






"""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


UpperCamelCase							       =    """true"""






def        lowerCAmelCase (      UpperCamelCase_:							Any     , UpperCamelCase_:							List[Any]=82     , UpperCamelCase_:							Optional[Any]=16					)       ->							Dict:







		'''simple docstring'''







		set_seed(42					)
		_a							=	RegressionModel()
		_a							=	deepcopy(UpperCamelCase_					)
		_a							=	RegressionDataset(length=UpperCamelCase_					)
		_a							=	DataLoader(UpperCamelCase_     , batch_size=UpperCamelCase_					)
		model.to(accelerator.device					)
		_a			,	_a							=	accelerator.prepare(UpperCamelCase_     , UpperCamelCase_					)
		return model, ddp_model, dataloader






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							Tuple=False					)       ->							Optional[Any]:







		'''simple docstring'''







		_a							=	AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"					)
		_a							=	load_dataset("glue"     , "mrpc"     , split="validation"					)

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

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

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

		def collate_fn(UpperCamelCase_:							Tuple					):
				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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							str					)       ->							List[str]:







		'''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int					)       ->							Optional[Any]:







		'''simple docstring'''







		_a							=	[]
		for batch in dataloader:
				_a			,	_a							=	batch.values()
				with torch.no_grad():
						_a							=	model(UpperCamelCase_					)
						_a			,	_a							=	accelerator.gather_for_metrics((logit, target)					)
						logits_and_targets.append((logit, target)					)
		_a			,	_a							=	[], []
		for logit, targ in logits_and_targets:
				logits.append(UpperCamelCase_					)
				targs.append(UpperCamelCase_					)
		_a			,	_a							=	torch.cat(UpperCamelCase_					), torch.cat(UpperCamelCase_					)
		return logits, targs






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							int=82     , UpperCamelCase_:							Any=False     , UpperCamelCase_:							Optional[Any]=False     , UpperCamelCase_:							Dict=16					)       ->							List[str]:







		'''simple docstring'''







		_a			,	_a			,	_a							=	get_basic_setup(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
		_a			,	_a							=	generate_predictions(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
		assert (
		    len(UpperCamelCase_					) == num_samples
		), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase_					)}'''






def        lowerCAmelCase (      UpperCamelCase_:							bool = False     , UpperCamelCase_:							bool = False					)       ->							Optional[Any]:







		'''simple docstring'''







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

		# Then do distributed
		_a			,	_a			,	_a							=	setup["ddp"]
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						_a							=	model(**UpperCamelCase_					)
				_a							=	outputs.logits.argmax(dim=-1					)
				_a							=	batch["labels"]
				_a			,	_a							=	accelerator.gather_for_metrics((preds, references)					)
				metric.add_batch(predictions=UpperCamelCase_     , references=UpperCamelCase_					)
		_a							=	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        lowerCAmelCase (      )       ->							Optional[Any]:







		'''simple docstring'''







		_a							=	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]:
						_a							=	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**"					)
		_a							=	Accelerator()
		test_torch_metrics(UpperCamelCase_     , 512					)
		accelerator.state._reset_state()






def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]					)       ->							List[str]:







		'''simple docstring'''







		main()


if __name__ == "__main__":
					main()










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase							       =    {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    ["""FNetTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FNetForMaskedLM""",
         """FNetForMultipleChoice""",
         """FNetForNextSentencePrediction""",
         """FNetForPreTraining""",
         """FNetForQuestionAnswering""",
         """FNetForSequenceClassification""",
         """FNetForTokenClassification""",
         """FNetLayer""",
         """FNetModel""",
         """FNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

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


