

'''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


A_							    =				"true"





def  A_	(			snake_case   ,							snake_case=82   ,							snake_case=16			):
  set_seed(42			)
  SCREAMING_SNAKE_CASE:Dict       =       RegressionModel()
  SCREAMING_SNAKE_CASE:Any       =       deepcopy(__UpperCAmelCase			)
  SCREAMING_SNAKE_CASE:Optional[int]       =       RegressionDataset(length=__UpperCAmelCase			)
  SCREAMING_SNAKE_CASE:Optional[int]       =       DataLoader(__UpperCAmelCase   ,							batch_size=__UpperCAmelCase			)
  model.to(accelerator.device			)
  SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[int]       =       accelerator.prepare(__UpperCAmelCase   ,							__UpperCAmelCase			)
  return model, ddp_model, dataloader





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

  def tokenize_function(snake_case			):
    SCREAMING_SNAKE_CASE:Optional[Any]       =       tokenizer(examples["sentence1"]   ,							examples["sentence2"]   ,							truncation=__UpperCAmelCase   ,							max_length=__UpperCAmelCase			)
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:Optional[int]       =       dataset.map(
        __UpperCAmelCase   ,							batched=__UpperCAmelCase   ,							remove_columns=["idx", "sentence1", "sentence2"]   ,							)

  SCREAMING_SNAKE_CASE:Optional[int]       =       tokenized_datasets.rename_column("label"   ,							"labels"			)

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





def  A_	(			snake_case   ,							snake_case   ,							snake_case			):
  SCREAMING_SNAKE_CASE:Optional[Any]       =       []
  for batch in dataloader:
    SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[Any]       =       batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:Optional[int]       =       model(__UpperCAmelCase			)
      SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[Any]       =       accelerator.gather_for_metrics((logit, target)			)
      logits_and_targets.append((logit, target)			)
  SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Tuple       =       [], []
  for logit, targ in logits_and_targets:
    logits.append(__UpperCAmelCase			)
    targs.append(__UpperCAmelCase			)
  SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[Any]       =       torch.cat(__UpperCAmelCase			), torch.cat(__UpperCAmelCase			)
  return logits, targs





def  A_	(			snake_case   ,							snake_case=82   ,							snake_case=False   ,							snake_case=False   ,							snake_case=16			):
  SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:Optional[Any]       =       get_basic_setup(__UpperCAmelCase   ,							__UpperCAmelCase   ,							__UpperCAmelCase			)
  SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE:List[Any]       =       generate_predictions(__UpperCAmelCase   ,							__UpperCAmelCase   ,							__UpperCAmelCase			)
  assert (
      len(__UpperCAmelCase			) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCAmelCase			)}'''





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

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





def  A_	(			snake_case			):
  main()


if __name__ == "__main__":
    main()













'''simple docstring'''

from PIL import Image

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Image:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(259 * (level + 255)) / (255 * (259 - level))

   def contrast(__UpperCAmelCase   ) -> int:
      return int(128 + factor * (c - 128)   )

   return img.point(__UpperCAmelCase   )


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change contrast to 170
        a   	=    change_contrast(img, 170)
        cont_img.save("image_data/lena_high_contrast.png", format="png")





