"""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


__UpperCamelCase									=			'''true'''







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=82						,					UpperCAmelCase=16					)				->      List[str]:
     set_seed(42					)
     snake_case_					       =					RegressionModel()
     snake_case_					       =					deepcopy(UpperCAmelCase					)
     snake_case_					       =					RegressionDataset(length=UpperCAmelCase					)
     snake_case_					       =					DataLoader(UpperCAmelCase						,					batch_size=UpperCAmelCase					)
     model.to(accelerator.device					)
     snake_case_       ,	snake_case_					       =					accelerator.prepare(UpperCAmelCase						,					UpperCAmelCase					)
     return model, ddp_model, dataloader







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=False					)				->      Dict:
     snake_case_					       =					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'					)
     snake_case_					       =					load_dataset('glue'						,					'mrpc'						,					split='validation'					)

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

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

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







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







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=82						,					UpperCAmelCase=False						,					UpperCAmelCase=False						,					UpperCAmelCase=16					)				->      Union[str, Any]:
     snake_case_       ,	snake_case_       ,	snake_case_					       =					get_basic_setup(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
     snake_case_       ,	snake_case_					       =					generate_predictions(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
     assert (
         len(UpperCAmelCase					) == num_samples
     ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase					)}'







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

     # Then do distributed
     snake_case_       ,	snake_case_       ,	snake_case_					       =					setup['ddp']
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               snake_case_					       =					model(**UpperCAmelCase					)
          snake_case_					       =					outputs.logits.argmax(dim=-1					)
          snake_case_					       =					batch['labels']
          snake_case_       ,	snake_case_					       =					accelerator.gather_for_metrics((preds, references)					)
          metric.add_batch(predictions=UpperCAmelCase						,					references=UpperCAmelCase					)
     snake_case_					       =					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 					UpperCAmelCase    (		)				->      Optional[int]:
     snake_case_					       =					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]:
               snake_case_					       =					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**'					)
     snake_case_					       =					Accelerator()
     test_torch_metrics(UpperCAmelCase						,					512					)
     accelerator.state._reset_state()







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Tuple:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
    main()








'''simple docstring'''







from ....utils import logging


lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					int					,				__a     :					Any=None					,				__a     :					Optional[int]=20_48					):
								_a								= config.__dict__
								_a								= modal_hidden_size
								if num_labels:
												_a								= num_labels
