



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


lowercase_								=     """true"""



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=82						,  SCREAMING_SNAKE_CASE_=16  ):
			set_seed(42  )
			lowercase__					  =				RegressionModel()
			lowercase__					  =				deepcopy(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				RegressionDataset(length=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				DataLoader(SCREAMING_SNAKE_CASE_						,  batch_size=SCREAMING_SNAKE_CASE_  )
			model.to(accelerator.device  )
			lowercase__  ,       lowercase__					  =				accelerator.prepare(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			return model, ddp_model, dataloader



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

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

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

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

			def collate_fn(SCREAMING_SNAKE_CASE_  ):
						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=128						,  return_tensors="pt"  )

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



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



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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=82						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=False						,  SCREAMING_SNAKE_CASE_=16  ):
			lowercase__  ,       lowercase__  ,       lowercase__					  =				get_basic_setup(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			lowercase__  ,       lowercase__					  =				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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_ = False						,  SCREAMING_SNAKE_CASE_ = False  ):
			lowercase__					  =				evaluate.load("glue"						,  "mrpc"  )
			lowercase__  ,       lowercase__					  =				get_mrpc_setup(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			# First do baseline
			lowercase__  ,       lowercase__  ,       lowercase__					  =				setup["no"]
			model.to(SCREAMING_SNAKE_CASE_  )
			model.eval()
			for batch in dataloader:
						batch.to(SCREAMING_SNAKE_CASE_  )
						with torch.inference_mode():
									lowercase__					  =				model(**SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				outputs.logits.argmax(dim=-1  )
						metric.add_batch(predictions=SCREAMING_SNAKE_CASE_						,  references=batch["labels"]  )
			lowercase__					  =				metric.compute()

			# Then do distributed
			lowercase__  ,       lowercase__  ,       lowercase__					  =				setup["ddp"]
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									lowercase__					  =				model(**SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				outputs.logits.argmax(dim=-1  )
						lowercase__					  =				batch["labels"]
						lowercase__  ,       lowercase__					  =				accelerator.gather_for_metrics((preds, references)  )
						metric.add_batch(predictions=SCREAMING_SNAKE_CASE_						,  references=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				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 (     ):
			lowercase__					  =				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]:
									lowercase__					  =				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_						,  99  )
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print("**Test last batch is not dropped when perfectly divisible**"  )
			lowercase__					  =				Accelerator()
			test_torch_metrics(SCREAMING_SNAKE_CASE_						,  512  )
			accelerator.state._reset_state()



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




