'''simple docstring'''





from pathlib import Path

import fire
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						Path(UpperCAmelCase					)
						_UpperCamelCase			:			Any   			=						Path(UpperCAmelCase					)
						dest_dir.mkdir(exist_ok=UpperCAmelCase					)
						for path in src_dir.iterdir():
												_UpperCamelCase			:			Optional[Any]   			=						[x.rstrip() for x in list(path.open().readlines()					)][:n]
												_UpperCamelCase			:			Tuple   			=						dest_dir.joinpath(path.name					)
												print(UpperCAmelCase					)
												dest_path.open("w"					).write("\n".join(UpperCAmelCase					)					)


if __name__ == "__main__":
			fire.Fire(minify)



'''simple docstring'''





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_	:		Tuple			=     """true"""
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=8_2     ,UpperCAmelCase=1_6					)     ->			Union[str, Any]:




						'''simple docstring'''
						set_seed(4_2					)
						_UpperCamelCase			:			List[Any]   			=						RegressionModel()
						_UpperCamelCase			:			Any   			=						deepcopy(UpperCAmelCase					)
						_UpperCamelCase			:			Tuple   			=						RegressionDataset(length=UpperCAmelCase					)
						_UpperCamelCase			:			Union[str, Any]   			=						DataLoader(UpperCAmelCase     ,batch_size=UpperCAmelCase					)
						model.to(accelerator.device					)
						_UpperCamelCase			,       _UpperCamelCase			:			Dict   			=						accelerator.prepare(UpperCAmelCase     ,UpperCAmelCase					)
						return model, ddp_model, dataloader
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=False					)     ->			List[str]:




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

						def tokenize_function(UpperCAmelCase					):
												_UpperCamelCase			:			Tuple   			=						tokenizer(examples["sentence1"]     ,examples["sentence2"]     ,truncation=UpperCAmelCase     ,max_length=UpperCAmelCase					)
												return outputs

						with accelerator.main_process_first():
												_UpperCamelCase			:			str   			=						dataset.map(
												    UpperCAmelCase     ,batched=UpperCAmelCase     ,remove_columns=["idx", "sentence1", "sentence2"]     ,)

						_UpperCamelCase			:			Optional[int]   			=						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_2_8     ,return_tensors="pt"					)

						return DataLoader(UpperCAmelCase     ,shuffle=UpperCAmelCase     ,collate_fn=UpperCAmelCase     ,batch_size=1_6					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




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




						'''simple docstring'''
						_UpperCamelCase			:			List[str]   			=						[]
						for batch in dataloader:
												_UpperCamelCase			,       _UpperCamelCase			:			int   			=						batch.values()
												with torch.no_grad():
																		_UpperCamelCase			:			Tuple   			=						model(UpperCAmelCase					)
																		_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						accelerator.gather_for_metrics((logit, target)					)
																		logits_and_targets.append((logit, target)					)
						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						[], []
						for logit, targ in logits_and_targets:
												logits.append(UpperCAmelCase					)
												targs.append(UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			int   			=						torch.cat(UpperCAmelCase					), torch.cat(UpperCAmelCase					)
						return logits, targs
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=8_2     ,UpperCAmelCase=False     ,UpperCAmelCase=False     ,UpperCAmelCase=1_6					)     ->			Any:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						get_basic_setup(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						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					)     ->			Tuple:




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

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




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						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]:
																		_UpperCamelCase			:			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     ,9_9					)
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test last batch is not dropped when perfectly divisible**"					)
						_UpperCamelCase			:			int   			=						Accelerator()
						test_torch_metrics(UpperCAmelCase     ,5_1_2					)
						accelerator.state._reset_state()
def 						__A							(     UpperCAmelCase					)     ->			List[str]:




						'''simple docstring'''
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
			main()


