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_    					=       'true'





def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str=82  ,  lowerCamelCase					:     Tuple=16	):
 set_seed(42	)
 UpperCamelCase_					:						List[Any] =					RegressionModel()
 UpperCamelCase_					:						List[Any] =					deepcopy(lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					RegressionDataset(length=lowerCamelCase	)
 UpperCamelCase_					:						str =					DataLoader(lowerCamelCase  ,  batch_size=lowerCamelCase	)
 model.to(accelerator.device	)
 UpperCamelCase_, UpperCamelCase_					:						Any =					accelerator.prepare(lowerCamelCase  ,  lowerCamelCase	)
 return model, ddp_model, dataloader





def    __lowercase     (						lowerCamelCase					:     Accelerator  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						List[Any] =					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'	)
 UpperCamelCase_					:						Optional[int] =					load_dataset('glue'  ,  'mrpc'  ,  split='validation'	)

 def tokenize_function(lowerCamelCase					:     Optional[int]	):
  UpperCamelCase_					:						int =					tokenizer(examples['sentence1']  ,  examples['sentence2']  ,  truncation=lowerCamelCase  ,  max_length=lowerCamelCase	)
  return outputs

 with accelerator.main_process_first():
  UpperCamelCase_					:						str =					dataset.map(
      lowerCamelCase  ,  batched=lowerCamelCase  ,  remove_columns=['idx', 'sentence1', 'sentence2']  ,  )

 UpperCamelCase_					:						Any =					tokenized_datasets.rename_column('label'  ,  'labels'	)

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

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





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





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						Union[str, Any] =					[]
 for batch in dataloader:
  UpperCamelCase_, UpperCamelCase_					:						List[str] =					batch.values()
  with torch.no_grad():
   UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
   UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					accelerator.gather_for_metrics((logit, target)	)
   logits_and_targets.append((logit, target)	)
 UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					[], []
 for logit, targ in logits_and_targets:
  logits.append(lowerCamelCase	)
  targs.append(lowerCamelCase	)
 UpperCamelCase_, UpperCamelCase_					:						Dict =					torch.cat(lowerCamelCase	), torch.cat(lowerCamelCase	)
 return logits, targs





def    __lowercase     (						lowerCamelCase					:     Accelerator  ,  lowerCamelCase					:     List[str]=82  ,  lowerCamelCase					:     Tuple=False  ,  lowerCamelCase					:     str=False  ,  lowerCamelCase					:     Union[str, Any]=16	):
 UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						int =					get_basic_setup(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_, UpperCamelCase_					:						List[str] =					generate_predictions(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 assert (
     len(lowerCamelCase	) == num_samples
 ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase	)}"





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

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





def    __lowercase     (						lowerCamelCase					:     Tuple	):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
   main()




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



