

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


lowerCAmelCase							:  List[str]           =						16
lowerCAmelCase							:  Optional[int]           =						32







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase = 16    ,     _UpperCAmelCase = "bert-base-cased"   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained(_a   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	load_dataset("glue"    ,     "mrpc"   )

						def tokenize_function(_UpperCAmelCase   ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_:			List[str]  		=	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
						SCREAMING_SNAKE_CASE_:			Dict  		=	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
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tokenized_datasets.rename_column("label"    ,     "labels"   )

						def collate_fn(_UpperCAmelCase   ):
												# 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_28    ,     return_tensors="pt"   )
												return tokenizer.pad(_a    ,     padding="longest"    ,     return_tensors="pt"   )

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:			Dict  		=	DataLoader(
						    tokenized_datasets["train"]    ,     shuffle=_a    ,     collate_fn=_a    ,     batch_size=_a   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(
						    tokenized_datasets["validation"]    ,     shuffle=_a    ,     collate_fn=_a    ,     batch_size=_a   )

						return train_dataloader, eval_dataloader







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						model.eval()
						SCREAMING_SNAKE_CASE_:			Any  		=	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():
																		SCREAMING_SNAKE_CASE_:			Any  		=	model(**_a   )
												SCREAMING_SNAKE_CASE_:			Any  		=	outputs.logits.argmax(dim=-1   )
												# It is slightly faster to call this once, than multiple times
												SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Dict  		=	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:
																								SCREAMING_SNAKE_CASE_:			List[str]  		=	predictions[: len(eval_dataloader.dataset   ) - samples_seen]
																								SCREAMING_SNAKE_CASE_:			List[Any]  		=	references[: len(eval_dataloader.dataset   ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=_a    ,     references=_a    ,     )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	metric.compute()
						return eval_metric["accuracy"]







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	Accelerator()

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

						set_seed(_a   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_dataloaders(_a    ,     _a    ,     _a   )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoModelForSequenceClassification.from_pretrained(_a    ,     return_dict=_a   )

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						SCREAMING_SNAKE_CASE_:			List[str]  		=	optimizer_cls(params=model.parameters()    ,     lr=_a   )

						if accelerator.state.deepspeed_plugin is not None:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												SCREAMING_SNAKE_CASE_:			int  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	(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
						):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	get_linear_schedule_with_warmup(
												    optimizer=_a    ,     num_warmup_steps=0    ,     num_training_steps=_a    ,     )
						else:
												SCREAMING_SNAKE_CASE_:			int  		=	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.
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	accelerator.prepare(
						    _a    ,     _a    ,     _a    ,     _a    ,     _a   )

						# We need to keep track of how many total steps we have iterated over
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						# We also need to keep track of the stating epoch so files are named properly
						SCREAMING_SNAKE_CASE_:			str  		=	0
						SCREAMING_SNAKE_CASE_:			int  		=	evaluate.load("glue"    ,     "mrpc"   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	num_epochs

						if args.partial_train_epoch is not None:
												SCREAMING_SNAKE_CASE_:			str  		=	args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint   )
												SCREAMING_SNAKE_CASE_:			Dict  		=	args.resume_from_checkpoint.split("epoch_"   )[1]
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	int(_a   ) + 1
												SCREAMING_SNAKE_CASE_:			str  		=	evaluation_loop(_a    ,     _a    ,     _a    ,     _a   )
												accelerator.print("resumed checkpoint performance:"    ,     _a   )
												accelerator.print("resumed checkpoint's scheduler's lr:"    ,     lr_scheduler.get_lr()[0]   )
												accelerator.print("resumed optimizers's lr:"    ,     optimizer.param_groups[0]["lr"]   )
												with open(os.path.join(args.output_dir    ,     f"state_{starting_epoch-1}.json"   )    ,     "r"   ) as f:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	json.load(_a   )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						SCREAMING_SNAKE_CASE_:			List[str]  		=	{}
						for epoch in range(_a    ,     _a   ):
												model.train()
												for step, batch in enumerate(_a   ):
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model(**_a   )
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	outputs.loss
																		SCREAMING_SNAKE_CASE_:			str  		=	loss / gradient_accumulation_steps
																		accelerator.backward(_a   )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												SCREAMING_SNAKE_CASE_:			Any  		=	f"epoch_{epoch}"
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.join(args.output_dir    ,     _a   )
												accelerator.save_state(_a   )
												SCREAMING_SNAKE_CASE_:			Dict  		=	evaluation_loop(_a    ,     _a    ,     _a    ,     _a   )
												SCREAMING_SNAKE_CASE_:			str  		=	accuracy
												SCREAMING_SNAKE_CASE_:			Any  		=	lr_scheduler.get_lr()[0]
												SCREAMING_SNAKE_CASE_:			Tuple  		=	optimizer.param_groups[0]["lr"]
												SCREAMING_SNAKE_CASE_:			Tuple  		=	epoch
												SCREAMING_SNAKE_CASE_:			Tuple  		=	overall_step
												accelerator.print(f"epoch {epoch}:"    ,     _a   )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir    ,     f"state_{epoch}.json"   )    ,     "w"   ) as f:
																								json.dump(_a    ,     _a   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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(
						    "--resume_from_checkpoint"    ,     type=_a    ,     default=_a    ,     help="If the training should continue from a checkpoint folder."    ,     )
						parser.add_argument(
						    "--partial_train_epoch"    ,     type=_a    ,     default=_a    ,     help="If passed, the training will stop after this number of epochs."    ,     )
						parser.add_argument(
						    "--num_epochs"    ,     type=_a    ,     default=2    ,     help="Number of train epochs."    ,     )
						SCREAMING_SNAKE_CASE_:			Any  		=	parser.parse_args()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

						training_function(_a    ,     _a   )


if __name__ == "__main__":
						main()









import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM



@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			int  		=	TextStreamer(lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(greedy_ids[0])

									SCREAMING_SNAKE_CASE_:			int  		=	TextIteratorStreamer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	""
									for new_text in streamer:
															streamer_text += new_text

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	greedy_ids[:, input_ids.shape[1] :]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.decode(new_greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Dict  		=	TextStreamer(lowerCAmelCase__       ,						skip_prompt=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Any  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
									# with actual models -- the dummy models' tokenizers are not aligned with their models, and
									# `skip_special_tokens=True` has no effect on them
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained("distilgpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, 5)       ,						device=lowerCAmelCase__).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TextStreamer(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=1       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									SCREAMING_SNAKE_CASE_:			str  		=	cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="pt")
									self.assertEqual(streamer_text_tokenized.input_ids.shape       ,						(1, 1))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	TextIteratorStreamer(lowerCAmelCase__       ,						timeout=0.001)
									SCREAMING_SNAKE_CASE_:			Any  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	""
															for new_text in streamer:
																					streamer_text += new_text






