



"""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


lowerCAmelCase__      =    '''true'''






def 	snake_case_  (				A_ :		Optional[int],    A_ :		Optional[Any]=82,    A_ :		int=16						):


       '''simple docstring'''





       set_seed(42						)
       _lowerCamelCase						:    Union[str, Any]         =			RegressionModel()
       _lowerCamelCase						:    int         =			deepcopy(A_						)
       _lowerCamelCase						:    Tuple         =			RegressionDataset(length=A_						)
       _lowerCamelCase						:    str         =			DataLoader(A_,    batch_size=A_						)
       model.to(accelerator.device						)
       _lowerCamelCase      ,   _lowerCamelCase						:    int         =			accelerator.prepare(A_,    A_						)
       return model, ddp_model, dataloader






def 	snake_case_  (				A_ :		Accelerator,    A_ :		Optional[int]=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''						)
       _lowerCamelCase						:    Optional[Any]         =			load_dataset('''glue''',    '''mrpc''',    split='''validation'''						)

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

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

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

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

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






def 	snake_case_  (				A_ :		Any,    A_ :		Dict						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		str,    A_ :		List[str],    A_ :		Optional[int]						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Accelerator,    A_ :		Tuple=82,    A_ :		Optional[Any]=False,    A_ :		List[Any]=False,    A_ :		Any=16						):


       '''simple docstring'''





       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Optional[Any]         =			get_basic_setup(A_,    A_,    A_						)
       _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			generate_predictions(A_,    A_,    A_						)
       assert (
           len(A_						) == num_samples
       ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_						)}'''






def 	snake_case_  (				A_ :		bool = False,    A_ :		bool = False						):


       '''simple docstring'''





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

       # Then do distributed
       _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    Optional[int]         =			setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     _lowerCamelCase						:    Tuple         =			model(**A_						)
              _lowerCamelCase						:    List[str]         =			outputs.logits.argmax(dim=-1						)
              _lowerCamelCase						:    Tuple         =			batch['''labels''']
              _lowerCamelCase      ,   _lowerCamelCase						:    str         =			accelerator.gather_for_metrics((preds, references)						)
              metric.add_batch(predictions=A_,    references=A_						)
       _lowerCamelCase						:    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 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			Accelerator(split_batches=A_,    dispatch_batches=A_						)
       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(A_,    A_						)
                            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						:    int         =			Accelerator(split_batches=A_,    dispatch_batches=A_						)
                     if accelerator.is_local_main_process:
                            print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''						)
                     test_torch_metrics(A_,    99						)
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test last batch is not dropped when perfectly divisible**'''						)
       _lowerCamelCase						:    Optional[Any]         =			Accelerator()
       test_torch_metrics(A_,    5_12						)
       accelerator.state._reset_state()






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       main()


if __name__ == "__main__":
   main()







"""simple docstring"""






from maths.prime_factors import prime_factors






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       if not isinstance(A_,    A_						):
              _lowerCamelCase						:    str         =			F'''Input value of [number={number}] must be an integer'''
              raise TypeError(A_						)
       if number < 1:
              raise ValueError('''Input must be a positive integer'''						)
       return -1 if len(prime_factors(A_						)						) % 2 else 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()


