

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


lowerCAmelCase							:  Union[str, Any]   	=						"""true"""







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=82				,	_UpperCAmelCase=16    ):
						set_seed(42    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       RegressionModel()
						SCREAMING_SNAKE_CASE_: List[str]  		=       deepcopy(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       RegressionDataset(length=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       DataLoader(_UpperCAmelCase				,	batch_size=_UpperCAmelCase    )
						model.to(accelerator.device    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						return model, ddp_model, dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: int  		=       load_dataset("glue"				,	"mrpc"				,	split="validation"    )

						def tokenize_function(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: int  		=       tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=_UpperCAmelCase				,	max_length=_UpperCAmelCase    )
												return outputs

						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_: int  		=       dataset.map(
												    _UpperCAmelCase				,	batched=_UpperCAmelCase				,	remove_columns=["idx", "sentence1", "sentence2"]				,	)

						SCREAMING_SNAKE_CASE_: Dict  		=       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=1_28				,	return_tensors="pt"    )

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







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







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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=82				,	_UpperCAmelCase=False				,	_UpperCAmelCase=False				,	_UpperCAmelCase=16    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_basic_setup(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       generate_predictions(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						assert (
						    len(_UpperCAmelCase    ) == num_samples
						), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase    )}"







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

						# Then do distributed
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       setup["ddp"]
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(**_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       outputs.logits.argmax(dim=-1    )
												SCREAMING_SNAKE_CASE_: Any  		=       batch["labels"]
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       accelerator.gather_for_metrics((preds, references)    )
												metric.add_batch(predictions=_UpperCAmelCase				,	references=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       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_      (    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       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]:
																		SCREAMING_SNAKE_CASE_: int  		=       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**"    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       Accelerator()
						test_torch_metrics(_UpperCAmelCase				,	5_12    )
						accelerator.state._reset_state()







def 			A_      (    _UpperCAmelCase    ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor


class        _a							(			unittest.TestCase			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Dict=7    ,						UpperCamelCase_:	Optional[int]=3    ,						UpperCamelCase_:	List[Any]=18    ,						UpperCamelCase_:	Dict=30    ,						UpperCamelCase_:	Optional[int]=400    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Union[str, Any]=None    ,						UpperCamelCase_:	List[str]=True    ,						UpperCamelCase_:	Dict=None    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Tuple=[0.5, 0.5, 0.5]    ,						UpperCamelCase_:	List[str]=[0.5, 0.5, 0.5]    ,						)			->   int:





									"""simple docstring"""







									lowercase__            =      size if size is not None else {'''shortest_edge''': 18}
									lowercase__            =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      num_channels
									lowercase__            =      image_size
									lowercase__            =      min_resolution
									lowercase__            =      max_resolution
									lowercase__            =      do_resize
									lowercase__            =      size
									lowercase__            =      do_center_crop
									lowercase__            =      crop_size
									lowercase__            =      do_normalize
									lowercase__            =      image_mean
									lowercase__            =      image_std


							def       lowerCamelCase_ (							self:	List[str]							)			->   Optional[Any]:





									"""simple docstring"""







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "do_center_crop": self.do_center_crop,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}


@require_torch
@require_vision
class        _a							(			UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			List[str]        = LevitImageProcessor if is_vision_available() else None

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      LevitImageProcessingTester(self							)

							@property
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Any:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_mean'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''image_std'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_resize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_center_crop'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''size'''							)							)

							def       lowerCamelCase_ (							self:	str							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 18}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 18, '''width''': 18}							)

									lowercase__            =      self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84							)
									self.assertEqual(image_processor.size    ,						{'''shortest_edge''': 42}							)
									self.assertEqual(image_processor.crop_size    ,						{'''height''': 84, '''width''': 84}							)

							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						numpify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						np.ndarray							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						torchify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						torch.Tensor							)

									# Test not batched input
									lowercase__            =      image_processing(image_inputs[0]    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)

									# Test batched
									lowercase__            =      image_processing(UpperCamelCase_    ,						return_tensors='''pt'''							).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,						(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,						)


