


'''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_  :				Optional[int]						= """true"""





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						str=82					,				UpperCAmelCase_						:						int=16		):
 set_seed(42		)
 lowerCamelCase_      =  RegressionModel()
 lowerCamelCase_      =  deepcopy(UpperCAmelCase_		)
 lowerCamelCase_      =  RegressionDataset(length=UpperCAmelCase_		)
 lowerCamelCase_      =  DataLoader(UpperCAmelCase_					,				batch_size=UpperCAmelCase_		)
 model.to(accelerator.device		)
 lowerCamelCase_ ,lowerCamelCase_      =  accelerator.prepare(UpperCAmelCase_					,				UpperCAmelCase_		)
 return model, ddp_model, dataloader





def __snake_case					(      UpperCAmelCase_						:						Accelerator					,				UpperCAmelCase_						:						str=False		):
 lowerCamelCase_      =  AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"		)
 lowerCamelCase_      =  load_dataset("glue"					,				"mrpc"					,				split="validation"		)

 def tokenize_function(UpperCAmelCase_						:						Union[str, Any]		):
  lowerCamelCase_      =  tokenizer(examples["sentence1"]					,				examples["sentence2"]					,				truncation=UpperCAmelCase_					,				max_length=UpperCAmelCase_		)
  return outputs

 with accelerator.main_process_first():
  lowerCamelCase_      =  dataset.map(
      UpperCAmelCase_					,				batched=UpperCAmelCase_					,				remove_columns=["idx", "sentence1", "sentence2"]					,				)

 lowerCamelCase_      =  tokenized_datasets.rename_column("label"					,				"labels"		)

 def collate_fn(UpperCAmelCase_						:						int		):
  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 __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  Accelerator(dispatch_batches=UpperCAmelCase_					,				split_batches=UpperCAmelCase_		)
 lowerCamelCase_      =  get_dataloader(UpperCAmelCase_					,				not dispatch_batches		)
 lowerCamelCase_      =  AutoModelForSequenceClassification.from_pretrained(
     "hf-internal-testing/mrpc-bert-base-cased"					,				return_dict=UpperCAmelCase_		)
 lowerCamelCase_ ,lowerCamelCase_      =  accelerator.prepare(UpperCAmelCase_					,				UpperCAmelCase_		)
 return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





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





def __snake_case					(      UpperCAmelCase_						:						Accelerator					,				UpperCAmelCase_						:						Optional[Any]=82					,				UpperCAmelCase_						:						Tuple=False					,				UpperCAmelCase_						:						str=False					,				UpperCAmelCase_						:						Union[str, Any]=16		):
 lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  get_basic_setup(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
 lowerCamelCase_ ,lowerCamelCase_      =  generate_predictions(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
 assert (
     len(UpperCAmelCase_		) == num_samples
 ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase_		)}'''





def __snake_case					(      UpperCAmelCase_						:						bool = False					,				UpperCAmelCase_						:						bool = False		):
 lowerCamelCase_      =  evaluate.load("glue"					,				"mrpc"		)
 lowerCamelCase_ ,lowerCamelCase_      =  get_mrpc_setup(UpperCAmelCase_					,				UpperCAmelCase_		)
 # First do baseline
 lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  setup["no"]
 model.to(UpperCAmelCase_		)
 model.eval()
 for batch in dataloader:
  batch.to(UpperCAmelCase_		)
  with torch.inference_mode():
   lowerCamelCase_      =  model(**UpperCAmelCase_		)
  lowerCamelCase_      =  outputs.logits.argmax(dim=-1		)
  metric.add_batch(predictions=UpperCAmelCase_					,				references=batch["labels"]		)
 lowerCamelCase_      =  metric.compute()

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





def __snake_case					(      UpperCAmelCase_						:						int		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()







'''simple docstring'''

a_  :				Any						= """0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
     from .utils import rich



