

'''simple docstring'''

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    "files"      ,       [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]      ,       )
def    A_  ( snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Any						      =					tmp_path_factory.mktemp("dset_infos_dir" )
		if "full:README.md" in files:
				with open(dataset_infos_dir / "README.md"      ,       "w" ) as f:
						f.write("---\ndataset_info:\n  dataset_size: 42\n---" )
		if "empty:README.md" in files:
				with open(dataset_infos_dir / "README.md"      ,       "w" ) as f:
						f.write("" )
    # we want to support dataset_infos.json for backward compatibility
		if "dataset_infos.json" in files:
				with open(dataset_infos_dir / "dataset_infos.json"      ,       "w" ) as f:
						f.write("{\"default\": {\"dataset_size\": 42}}" )
		SCREAMING_SNAKE_CASE:int						      =					DatasetInfosDict.from_directory(snake_case )
		assert dataset_infos
		assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    "dataset_info"      ,       [
        DatasetInfo(),
        DatasetInfo(
            description="foo"      ,       features=Features({"a": Value("int32" )} )      ,       builder_name="builder"      ,       config_name="config"      ,       version="1.0.0"      ,       splits=[{"name": "train"}]      ,       download_size=42      ,       ),
    ]      ,       )
def    A_  ( snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:Optional[Any]						      =					str(snake_case )
		dataset_info.write_to_directory(snake_case )
		SCREAMING_SNAKE_CASE:Union[str, Any]						      =					DatasetInfo.from_directory(snake_case )
		assert dataset_info == reloaded
		assert os.path.exists(os.path.join(snake_case      ,       "dataset_info.json" ) )





def    A_  ( ):
		SCREAMING_SNAKE_CASE:Tuple						      =					DatasetInfo(
		    description="foo"      ,       citation="bar"      ,       homepage="https://foo.bar"      ,       license="CC0"      ,       features=Features({"a": Value("int32" )} )      ,       post_processed={}      ,       supervised_keys=()      ,       task_templates=[]      ,       builder_name="builder"      ,       config_name="config"      ,       version="1.0.0"      ,       splits=[{"name": "train", "num_examples": 42}]      ,       download_checksums={}      ,       download_size=1337      ,       post_processing_size=442      ,       dataset_size=1234      ,       size_in_bytes=1337 + 442 + 1234      ,       )
		SCREAMING_SNAKE_CASE:str						      =					dataset_info._to_yaml_dict()
		assert sorted(snake_case ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				assert key in dataset_info_yaml_dict
				assert isinstance(dataset_info_yaml_dict[key]      ,       (list, dict, int, str) )
		SCREAMING_SNAKE_CASE:Any						      =					yaml.safe_dump(snake_case )
		SCREAMING_SNAKE_CASE:str						      =					yaml.safe_load(snake_case )
		assert dataset_info_yaml_dict == reloaded





def    A_  ( ):
		SCREAMING_SNAKE_CASE:List[str]						      =					DatasetInfo()
		SCREAMING_SNAKE_CASE:Optional[int]						      =					dataset_info._to_yaml_dict()
		assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict"      ,       [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()} ),
        DatasetInfosDict({"my_config_name": DatasetInfo()} ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"      ,       features=Features({"a": Value("int32" )} )      ,       builder_name="builder"      ,       config_name="config"      ,       version="1.0.0"      ,       splits=[{"name": "train"}]      ,       download_size=42      ,       )
            } ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42 ),
                "v2": DatasetInfo(dataset_size=1337 ),
            } ),
    ]      ,       )
def    A_  ( snake_case      ,       snake_case ):
		SCREAMING_SNAKE_CASE:str						      =					str(snake_case )
		dataset_infos_dict.write_to_directory(snake_case )
		SCREAMING_SNAKE_CASE:Any						      =					DatasetInfosDict.from_directory(snake_case )

		# the config_name of the dataset_infos_dict take over the attribute
		for config_name, dataset_info in dataset_infos_dict.items():
				SCREAMING_SNAKE_CASE:Any						      =					config_name
				# the yaml representation doesn't include fields like description or citation
				# so we just test that we can recover what we can from the yaml
				SCREAMING_SNAKE_CASE:Tuple						      =					DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
		assert dataset_infos_dict == reloaded

		if dataset_infos_dict:
				assert os.path.exists(os.path.join(snake_case      ,       "README.md" ) )









'''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:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  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:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

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

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

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

  def collate_fn(snake_case ):
    if use_longest:
      return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )

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





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(dispatch_batches=snake_case      ,       split_batches=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloader(snake_case      ,       not dispatch_batches )
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.prepare(snake_case      ,       snake_case )
  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:str						      =					[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:int						      =					model(snake_case )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case )
    targs.append(snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.cat(snake_case ), torch.cat(snake_case )
  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:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_predictions(snake_case      ,       snake_case      ,       snake_case )
  assert (
      len(snake_case ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case )}'''





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

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					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:Optional[int]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
  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(snake_case      ,       snake_case )
        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:Optional[Any]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
      test_torch_metrics(snake_case      ,       99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






