






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      (		snake_case__	,snake_case__=82	,snake_case__=16		)							->  Dict:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=False		)							->  Any:






						"""simple docstring"""





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

						def tokenize_function(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  tokenizer(examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__		)
												return outputs

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

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

						def collate_fn(snake_case__		):
												if use_longest:
																		return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""		)
												return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=1_28	,return_tensors="""pt"""		)

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  Dict:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=82	,snake_case__=False	,snake_case__=False	,snake_case__=16		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  get_basic_setup(snake_case__	,snake_case__	,snake_case__		)
						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  generate_predictions(snake_case__	,snake_case__	,snake_case__		)
						assert (
						    len(snake_case__		) == num_samples
						), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}'

def 	__lowerCamelCase      (		snake_case__ = False	,snake_case__ = False		)							->  str:






						"""simple docstring"""





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

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






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  Accelerator(split_batches=snake_case__	,dispatch_batches=snake_case__		)
						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(snake_case__	,snake_case__		)
																								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]:
																		_SCREAMING_SNAKE_CASE     				=  Accelerator(split_batches=snake_case__	,dispatch_batches=snake_case__		)
																		if accelerator.is_local_main_process:
																								print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'		)
																		test_torch_metrics(snake_case__	,99		)
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("""**Test last batch is not dropped when perfectly divisible**"""		)
						_SCREAMING_SNAKE_CASE     				=  Accelerator()
						test_torch_metrics(snake_case__	,5_12		)
						accelerator.state._reset_state()

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





						main()


if __name__ == "__main__":
		main()









from typing import TYPE_CHECKING

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


UpperCamelCase			    =     {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
		from .configuration_mmbt import MMBTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
		import sys

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

