward(__a							)



def 					_A			(__a    ,				__a=False							) ->       Tuple:
    """simple docstring"""






    set_seed(42							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		RegressionModel()
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		deepcopy(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		RegressionDataset(length=80							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		DataLoader(__a    ,				batch_size=16							)
    model.to(accelerator.device							)
    if sched:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		AdamW(params=model.parameters()    ,				lr=1e-3							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		AdamW(params=ddp_model.parameters()    ,				lr=1e-3							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		LambdaLR(__a    ,				lr_lambda=lambda __a							: epoch**0.65							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		LambdaLR(__a    ,				lr_lambda=lambda __a							: epoch**0.65							)
    # Make a copy of `model`
    if sched:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.prepare(__a    ,				__a    ,				__a    ,				__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  int    						=		accelerator.prepare(__a    ,				__a							)
    if sched:
        return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
    return model, ddp_model, dataloader



def 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_training_setup(__a							)
    # Use a single batch
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		next(iter(__a							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		accelerator.gather((ddp_input, ddp_target)							)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__a    ,				__a    ,				__a    ,				__a							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__a							):
                step_model(__a    ,				__a    ,				__a    ,				__a							)
        else:
            # Sync grads
            step_model(__a    ,				__a    ,				__a    ,				__a							)

        # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
        check_model_parameters(__a    ,				__a    ,				__a    ,				__a							)
        for param, ddp_param in zip(model.parameters()    ,				ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            assert torch.allclose(
                param.grad    ,				ddp_param.grad							), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
        torch.manual_seed(13_37 + iteration							)
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		ddp_input[torch.randperm(len(__a							)							)]



def 					_A			(__a							) ->       Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		get_training_setup(__a							)
    # Use a single batch
    SCREAMING_SNAKE_CASE_      :  Any    						=		next(iter(__a							)							).values()
    for iteration in range(3							):
        # Gather the distributed inputs and targs for the base model
        SCREAMING_SNAKE_CASE_      :  Any    						=		accelerator.gather((ddp_input, ddp_target)							)
        SCREAMING_SNAKE_CASE_      :  int    						=		input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__a    ,				__a    ,				__a    ,				__a							)
        # Do "gradient accumulation" (noop)
        if iteration % 2 == 0:
            # Accumulate grads locally
            with accelerator.no_sync(__a							):
                step_model(__a    ,				__a    ,				__a    ,				__a							)
        else:
            # Sync grads
            step_model(__a    ,				__a    ,				__a    ,				__a							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,				ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if iteration % 2 == 0:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,				ddp_param.grad							) is False
                ), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
            else:
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,				ddp_param.grad							) is True
                ), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

                    # Shuffle ddp_input on each iteration
        torch.manual_seed(13_37 + iteration							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		ddp_input[torch.randperm(len(__a							)							)]



def 					_A			(__a=False    ,				__a=False							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Accelerator(
        split_batches=__a    ,				dispatch_batches=__a    ,				gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		get_training_setup(__a							)
    for iteration, batch in enumerate(__a							):
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		batch.values()
        # Gather the distributed inputs and targs for the base model
        SCREAMING_SNAKE_CASE_      :  str    						=		accelerator.gather((ddp_input, ddp_target)							)
        SCREAMING_SNAKE_CASE_      :  int    						=		input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        step_model(__a    ,				__a    ,				__a    ,				__a    ,				__a							)
        # Do "gradient accumulation" (noop)
        with accelerator.accumulate(__a							):
            step_model(__a    ,				__a    ,				__a    ,				__a							)

        # DDP model and model should only be in sync when not (iteration % 2 == 0)
        for param, ddp_param in zip(model.parameters()    ,				ddp_model.parameters()							):
            if not param.requires_grad:
                continue
            if ((iteration + 1) % 2 == 0) or (iteration == len(__a							) - 1):
                # Grads should be in sync
                assert (
                    torch.allclose(param.grad    ,				ddp_param.grad							) is True
                ), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
            else:
                # Grads should not be in sync
                assert (
                    torch.allclose(param.grad    ,				ddp_param.grad							) is False
                ), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

