


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	: List[str]  				='true'


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		load_dataset('''glue'''    ,       '''mrpc'''    ,       split='''validation'''      )

						def tokenize_function(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		dataset.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												if use_longest:
																		return tokenizer.pad(lowercase__    ,       padding='''longest'''    ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase__    ,       padding='''max_length'''    ,       max_length=128    ,       return_tensors='''pt'''      )

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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		Accelerator(dispatch_batches=lowercase__    ,       split_batches=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_dataloader(lowercase__    ,       not dispatch_batches      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		AutoModelForSequenceClassification.from_pretrained(
						    '''hf-internal-testing/mrpc-bert-base-cased'''    ,       return_dict=lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase__      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase__      )
												targs.append(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		torch.cat(lowercase__      ), torch.cat(lowercase__      )
						return logits, targs


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=False    ,       lowercase__=False    ,       lowercase__=16      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_basic_setup(lowercase__    ,       lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		generate_predictions(lowercase__    ,       lowercase__    ,       lowercase__      )
						assert (
						    len(lowercase__      ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}'''


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

						# Then do distributed
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     int					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     str					=		outputs.logits.argmax(dim=-1      )
												__SCREAMING_SNAKE_CASE						:     Any					=		batch['''labels''']
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase__    ,       references=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[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							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
						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(lowercase__    ,       lowercase__      )
																								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						:     List[Any]					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
																		if accelerator.is_local_main_process:
																								print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
																		test_torch_metrics(lowercase__    ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Accelerator()
						test_torch_metrics(lowercase__    ,       512      )
						accelerator.state._reset_state()


def        _UpperCamelCase							(      lowercase__      ):
						# 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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_frames
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		min_resolution
										__SCREAMING_SNAKE_CASE						:     Any					=		max_resolution
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_mean
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_std
										__SCREAMING_SNAKE_CASE						:     str					=		crop_size



				def 		__magic_name__(       self			:Tuple	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_center_crop'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		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}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		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 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)


				def 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			numpify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			np.ndarray	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Any					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)



				def 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			torchify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			torch.Tensor	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)
