
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


__SCREAMING_SNAKE_CASE 						="""true"""

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=8_2  ,    _lowerCAmelCase=1_6     ):
							set_seed(4_2     )
							SCREAMING_SNAKE_CASE_   	=    RegressionModel()
							SCREAMING_SNAKE_CASE_   	=    deepcopy(SCREAMING_SNAKE_CASE_     )
							SCREAMING_SNAKE_CASE_   	=    RegressionDataset(length=SCREAMING_SNAKE_CASE_     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(SCREAMING_SNAKE_CASE_  ,    batch_size=SCREAMING_SNAKE_CASE_     )
							model.to(accelerator.device     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_     )
							return model, ddp_model, dataloader

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							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(_lowerCAmelCase     ):
														SCREAMING_SNAKE_CASE_   	=    tokenizer(examples['''sentence1''']  ,    examples['''sentence2''']  ,    truncation=SCREAMING_SNAKE_CASE_  ,    max_length=SCREAMING_SNAKE_CASE_     )
														return outputs

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

							SCREAMING_SNAKE_CASE_   	=    tokenized_datasets.rename_column('''label'''  ,    '''labels'''     )

							def collate_fn(_lowerCAmelCase     ):
														if use_longest:
																					return tokenizer.pad(SCREAMING_SNAKE_CASE_  ,    padding='''longest'''  ,    return_tensors='''pt'''     )
														return tokenizer.pad(SCREAMING_SNAKE_CASE_  ,    padding='''max_length'''  ,    max_length=1_2_8  ,    return_tensors='''pt'''     )

							return DataLoader(SCREAMING_SNAKE_CASE_  ,    shuffle=SCREAMING_SNAKE_CASE_  ,    collate_fn=SCREAMING_SNAKE_CASE_  ,    batch_size=1_6     )

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    []
							for batch in dataloader:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    batch.values()
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE_   	=    model(SCREAMING_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(SCREAMING_SNAKE_CASE_     )
														targs.append(SCREAMING_SNAKE_CASE_     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    torch.cat(SCREAMING_SNAKE_CASE_     ), torch.cat(SCREAMING_SNAKE_CASE_     )
							return logits, targs

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=8_2  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=1_6     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_basic_setup(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    generate_predictions(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_     )
							assert (
							    len(SCREAMING_SNAKE_CASE_     ) == num_samples
							), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_     )}"

def 			a   (_lowerCAmelCase = False  ,    _lowerCAmelCase = False     ):
							SCREAMING_SNAKE_CASE_   	=    evaluate.load('''glue'''  ,    '''mrpc'''     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    get_mrpc_setup(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_     )
							# First do baseline
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    setup['''no''']
							model.to(SCREAMING_SNAKE_CASE_     )
							model.eval()
							for batch in dataloader:
														batch.to(SCREAMING_SNAKE_CASE_     )
														with torch.inference_mode():
																					SCREAMING_SNAKE_CASE_   	=    model(**SCREAMING_SNAKE_CASE_     )
														SCREAMING_SNAKE_CASE_   	=    outputs.logits.argmax(dim=-1     )
														metric.add_batch(predictions=SCREAMING_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(**SCREAMING_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=SCREAMING_SNAKE_CASE_  ,    references=SCREAMING_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 			a   ():
							SCREAMING_SNAKE_CASE_   	=    Accelerator(split_batches=SCREAMING_SNAKE_CASE_  ,    dispatch_batches=SCREAMING_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(SCREAMING_SNAKE_CASE_  ,    SCREAMING_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=SCREAMING_SNAKE_CASE_  ,    dispatch_batches=SCREAMING_SNAKE_CASE_     )
																					if accelerator.is_local_main_process:
																												print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"     )
																					test_torch_metrics(SCREAMING_SNAKE_CASE_  ,    9_9     )
																					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(SCREAMING_SNAKE_CASE_  ,    5_1_2     )
							accelerator.state._reset_state()

def 			a   (_lowerCAmelCase     ):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()








from typing import Any

def 			a   (_lowerCAmelCase     ):
							if not input_list:
														return []
							SCREAMING_SNAKE_CASE_   	=    [input_list.count(_lowerCAmelCase     ) for value in input_list]
							SCREAMING_SNAKE_CASE_   	=    max(_lowerCAmelCase     )  # Gets the maximum count in the input list.
							# Gets values of modes
							return sorted({input_list[i] for i, value in enumerate(_lowerCAmelCase     ) if value == y}     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