                    # Shuffle ddp_input on each iteration
        torch.manual_seed(13_37 + iteration							)
        SCREAMING_SNAKE_CASE_      :  str    						=		ddp_input[torch.randperm(len(__a							)							)]
    GradientState._reset_state()



def 					_A			(__a=False    ,				__a=False							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Accelerator(
        split_batches=__a    ,				dispatch_batches=__a    ,				gradient_accumulation_steps=2							)
    # Test that context manager behaves properly
    SCREAMING_SNAKE_CASE_      :  Dict    						=		get_training_setup(__a    ,				__a							)
    for iteration, batch in enumerate(__a							):
        SCREAMING_SNAKE_CASE_      :  Any    						=		batch.values()
        # Gather the distributed inputs and targs for the base model
        SCREAMING_SNAKE_CASE_      :  Any    						=		accelerator.gather((ddp_input, ddp_target)							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		input.to(accelerator.device							), target.to(accelerator.device							)
        # Perform our initial ground truth step in non "DDP"
        model.train()
        ddp_model.train()
        step_model(__a    ,				__a    ,				__a    ,				__a    ,				__a							)
        opt.step()

        if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__a							)):
            if split_batches:
                sched.step()
            else:
                for _ in range(accelerator.num_processes							):
                    sched.step()
        opt.zero_grad()
        # Perform gradient accumulation under wrapper
        with accelerator.accumulate(__a							):
            step_model(__a    ,				__a    ,				__a    ,				__a							)
            ddp_opt.step()
            ddp_sched.step()
            ddp_opt.zero_grad()

        # Learning rates should be the same
        assert (
            opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
        ), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__a							))
        if accelerator.num_processes > 1:
            check_model_parameters(__a    ,				__a    ,				__a    ,				__a							)
        # Shuffle ddp_input on each iteration
        torch.manual_seed(13_37 + iteration							)
    GradientState._reset_state()



def 					_A			() ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		Accelerator()

    SCREAMING_SNAKE_CASE_      :  Any    						=		RegressionDataset(length=80							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		DataLoader(__a    ,				batch_size=16							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		RegressionDataset(length=96							)
    SCREAMING_SNAKE_CASE_      :  str    						=		DataLoader(__a    ,				batch_size=16							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		accelerator.prepare(__a    ,				__a							)
    assert accelerator.gradient_state.active_dataloader is None
    for iteration, _ in enumerate(__a							):
        assert id(accelerator.gradient_state.active_dataloader							) == id(__a							)
        if iteration < len(__a							) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
            if iteration == 1:
                for batch_num, _ in enumerate(__a							):
                    assert id(accelerator.gradient_state.active_dataloader							) == id(__a							)
                    if batch_num < len(__a							) - 1:
                        assert not accelerator.gradient_state.end_of_dataloader
                    else:
                        assert accelerator.gradient_state.end_of_dataloader
        else:
            assert accelerator.gradient_state.end_of_dataloader
    assert accelerator.gradient_state.active_dataloader is None



def 					_A			() ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		Accelerator()
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		accelerator.state
    if state.local_process_index == 0:
        print('''**Test `accumulate` gradient accumulation with dataloader break**'''							)
    test_dataloader_break()
    if state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print('''**Test NOOP `no_sync` context manager**'''							)
        test_noop_sync(__a							)
    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
        if state.local_process_index == 0:
            print('''**Test Distributed `no_sync` context manager**'''							)
        test_distributed_sync(__a							)
    if state.distributed_type == DistributedType.MULTI_GPU:
        for split_batch in [True, False]:
            for dispatch_batches in [True, False]:
                if state.local_process_index == 0:
                    print(
                        '''**Test `accumulate` gradient accumulation, '''    ,				f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,				)
                test_gradient_accumulation(__a    ,				__a							)

                # Currently will break on torch 2.0 +, need to investigate why
    if is_torch_version('''<'''    ,				'''2.0'''							) or state.distributed_type == DistributedType.NO:
        if state.local_process_index == 0:
            print(
                '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''    ,				'''`split_batches=False`, `dispatch_batches=False`**'''    ,				)
        test_gradient_accumulation_with_opt_and_scheduler()
        if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                for dispatch_batches in [True, False]:
                    if not split_batch and not dispatch_batches:
                        continue
                    if state.local_process_index == 0:
                        print(
                            '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''    ,				f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,				)
                    test_gradient_accumulation_with_opt_and_scheduler(__a    ,				__a							)



def 					_A			(__a							) ->       Optional[Any]:
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()







"""simple docstring"""
import datasets

from .evaluate import evaluate


A:   Optional[Any]								=		"\\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"

A:   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"

A:   int								=		"\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       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    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      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
    UpperCAmelCase	:					Tuple	=	[
        {
            """paragraphs""": [
                {
                    """qas""": [
                        {
                            """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                            """id""": ref["""id"""],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    UpperCAmelCase	:					Optional[Any]	=	evaluate(dataset=_SCREAMING_SNAKE_CASE      ,     predictions=_SCREAMING_SNAKE_CASE						)
    return score
