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__				= 'true'
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=82 ,						__UpperCAmelCase=16		)						->    Union[str, Any]:



      '''simple docstring'''

      set_seed(42		)
      _lowerCamelCase			    =				RegressionModel()
      _lowerCamelCase			    =				deepcopy(__UpperCAmelCase		)
      _lowerCamelCase			    =				RegressionDataset(length=__UpperCAmelCase		)
      _lowerCamelCase			    =				DataLoader(__UpperCAmelCase ,						batch_size=__UpperCAmelCase		)
      model.to(accelerator.device		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				accelerator.prepare(__UpperCAmelCase ,						__UpperCAmelCase		)
      return model, ddp_model, dataloader
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
      _lowerCamelCase			    =				load_dataset('''glue''' ,						'''mrpc''' ,						split='''validation'''		)

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

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

      _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    str:



      '''simple docstring'''

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



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for batch in dataloader:
            _lowerCamelCase    ,					_lowerCamelCase			    =				batch.values()
            with torch.no_grad():
                  _lowerCamelCase			    =				model(__UpperCAmelCase		)
                  _lowerCamelCase    ,					_lowerCamelCase			    =				accelerator.gather_for_metrics((logit, target)		)
                  logits_and_targets.append((logit, target)		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				[], []
      for logit, targ in logits_and_targets:
            logits.append(__UpperCAmelCase		)
            targs.append(__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				torch.cat(__UpperCAmelCase		), torch.cat(__UpperCAmelCase		)
      return logits, targs
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=82 ,						__UpperCAmelCase=False ,						__UpperCAmelCase=False ,						__UpperCAmelCase=16		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				get_basic_setup(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				generate_predictions(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      assert (
          len(__UpperCAmelCase		) == num_samples
      ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCAmelCase		)}'
def       __magic_name__(  __UpperCAmelCase = False ,						__UpperCAmelCase = False		)						->    Dict:



      '''simple docstring'''

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

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



      '''simple docstring'''

      _lowerCamelCase			    =				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]:
                  _lowerCamelCase			    =				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**'''		)
      _lowerCamelCase			    =				Accelerator()
      test_torch_metrics(__UpperCAmelCase ,						512		)
      accelerator.state._reset_state()
def       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      main()


if __name__ == "__main__":
     main()
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)