

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


A									=  16
A									=  32





def a(lowercase__      ,			lowercase__ = 16      ,			lowercase__ = "bert-base-cased"						):





	'''simple docstring'''
	snake_case_   		=			AutoTokenizer.from_pretrained(lowercase__						)
	snake_case_   		=			load_dataset('glue'      ,			'mrpc'						)

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

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

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

	def collate_fn(lowercase__						):
		# 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(lowercase__      ,			padding='max_length'      ,			max_length=128      ,			return_tensors='pt'						)
		return tokenizer.pad(lowercase__      ,			padding='longest'      ,			return_tensors='pt'						)

	# Instantiate dataloaders.
	snake_case_   		=			DataLoader(
	    tokenized_datasets['train']      ,			shuffle=lowercase__      ,			collate_fn=lowercase__      ,			batch_size=lowercase__						)
	snake_case_   		=			DataLoader(
	    tokenized_datasets['validation']      ,			shuffle=lowercase__      ,			collate_fn=lowercase__      ,			batch_size=lowercase__						)

	return train_dataloader, eval_dataloader





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





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

	snake_case_   		=			metric.compute()
	return eval_metric["accuracy"]





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	# Initialize accelerator
	snake_case_   		=			Accelerator()

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

	set_seed(lowercase__						)
	snake_case_		,							snake_case_   		=			get_dataloaders(lowercase__      ,			lowercase__      ,			lowercase__						)

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

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

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_   		=			accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		snake_case_   		=			1
	snake_case_   		=			(len(lowercase__						) * num_epochs) // gradient_accumulation_steps

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

	# We need to keep track of how many total steps we have iterated over
	snake_case_   		=			0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case_   		=			0
	snake_case_   		=			evaluate.load('glue'      ,			'mrpc'						)
	snake_case_   		=			num_epochs

	if args.partial_train_epoch is not None:
		snake_case_   		=			args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint						)
		snake_case_   		=			args.resume_from_checkpoint.split('epoch_'						)[1]
		snake_case_   		=			''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		snake_case_   		=			int(lowercase__						) + 1
		snake_case_   		=			evaluation_loop(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
		accelerator.print('resumed checkpoint performance:'      ,			lowercase__						)
		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:
			snake_case_   		=			json.load(lowercase__						)
			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
	snake_case_   		=			{}
	for epoch in range(lowercase__      ,			lowercase__						):
		model.train()
		for step, batch in enumerate(lowercase__						):
			snake_case_   		=			model(**lowercase__						)
			snake_case_   		=			outputs.loss
			snake_case_   		=			loss / gradient_accumulation_steps
			accelerator.backward(lowercase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		snake_case_   		=			f"""epoch_{epoch}"""
		snake_case_   		=			os.path.join(args.output_dir      ,			lowercase__						)
		accelerator.save_state(lowercase__						)
		snake_case_   		=			evaluation_loop(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
		snake_case_   		=			accuracy
		snake_case_   		=			lr_scheduler.get_lr()[0]
		snake_case_   		=			optimizer.param_groups[0]['lr']
		snake_case_   		=			epoch
		snake_case_   		=			overall_step
		accelerator.print(f"""epoch {epoch}:"""      ,			lowercase__						)

		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(lowercase__      ,			lowercase__						)





def a():





	'''simple docstring'''
	snake_case_   		=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'						)
	parser.add_argument(
	    '--model_name_or_path'      ,			type=lowercase__      ,			default='bert-base-cased'      ,			help='Path to pretrained model or model identifier from huggingface.co/models.'      ,			required=lowercase__      ,			)
	parser.add_argument(
	    '--output_dir'      ,			type=lowercase__      ,			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=lowercase__      ,			default=lowercase__      ,			help='If the training should continue from a checkpoint folder.'      ,			)
	parser.add_argument(
	    '--partial_train_epoch'      ,			type=lowercase__      ,			default=lowercase__      ,			help='If passed, the training will stop after this number of epochs.'      ,			)
	parser.add_argument(
	    '--num_epochs'      ,			type=lowercase__      ,			default=2      ,			help='Number of train epochs.'      ,			)
	snake_case_   		=			parser.parse_args()
	snake_case_   		=			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

	training_function(lowercase__      ,			lowercase__						)


if __name__ == "__main__":
		main()



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A									=  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MraForMaskedLM',
		    'MraForMultipleChoice',
		    'MraForQuestionAnswering',
		    'MraForSequenceClassification',
		    'MraForTokenClassification',
		    'MraLayer',
		    'MraModel',
		    'MraPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure)
