




"""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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=82					,							UpperCamelCase=16				)      ->      Tuple:


			"""simple docstring"""
			set_seed(42				)
			__UpperCAmelCase						:			Dict												=       RegressionModel()
			__UpperCAmelCase						:			Optional[Any]												=       deepcopy(UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       RegressionDataset(length=UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       DataLoader(UpperCamelCase					,							batch_size=UpperCamelCase				)
			model.to(accelerator.device				)
			__UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       accelerator.prepare(UpperCamelCase					,							UpperCamelCase				)
			return model, ddp_model, dataloader

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=False				)      ->      Optional[int]:


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

			def tokenize_function(UpperCamelCase				):
						__UpperCAmelCase						:			Dict												=       tokenizer(examples["sentence1"]					,							examples["sentence2"]					,							truncation=UpperCamelCase					,							max_length=UpperCamelCase				)
						return outputs

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

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

			def collate_fn(UpperCamelCase				):
						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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Optional[int]:


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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase=82					,							UpperCamelCase=False					,							UpperCamelCase=False					,							UpperCamelCase=16				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       get_basic_setup(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)
			__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, 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        _UpperCamelCase     (      UpperCamelCase = False					,							UpperCamelCase = False				)      ->      List[str]:


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

			# Then do distributed
			__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       setup["ddp"]
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									__UpperCAmelCase						:			Any												=       model(**UpperCamelCase				)
						__UpperCAmelCase						:			str												=       outputs.logits.argmax(dim=-1				)
						__UpperCAmelCase						:			Union[str, Any]												=       batch["labels"]
						__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       accelerator.gather_for_metrics((preds, references)				)
						metric.add_batch(predictions=UpperCamelCase					,							references=UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, 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        _UpperCamelCase     (      )      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       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]:
									__UpperCAmelCase						:			Union[str, Any]												=       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**"				)
			__UpperCAmelCase						:			Any												=       Accelerator()
			test_torch_metrics(UpperCamelCase					,							512				)
			accelerator.state._reset_state()

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
					main()








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_		:     Optional[Any]          =		{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		[
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   UpperCamelCase_		:     int          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


