


'''simple docstring'''






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


a_   :      str				    =      "true"






def 		_A			(lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Tuple=82			,  lowerCAmelCase__					:str=16						)				->					Union[str, Any]:




     '''simple docstring'''



     set_seed(42						)
     _a      =							RegressionModel()
     _a      =							deepcopy(lowerCAmelCase__						)
     _a      =							RegressionDataset(length=lowerCAmelCase__						)
     _a      =							DataLoader(lowerCAmelCase__			,  batch_size=lowerCAmelCase__						)
     model.to(accelerator.device						)
     _a ,				_a      =							accelerator.prepare(lowerCAmelCase__			,  lowerCAmelCase__						)
     return model, ddp_model, dataloader






def 		_A			(lowerCAmelCase__					:Accelerator			,  lowerCAmelCase__					:Tuple=False						)				->					List[Any]:




     '''simple docstring'''



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

     def tokenize_function(lowerCAmelCase__					:List[str]						):
          _a      =							tokenizer(examples['sentence1']			,  examples['sentence2']			,  truncation=lowerCAmelCase__			,  max_length=lowerCAmelCase__						)
          return outputs

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

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

     def collate_fn(lowerCAmelCase__					:Any						):
          if use_longest:
               return tokenizer.pad(lowerCAmelCase__			,  padding='longest'			,  return_tensors='pt'						)
          return tokenizer.pad(lowerCAmelCase__			,  padding='max_length'			,  max_length=1_28			,  return_tensors='pt'						)

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






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:int						)				->					Union[str, Any]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:Tuple						)				->					List[Any]:




     '''simple docstring'''



     _a      =							[]
     for batch in dataloader:
          _a ,				_a      =							batch.values()
          with torch.no_grad():
               _a      =							model(lowerCAmelCase__						)
               _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(lowerCAmelCase__						)
          targs.append(lowerCAmelCase__						)
     _a ,				_a      =							torch.cat(lowerCAmelCase__						), torch.cat(lowerCAmelCase__						)
     return logits, targs






def 		_A			(lowerCAmelCase__					:Accelerator			,  lowerCAmelCase__					:Optional[int]=82			,  lowerCAmelCase__					:int=False			,  lowerCAmelCase__					:List[str]=False			,  lowerCAmelCase__					:Union[str, Any]=16						)				->					Dict:




     '''simple docstring'''



     _a ,				_a ,				_a      =							get_basic_setup(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
     _a ,				_a      =							generate_predictions(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
     assert (
         len(lowerCAmelCase__						) == num_samples
     ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__						)}'






def 		_A			(lowerCAmelCase__					:bool = False			,  lowerCAmelCase__					:bool = False						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							evaluate.load('glue'			,  'mrpc'						)
     _a ,				_a      =							get_mrpc_setup(lowerCAmelCase__			,  lowerCAmelCase__						)
     # First do baseline
     _a ,				_a ,				_a      =							setup['no']
     model.to(lowerCAmelCase__						)
     model.eval()
     for batch in dataloader:
          batch.to(lowerCAmelCase__						)
          with torch.inference_mode():
               _a      =							model(**lowerCAmelCase__						)
          _a      =							outputs.logits.argmax(dim=-1						)
          metric.add_batch(predictions=lowerCAmelCase__			,  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(**lowerCAmelCase__						)
          _a      =							outputs.logits.argmax(dim=-1						)
          _a      =							batch['labels']
          _a ,				_a      =							accelerator.gather_for_metrics((preds, references)						)
          metric.add_batch(predictions=lowerCAmelCase__			,  references=lowerCAmelCase__						)
     _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 		_A			()				->					Optional[int]:




     '''simple docstring'''



     _a      =							Accelerator(split_batches=lowerCAmelCase__			,  dispatch_batches=lowerCAmelCase__						)
     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(lowerCAmelCase__			,  lowerCAmelCase__						)
                    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=lowerCAmelCase__			,  dispatch_batches=lowerCAmelCase__						)
               if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'						)
               test_torch_metrics(lowerCAmelCase__			,  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(lowerCAmelCase__			,  5_12						)
     accelerator.state._reset_state()






def 		_A			(lowerCAmelCase__					:Any						)				->					int:




     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      int				    =      {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      [
     "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXJapaneseForCausalLM",
     "GPTNeoXJapaneseLayer",
     "GPTNeoXJapaneseModel",
     "GPTNeoXJapanesePreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 a_   :      int				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

