



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__snake_case						:Dict						=					"""true"""





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=82       ,			_UpperCAmelCase=16    ):
    set_seed(42    )
    __a							=    RegressionModel()
    __a							=    deepcopy(_UpperCAmelCase    )
    __a							=    RegressionDataset(length=_UpperCAmelCase    )
    __a							=    DataLoader(_UpperCAmelCase       ,			batch_size=_UpperCAmelCase    )
    model.to(accelerator.device    )
    __a	,       __a							=    accelerator.prepare(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return model, ddp_model, dataloader





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=False    ):
    __a							=    AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''    )
    __a							=    load_dataset('''glue'''       ,			'''mrpc'''       ,			split='''validation'''    )

    def tokenize_function(_UpperCAmelCase    ):
        __a							=    tokenizer(examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCAmelCase       ,			max_length=_UpperCAmelCase    )
        return outputs

    with accelerator.main_process_first():
        __a							=    dataset.map(
            _UpperCAmelCase       ,			batched=_UpperCAmelCase       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			)

    __a							=    tokenized_datasets.rename_column('''label'''       ,			'''labels'''    )

    def collate_fn(_UpperCAmelCase    ):
        if use_longest:
            return tokenizer.pad(_UpperCAmelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''    )
        return tokenizer.pad(_UpperCAmelCase       ,			padding='''max_length'''       ,			max_length=128       ,			return_tensors='''pt'''    )

    return DataLoader(_UpperCAmelCase       ,			shuffle=_UpperCAmelCase       ,			collate_fn=_UpperCAmelCase       ,			batch_size=16    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    Accelerator(dispatch_batches=_UpperCAmelCase       ,			split_batches=_UpperCAmelCase    )
    __a							=    get_dataloader(_UpperCAmelCase       ,			not dispatch_batches    )
    __a							=    AutoModelForSequenceClassification.from_pretrained(
        '''hf-internal-testing/mrpc-bert-base-cased'''       ,			return_dict=_UpperCAmelCase    )
    __a	,       __a							=    accelerator.prepare(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    []
    for batch in dataloader:
        __a	,       __a							=    batch.values()
        with torch.no_grad():
            __a							=    model(_UpperCAmelCase    )
            __a	,       __a							=    accelerator.gather_for_metrics((logit, target)    )
            logits_and_targets.append((logit, target)    )
    __a	,       __a							=    [], []
    for logit, targ in logits_and_targets:
        logits.append(_UpperCAmelCase    )
        targs.append(_UpperCAmelCase    )
    __a	,       __a							=    torch.cat(_UpperCAmelCase    ), torch.cat(_UpperCAmelCase    )
    return logits, targs





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=82       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=16    ):
    __a	,       __a	,       __a							=    get_basic_setup(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a	,       __a							=    generate_predictions(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
    assert (
        len(_UpperCAmelCase    ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase    )}'





def 					__snake_case     (      _UpperCAmelCase = False       ,			_UpperCAmelCase = False    ):
    __a							=    evaluate.load('''glue'''       ,			'''mrpc'''    )
    __a	,       __a							=    get_mrpc_setup(_UpperCAmelCase       ,			_UpperCAmelCase    )
    # First do baseline
    __a	,       __a	,       __a							=    setup['''no''']
    model.to(_UpperCAmelCase    )
    model.eval()
    for batch in dataloader:
        batch.to(_UpperCAmelCase    )
        with torch.inference_mode():
            __a							=    model(**_UpperCAmelCase    )
        __a							=    outputs.logits.argmax(dim=-1    )
        metric.add_batch(predictions=_UpperCAmelCase       ,			references=batch['''labels''']    )
    __a							=    metric.compute()

    # Then do distributed
    __a	,       __a	,       __a							=    setup['''ddp''']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            __a							=    model(**_UpperCAmelCase    )
        __a							=    outputs.logits.argmax(dim=-1    )
        __a							=    batch['''labels''']
        __a	,       __a							=    accelerator.gather_for_metrics((preds, references)    )
        metric.add_batch(predictions=_UpperCAmelCase       ,			references=_UpperCAmelCase    )
    __a							=    metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]       ,			distributed[key]    ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'





def 					__snake_case     (      ):
    __a							=    Accelerator(split_batches=_UpperCAmelCase       ,			dispatch_batches=_UpperCAmelCase    )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('''**Testing gather_for_metrics**'''    )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'    )
                test_mrpc(_UpperCAmelCase       ,			_UpperCAmelCase    )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('''**Test torch metrics**'''    )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            __a							=    Accelerator(split_batches=_UpperCAmelCase       ,			dispatch_batches=_UpperCAmelCase    )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'    )
            test_torch_metrics(_UpperCAmelCase       ,			99    )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('''**Test last batch is not dropped when perfectly divisible**'''    )
    __a							=    Accelerator()
    test_torch_metrics(_UpperCAmelCase       ,			512    )
    accelerator.state._reset_state()





def 					__snake_case     (      _UpperCAmelCase    ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





