

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a:    Optional[int] 							=			16
_a:    Union[str, Any] 							=			32



def       __lowerCAmelCase      (						A       ,     A = 16    ):
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained("bert-base-cased"    )
	UpperCAmelCase_       				=			load_dataset("glue"       ,     "mrpc"    )

	def tokenize_function(A    ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_       				=			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
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_       				=			datasets.map(
		    A       ,     batched=A       ,     remove_columns=["idx", "sentence1", "sentence2"]       ,     )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCAmelCase_       				=			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.
		UpperCAmelCase_       				=			128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			UpperCAmelCase_       				=			16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_       				=			8
		else:
			UpperCAmelCase_       				=			None

		return tokenizer.pad(
		    A       ,     padding="longest"       ,     max_length=A       ,     pad_to_multiple_of=A       ,     return_tensors="pt"       ,     )

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

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_a:    Tuple 							=			mocked_dataloaders  # noqa: F811



def       __lowerCAmelCase      (						A       ,     A    ):
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,     A    ) == "1":
		UpperCAmelCase_       				=			2
	# New Code #
	UpperCAmelCase_       				=			int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	UpperCAmelCase_       				=			Accelerator(
	    cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     gradient_accumulation_steps=A    )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"    )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_       				=			config["lr"]
	UpperCAmelCase_       				=			int(config["num_epochs"]    )
	UpperCAmelCase_       				=			int(config["seed"]    )
	UpperCAmelCase_       				=			int(config["batch_size"]    )

	UpperCAmelCase_       				=			evaluate.load("glue"       ,     "mrpc"    )

	set_seed(A    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			get_dataloaders(A       ,     A    )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_       				=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,     return_dict=A    )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	UpperCAmelCase_       				=			model.to(accelerator.device    )

	# Instantiate optimizer
	UpperCAmelCase_       				=			AdamW(params=model.parameters()       ,     lr=A    )

	# Instantiate scheduler
	UpperCAmelCase_       				=			get_linear_schedule_with_warmup(
	    optimizer=A       ,     num_warmup_steps=100       ,     num_training_steps=(len(A    ) * num_epochs)       ,     )

	# 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.
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.prepare(
	    A       ,     A       ,     A       ,     A       ,     A    )

	# Now we train the model
	for epoch in range(A    ):
		model.train()
		for step, batch in enumerate(A    ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device    )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(A    ):
				UpperCAmelCase_       				=			model(**A    )
				UpperCAmelCase_       				=			output.loss
				accelerator.backward(A    )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		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():
				UpperCAmelCase_       				=			model(**A    )
			UpperCAmelCase_       				=			outputs.logits.argmax(dim=-1    )
			UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
			metric.add_batch(
			    predictions=A       ,     references=A       ,     )

		UpperCAmelCase_       				=			metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"       ,     A    )



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			argparse.ArgumentParser(description="Simple example of training script."    )
	parser.add_argument(
	    "--mixed_precision"       ,     type=A       ,     default=A       ,     choices=["no", "fp16", "bf16", "fp8"]       ,     help="Whether to use mixed precision. Choose"
	    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
	    "and an Nvidia Ampere GPU."       ,     )
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"       ,     type=A       ,     default=1       ,     help="The number of minibatches to be ran before gradients are accumulated."       ,     )
	parser.add_argument("--cpu"       ,     action="store_true"       ,     help="If passed, will train on the CPU."    )
	UpperCAmelCase_       				=			parser.parse_args()
	UpperCAmelCase_       				=			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(A       ,     A    )


if __name__ == "__main__":
					main()


import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a:    Dict 							=			logging.get_logger(__name__)

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]="</s>"		,	lowerCAmelCase	:						str="<unk>"		,	lowerCAmelCase	:						Optional[int]="<pad>"		,	lowerCAmelCase	:						List[str]=125		,	lowerCAmelCase	:						List[Any]=None		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







		if extra_ids > 0 and additional_special_tokens is None:
			UpperCAmelCase_       				=			[F"<extra_id_{i}>" for i in range(lowerCAmelCase							)]
		elif extra_ids > 0 and additional_special_tokens is not None:
			# Check that we have the right number of extra_id special tokens
			UpperCAmelCase_       				=			len(set(filter(lambda lowerCAmelCase							: bool("extra_id" in str(lowerCAmelCase							)							)		,	lowerCAmelCase							)							)							)
			if extra_tokens != extra_ids:
				raise ValueError(
				    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
				    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
				    " extra_ids tokens"							)

