




'''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_      :  int				     =  'true'



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					List[str]=82 ,    UpperCamelCase__    :					str=16				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Accelerator ,    UpperCamelCase__    :					Optional[Any]=False				):




     """simple docstring"""


     a_     :							List[Any]     						=  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""				)
     a_     :							int     						=  load_dataset("""glue""" ,    """mrpc""" ,    split="""validation"""				)

     def tokenize_function(UpperCamelCase__    :					List[str]				):
          a_     :							List[str]     						=  tokenizer(examples["""sentence1"""] ,    examples["""sentence2"""] ,    truncation=UpperCamelCase__ ,    max_length=UpperCamelCase__				)
          return outputs

     with accelerator.main_process_first():
          a_     :							str     						=  dataset.map(
              UpperCamelCase__ ,    batched=UpperCamelCase__ ,    remove_columns=["""idx""", """sentence1""", """sentence2"""] ,    )

     a_     :							Any     						=  tokenized_datasets.rename_column("""label""" ,    """labels"""				)

     def collate_fn(UpperCamelCase__    :					Dict				):
          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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					List[Any]				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  []
     for batch in dataloader:
          a_						,     a_     :							Union[str, Any]     						=  batch.values()
          with torch.no_grad():
               a_     :							List[str]     						=  model(UpperCamelCase__				)
               a_						,     a_     :							int     						=  accelerator.gather_for_metrics((logit, target)				)
               logits_and_targets.append((logit, target)				)
     a_						,     a_     :							int     						=  [], []
     for logit, targ in logits_and_targets:
          logits.append(UpperCamelCase__				)
          targs.append(UpperCamelCase__				)
     a_						,     a_     :							str     						=  torch.cat(UpperCamelCase__				), torch.cat(UpperCamelCase__				)
     return logits, targs



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Accelerator ,    UpperCamelCase__    :					Union[str, Any]=82 ,    UpperCamelCase__    :					Any=False ,    UpperCamelCase__    :					Tuple=False ,    UpperCamelCase__    :					str=16				):




     """simple docstring"""


     a_						,     a_						,     a_     :							str     						=  get_basic_setup(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
     a_						,     a_     :							Tuple     						=  generate_predictions(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
     assert (
         len(UpperCamelCase__				) == num_samples
     ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__				)}"



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					bool = False ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


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

     # Then do distributed
     a_						,     a_						,     a_     :							str     						=  setup["""ddp"""]
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               a_     :							Tuple     						=  model(**UpperCamelCase__				)
          a_     :							Union[str, Any]     						=  outputs.logits.argmax(dim=-1				)
          a_     :							Optional[Any]     						=  batch["""labels"""]
          a_						,     a_     :							Union[str, Any]     						=  accelerator.gather_for_metrics((preds, references)				)
          metric.add_batch(predictions=UpperCamelCase__ ,    references=UpperCamelCase__				)
     a_     :							Optional[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  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							str     						=  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]:
               a_     :							Tuple     						=  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**"""				)
     a_     :							List[str]     						=  Accelerator()
     test_torch_metrics(UpperCamelCase__ ,    512				)
     accelerator.state._reset_state()



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Any				):




     """simple docstring"""


     main()


if __name__ == "__main__":
 main()






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_      :  Optional[Any]				     =  {}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Any				     =  ['GPTSw3Tokenizer']


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 lowerCAmelCase_      :  int				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
