






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	:							List[Any]				='''true'''







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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=False			) ->	Any:
     UpperCamelCase__			:   List[str]       				=  AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"			)
     UpperCamelCase__			:   Tuple       				=  load_dataset("glue"					,    "mrpc"					,    split="validation"			)

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

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

     UpperCamelCase__			:   Union[str, Any]       				=  tokenized_datasets.rename_column("label"					,    "labels"			)

     def collate_fn(__lowerCAmelCase			):
          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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Union[str, Any]:
     UpperCamelCase__			:   List[Any]       				=  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__			:   List[Any]       				=  accelerator.prepare(__lowerCAmelCase					,    __lowerCAmelCase			)
     return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Union[str, Any]:
     UpperCamelCase__			:   Union[str, Any]       				=  []
     for batch in dataloader:
          UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  batch.values()
          with torch.no_grad():
               UpperCamelCase__			:   List[Any]       				=  model(__lowerCAmelCase			)
               UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  accelerator.gather_for_metrics((logit, target)			)
               logits_and_targets.append((logit, target)			)
     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  [], []
     for logit, targ in logits_and_targets:
          logits.append(__lowerCAmelCase			)
          targs.append(__lowerCAmelCase			)
     UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  torch.cat(__lowerCAmelCase			), torch.cat(__lowerCAmelCase			)
     return logits, targs







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=82					,    __lowerCAmelCase=False					,    __lowerCAmelCase=False					,    __lowerCAmelCase=16			) ->	Any:
     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   int       				=  get_basic_setup(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  generate_predictions(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     assert (
         len(__lowerCAmelCase			) == num_samples
     ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCAmelCase			)}'







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase = False					,    __lowerCAmelCase = False			) ->	List[str]:
     UpperCamelCase__			:   Optional[Any]       				=  evaluate.load("glue"					,    "mrpc"			)
     UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  get_mrpc_setup(__lowerCAmelCase					,    __lowerCAmelCase			)
     # First do baseline
     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   int       				=  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__			:   Tuple       				=  outputs.logits.argmax(dim=-1			)
          metric.add_batch(predictions=__lowerCAmelCase					,    references=batch["labels"]			)
     UpperCamelCase__			:   int       				=  metric.compute()

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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Union[str, Any]:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()







lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()