		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else pad_token
		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else eos_token
		UpperCAmelCase_       				=			AddedToken(lowerCAmelCase		,	lstrip=lowerCAmelCase		,	rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase		,	lowerCAmelCase							) else unk_token

		super().__init__(
		    eos_token=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	extra_ids=lowerCAmelCase		,	additional_special_tokens=lowerCAmelCase		,	**lowerCAmelCase		,	)

		UpperCAmelCase_       				=			extra_ids

		UpperCAmelCase_       				=			2**8  # utf is 8 bits

		# define special tokens dict
		UpperCAmelCase_       				=			{
		    self.pad_token: 0,
		    self.eos_token: 1,
		    self.unk_token: 2,
		}
		UpperCAmelCase_       				=			len(self.special_tokens_encoder							)
		UpperCAmelCase_       				=			len(lowerCAmelCase							)
		for i, token in enumerate(lowerCAmelCase							):
			UpperCAmelCase_       				=			self.vocab_size + i - n
		UpperCAmelCase_       				=			{v: k for k, v in self.special_tokens_encoder.items()}




	@property
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = False							):


		'''simple docstring'''







		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=lowerCAmelCase		,	token_ids_a=lowerCAmelCase		,	already_has_special_tokens=lowerCAmelCase							)

		# normal case: some special tokens
		if token_ids_a is None:
			return ([0] * len(lowerCAmelCase							)) + [1]
		return ([0] * len(lowerCAmelCase							)) + [1] + ([0] * len(lowerCAmelCase							)) + [1]




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[int]							):


		'''simple docstring'''







		if len(lowerCAmelCase							) > 0 and token_ids[-1] == self.eos_token_id:
			warnings.warn(
			    F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
			    " eos tokens being added."							)
			return token_ids
		else:
			return token_ids + [self.eos_token_id]




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[self.eos_token_id]

		if token_ids_a is None:
			return len(token_ids_a + eos							) * [0]
		return len(token_ids_a + eos + token_ids_a + eos							) * [0]




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._add_eos_if_not_present(lowerCAmelCase							)
		if token_ids_a is None:
			return token_ids_a
		else:
			UpperCAmelCase_       				=			self._add_eos_if_not_present(lowerCAmelCase							)
			return token_ids_a + token_ids_a




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[chr(lowerCAmelCase							) for i in text.encode("utf-8"							)]
		return tokens




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		if token in self.special_tokens_encoder:
			UpperCAmelCase_       				=			self.special_tokens_encoder[token]
		elif token in self.added_tokens_encoder:
			UpperCAmelCase_       				=			self.added_tokens_encoder[token]
		elif len(lowerCAmelCase							) != 1:
			UpperCAmelCase_       				=			self.unk_token_id
		else:
			UpperCAmelCase_       				=			ord(lowerCAmelCase							) + self._num_special_tokens
		return token_id




	def       __A					(		self	:						str		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		if index in self.special_tokens_decoder:
			UpperCAmelCase_       				=			self.special_tokens_decoder[index]
		else:
			UpperCAmelCase_       				=			chr(index - self._num_special_tokens							)
		return token




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			b""
		for token in tokens:
			if token in self.special_tokens_decoder:
				UpperCAmelCase_       				=			self.special_tokens_decoder[token].encode("utf-8"							)
			elif token in self.added_tokens_decoder:
				UpperCAmelCase_       				=			self.special_tokens_decoder[token].encode("utf-8"							)
			elif token in self.special_tokens_encoder:
				UpperCAmelCase_       				=			token.encode("utf-8"							)
			elif token in self.added_tokens_encoder:
				UpperCAmelCase_       				=			token.encode("utf-8"							)
			else:
				UpperCAmelCase_       				=			bytes([ord(lowerCAmelCase							)]							)
			bstring += tok_string
		UpperCAmelCase_       				=			bstring.decode("utf-8"		,	errors="ignore"							)
		return string




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		return ()