


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


__lowerCAmelCase		       =							'''true'''




def    snake_case_     (  snake_case			,							snake_case=82			,							snake_case=16     )  ->				Optional[Any]:
       set_seed(42     )
       lowercase__:   List[Any]  			=   RegressionModel()
       lowercase__:   Any  			=   deepcopy(__lowercase     )
       lowercase__:   int  			=   RegressionDataset(length=__lowercase     )
       lowercase__:   Optional[int]  			=   DataLoader(__lowercase			,							batch_size=__lowercase     )
       model.to(accelerator.device     )
       lowercase__			,						lowercase__:   Any  			=   accelerator.prepare(__lowercase			,							__lowercase     )
       return model, ddp_model, dataloader




def    snake_case_     (  snake_case			,							snake_case=False     )  ->				int:
       lowercase__:   Union[str, Any]  			=   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'     )
       lowercase__:   Dict  			=   load_dataset('glue'			,							'mrpc'			,							split='validation'     )

       def tokenize_function(snake_case     ):
              lowercase__:   Union[str, Any]  			=   tokenizer(examples['sentence1']			,							examples['sentence2']			,							truncation=__lowercase			,							max_length=__lowercase     )
              return outputs

       with accelerator.main_process_first():
              lowercase__:   Dict  			=   dataset.map(
                  __lowercase			,							batched=__lowercase			,							remove_columns=['idx', 'sentence1', 'sentence2']			,							)

       lowercase__:   List[Any]  			=   tokenized_datasets.rename_column('label'			,							'labels'     )

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

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




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




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




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




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

       # Then do distributed
       lowercase__			,						lowercase__			,						lowercase__:   Optional[int]  			=   setup['ddp']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     lowercase__:   int  			=   model(**__lowercase     )
              lowercase__:   int  			=   outputs.logits.argmax(dim=-1     )
              lowercase__:   str  			=   batch['labels']
              lowercase__			,						lowercase__:   Tuple  			=   accelerator.gather_for_metrics((preds, references)     )
              metric.add_batch(predictions=__lowercase			,							references=__lowercase     )
       lowercase__:   int  			=   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_     (  )  ->				int:
       lowercase__:   Dict  			=   Accelerator(split_batches=__lowercase			,							dispatch_batches=__lowercase     )
       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(__lowercase			,							__lowercase     )
                            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]:
                     lowercase__:   Union[str, Any]  			=   Accelerator(split_batches=__lowercase			,							dispatch_batches=__lowercase     )
                     if accelerator.is_local_main_process:
                            print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'     )
                     test_torch_metrics(__lowercase			,							99     )
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('**Test last batch is not dropped when perfectly divisible**'     )
       lowercase__:   Tuple  			=   Accelerator()
       test_torch_metrics(__lowercase			,							5_12     )
       accelerator.state._reset_state()




def    snake_case_     (  snake_case     )  ->				Any:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
 main()




def        a_		(       __lowercase   :	str ) ->  int:
			_snake_case		=			hex_num.strip()
			if not hex_num:
						raise ValueError('No value was passed to the function' )

			_snake_case		=			hex_num[0] == '-'
			if is_negative:
						_snake_case		=			hex_num[1:]

			try:
						_snake_case		=			int(__lowercase       ,					16 )
			except ValueError:
						raise ValueError('Invalid value was passed to the function' )

			_snake_case		=			''
			while int_num > 0:
						_snake_case		=			str(int_num % 2 ) + bin_str
						int_num >>= 1

			return int(('-' + bin_str) if is_negative else bin_str )


if __name__ == "__main__":
			import doctest

			doctest.testmod()