


"""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


__lowercase    					=      """true"""


def      lowercase			(				A_  ,   A_=82  ,   A_=16       )->      Tuple:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_=False       )->      List[Any]:
  '''simple docstring'''







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

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

  with accelerator.main_process_first():
    a						:		Union[str, Any]      							=			dataset.map(
        A_  ,   batched=A_  ,   remove_columns=["idx", "sentence1", "sentence2"]  ,   )

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

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

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


def      lowercase			(				A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_=82  ,   A_=False  ,   A_=False  ,   A_=16       )->      str:
  '''simple docstring'''







  a							,		a							,		a						:		Tuple      							=			get_basic_setup(A_  ,   A_  ,   A_       )
  a							,		a						:		Dict      							=			generate_predictions(A_  ,   A_  ,   A_       )
  assert (
      len(A_       ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_       )}'''


def      lowercase			(				A_ = False  ,   A_ = False       )->      Union[str, Any]:
  '''simple docstring'''







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

  # Then do distributed
  a							,		a							,		a						:		List[str]      							=			setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      a						:		Tuple      							=			model(**A_       )
    a						:		Dict      							=			outputs.logits.argmax(dim=-1       )
    a						:		List[str]      							=			batch["labels"]
    a							,		a						:		Tuple      							=			accelerator.gather_for_metrics((preds, references)       )
    metric.add_batch(predictions=A_  ,   references=A_       )
  a						:		List[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      lowercase			(				)->      List[str]:
  '''simple docstring'''







  a						:		int      							=			Accelerator(split_batches=A_  ,   dispatch_batches=A_       )
  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(A_  ,   A_       )
        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						:		Optional[Any]      							=			Accelerator(split_batches=A_  ,   dispatch_batches=A_       )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''       )
      test_torch_metrics(A_  ,   99       )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**"       )
  a						:		Dict      							=			Accelerator()
  test_torch_metrics(A_  ,   512       )
  accelerator.state._reset_state()


def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  main()


if __name__ == "__main__":
 main()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






