


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


__UpperCAmelCase	:       str    	=							"true"







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=82 , SCREAMING_SNAKE_CASE__=16)       ->   Union[str, Any]:
  set_seed(42)
  __snake_case:						Optional[Any]			  =						RegressionModel()
  __snake_case:						Dict			  =						deepcopy(lowerCAmelCase__)
  __snake_case:						int			  =						RegressionDataset(length=lowerCAmelCase__)
  __snake_case:						Any			  =						DataLoader(lowerCAmelCase__ , batch_size=lowerCAmelCase__)
  model.to(accelerator.device)
  __snake_case  ,       __snake_case:						Any			  =						accelerator.prepare(lowerCAmelCase__ , lowerCAmelCase__)
  return model, ddp_model, dataloader







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False)       ->   List[Any]:
  __snake_case:						List[str]			  =						AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""")
  __snake_case:						Optional[Any]			  =						load_dataset("""glue""" , """mrpc""" , split="""validation""")

  def tokenize_function(SCREAMING_SNAKE_CASE__):
    __snake_case:						Tuple			  =						tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__)
    return outputs

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

  __snake_case:						List[Any]			  =						tokenized_datasets.rename_column("""label""" , """labels""")

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







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







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=82 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=16)       ->   Dict:
  __snake_case  ,       __snake_case  ,       __snake_case:						Optional[Any]			  =						get_basic_setup(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__)
  __snake_case  ,       __snake_case:						Tuple			  =						generate_predictions(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__)
  assert (
      len(lowerCAmelCase__) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__)}'''







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

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







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:
  main()


if __name__ == "__main__":
      main()













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






