

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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					int=82					, lowerCAmelCase:					Optional[Any]=16	)				->    Optional[int]:
				set_seed(42	)
				_UpperCAmelCase    :						Union[str, Any]							=      RegressionModel()
				_UpperCAmelCase    :						str							=      deepcopy(lowerCAmelCase	)
				_UpperCAmelCase    :						Any							=      RegressionDataset(length=lowerCAmelCase	)
				_UpperCAmelCase    :						Dict							=      DataLoader(lowerCAmelCase					, batch_size=lowerCAmelCase	)
				model.to(accelerator.device	)
				_UpperCAmelCase    :						Tuple							=      accelerator.prepare(lowerCAmelCase					, lowerCAmelCase	)
				return model, ddp_model, dataloader

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Accelerator					, lowerCAmelCase:					Dict=False	)				->    Union[str, Any]:
				_UpperCAmelCase    :						Union[str, Any]							=      AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
				_UpperCAmelCase    :						Any							=      load_dataset("glue"					, "mrpc"					, split="validation"	)

				def tokenize_function(lowerCAmelCase:					Union[str, Any]	):
								_UpperCAmelCase    :						List[Any]							=      tokenizer(examples["sentence1"]					, examples["sentence2"]					, truncation=lowerCAmelCase					, max_length=lowerCAmelCase	)
								return outputs

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

				_UpperCAmelCase    :						List[Any]							=      tokenized_datasets.rename_column("label"					, "labels"	)

				def collate_fn(lowerCAmelCase:					List[str]	):
								if use_longest:
												return tokenizer.pad(lowerCAmelCase					, padding="longest"					, return_tensors="pt"	)
								return tokenizer.pad(lowerCAmelCase					, padding="max_length"					, max_length=128					, return_tensors="pt"	)

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

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					Tuple					, lowerCAmelCase:					List[Any]	)				->    Optional[int]:
				_UpperCAmelCase    :						str							=      []
				for batch in dataloader:
								_UpperCAmelCase    :						Union[str, Any]							=      batch.values()
								with torch.no_grad():
												_UpperCAmelCase    :						List[Any]							=      model(lowerCAmelCase	)
												_UpperCAmelCase    :						List[Any]							=      accelerator.gather_for_metrics((logit, target)	)
												logits_and_targets.append((logit, target)	)
				_UpperCAmelCase    :						Tuple							=      [], []
				for logit, targ in logits_and_targets:
								logits.append(lowerCAmelCase	)
								targs.append(lowerCAmelCase	)
				_UpperCAmelCase    :						Optional[int]							=      torch.cat(lowerCAmelCase	), torch.cat(lowerCAmelCase	)
				return logits, targs

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Accelerator					, lowerCAmelCase:					Any=82					, lowerCAmelCase:					List[Any]=False					, lowerCAmelCase:					List[str]=False					, lowerCAmelCase:					Union[str, Any]=16	)				->    List[str]:
				_UpperCAmelCase    :						List[str]							=      get_basic_setup(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      generate_predictions(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)
				assert (
				    len(lowerCAmelCase	) == num_samples
				), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase	)}'

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

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()







from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					float | Decimal					, lowerCAmelCase:					float = 10**-10	)				->    float:
				_UpperCAmelCase    :						Optional[int]							=      a
				while True:
								_UpperCAmelCase    :						Tuple							=      Decimal(lowerCAmelCase	) - (
								    Decimal(eval(lowerCAmelCase	)	) / Decimal(eval(str(diff(lowerCAmelCase	)	)	)	)  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(lowerCAmelCase	)	) < precision:  # noqa: S307
												return float(lowerCAmelCase	)


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
					# Find root of polynomial
					print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
					# Find Square Root of 5
					print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
					# Exponential Roots
					print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


