



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE							:Dict										=    16
SCREAMING_SNAKE_CASE							:Tuple										=    32


def 						UpperCAmelCase      (     a_		,       a_ = 1_6		,       a_ = "bert-base-cased"						)		->     List[str]:







				"""simple docstring"""



				__A             =			AutoTokenizer.from_pretrained(a_						)
				__A             =			load_dataset("glue"		,       "mrpc"						)

				def tokenize_function(a_						):
								# max_length=None => use the model max length (it's actually the default)
								__A             =			tokenizer(examples["sentence1"]		,       examples["sentence2"]		,       truncation=a_		,       max_length=a_						)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				__A             =			datasets.map(
				    a_		,       batched=a_		,       remove_columns=["idx", "sentence1", "sentence2"]		,       load_from_cache_file=a_						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__A             =			tokenized_datasets.rename_column("label"		,       "labels"						)

				def collate_fn(a_						):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(a_		,       padding="max_length"		,       max_length=1_2_8		,       return_tensors="pt"						)
								return tokenizer.pad(a_		,       padding="longest"		,       return_tensors="pt"						)

				# Instantiate dataloaders.
				__A             =			DataLoader(
				    tokenized_datasets["train"]		,       shuffle=a_		,       collate_fn=a_		,       batch_size=a_						)
				__A             =			DataLoader(
				    tokenized_datasets["validation"]		,       shuffle=a_		,       collate_fn=a_		,       batch_size=a_						)

				return train_dataloader, eval_dataloader


def 						UpperCAmelCase      (     a_		,       a_						)		->     int:







				"""simple docstring"""



				__A             =			Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__A             =			config["lr"]
				__A             =			int(config["num_epochs"]						)
				__A             =			int(config["seed"]						)
				__A             =			int(config["batch_size"]						)
				__A             =			args.model_name_or_path

				set_seed(a_						)
				__A             =			get_dataloaders(a_		,       a_		,       a_						)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__A             =			AutoModelForSequenceClassification.from_pretrained(a_		,       return_dict=a_						)

				# Instantiate optimizer
				__A             =			(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__A             =			optimizer_cls(params=model.parameters()		,       lr=a_						)

				if accelerator.state.deepspeed_plugin is not None:
								__A             =			accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								__A             =			1
				__A             =			(len(a_						) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								__A             =			get_linear_schedule_with_warmup(
								    optimizer=a_		,       num_warmup_steps=0		,       num_training_steps=a_		,       )
				else:
								__A             =			DummyScheduler(a_		,       total_num_steps=a_		,       warmup_num_steps=0						)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__A             =			accelerator.prepare(
				    a_		,       a_		,       a_		,       a_		,       a_						)

				# We need to keep track of how many total steps we have iterated over
				__A             =			0
				# We also need to keep track of the stating epoch so files are named properly
				__A             =			0

				# Now we train the model
				__A             =			evaluate.load("glue"		,       "mrpc"						)
				__A             =			0
				__A             =			{}
				for epoch in range(a_		,       a_						):
								model.train()
								for step, batch in enumerate(a_						):
												__A             =			model(**a_						)
												__A             =			outputs.loss
												__A             =			loss / gradient_accumulation_steps
												accelerator.backward(a_						)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

								model.eval()
								__A             =			0
								for step, batch in enumerate(a_						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												with torch.no_grad():
																__A             =			model(**a_						)
												__A             =			outputs.logits.argmax(dim=-1						)
												# It is slightly faster to call this once, than multiple times
												__A             =			accelerator.gather(
												    (predictions, batch["labels"])						)  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																if step == len(a_						) - 1:
																				__A             =			predictions[: len(eval_dataloader.dataset						) - samples_seen]
																				__A             =			references[: len(eval_dataloader.dataset						) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=a_		,       references=a_		,       )

								__A             =			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''		,       a_						)
								__A             =			eval_metric["accuracy"]

								if best_performance < eval_metric["accuracy"]:
												__A             =			eval_metric["accuracy"]

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir		,       "all_results.json"						)		,       "w"						) as f:
												json.dump(a_		,       a_						)


def 						UpperCAmelCase      (     )		->     List[str]:







				"""simple docstring"""



				__A             =			argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
				parser.add_argument(
				    "--model_name_or_path"		,       type=a_		,       default="bert-base-cased"		,       help="Path to pretrained model or model identifier from huggingface.co/models."		,       required=a_		,       )
				parser.add_argument(
				    "--output_dir"		,       type=a_		,       default="."		,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,       )
				parser.add_argument(
				    "--performance_lower_bound"		,       type=a_		,       default=a_		,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."		,       )
				parser.add_argument(
				    "--num_epochs"		,       type=a_		,       default=3		,       help="Number of train epochs."		,       )
				__A             =			parser.parse_args()
				__A             =			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
				training_function(a_		,       a_						)


if __name__ == "__main__":
					main()


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					CTRLTokenizer
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Dict			):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A             =			["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
					__A             =			dict(zip(A							,range(len(A			)			)			)			)
					__A             =			["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
					__A             =			{"unk_token": "<unk>"}

					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"]			)
					__A             =			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["merges_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write(json.dumps(A			) + "\n"			)
					with open(self.merges_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write("\n".join(A			)			)






	def     UpperCamelCase_  (	self :			List[str]							,**A :			List[str]			):
					kwargs.update(self.special_tokens_map			)
					return CTRLTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Tuple			):
					__A             =			"adapt react readapt apt"
					__A             =			"adapt react readapt apt"
					return input_text, output_text







	def     UpperCamelCase_  (	self :			Any			):
					__A             =			CTRLTokenizer(self.vocab_file							,self.merges_file							,**self.special_tokens_map			)
					__A             =			"adapt react readapt apt"
					__A             =			"adapt re@@ a@@ c@@ t re@@ adapt apt".split()
					__A             =			tokenizer.tokenize(A			)
					self.assertListEqual(A							,A			)

					__A             =			tokens + [tokenizer.unk_token]

					__A             =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)							,A			)
