




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__     :     str		    =       16
SCREAMING_SNAKE_CASE__     :     List[str]		    =       32

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE = 16       ,    SCREAMING_SNAKE_CASE = "bert-base-cased" )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						load_dataset('glue'       ,    'mrpc' )

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

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

			# 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_								=						tokenized_datasets.rename_column('label'       ,    'labels' )

			def collate_fn(SCREAMING_SNAKE_CASE ):
						# 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(SCREAMING_SNAKE_CASE       ,    padding='max_length'       ,    max_length=1_28       ,    return_tensors='pt' )
						return tokenizer.pad(SCREAMING_SNAKE_CASE       ,    padding='longest'       ,    return_tensors='pt' )

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

			return train_dataloader, eval_dataloader

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			model.eval()
			SCREAMING_SNAKE_CASE_								=						0
			for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
						# 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_								=						model(**SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						outputs.logits.argmax(dim=-1 )
						# It is slightly faster to call this once, than multiple times
						SCREAMING_SNAKE_CASE_						,		SCREAMING_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(SCREAMING_SNAKE_CASE ) - 1:
												SCREAMING_SNAKE_CASE_								=						predictions[: len(eval_dataloader.dataset ) - samples_seen]
												SCREAMING_SNAKE_CASE_								=						references[: len(eval_dataloader.dataset ) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=SCREAMING_SNAKE_CASE       ,    references=SCREAMING_SNAKE_CASE       ,    )

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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Accelerator()

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

			set_seed(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						get_dataloaders(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

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

			if accelerator.state.deepspeed_plugin is not None:
						SCREAMING_SNAKE_CASE_								=						accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						SCREAMING_SNAKE_CASE_								=						1
			SCREAMING_SNAKE_CASE_								=						(len(SCREAMING_SNAKE_CASE ) * 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_								=						get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE       ,    num_warmup_steps=0       ,    num_training_steps=SCREAMING_SNAKE_CASE       ,    )
			else:
						SCREAMING_SNAKE_CASE_								=						DummyScheduler(SCREAMING_SNAKE_CASE       ,    total_num_steps=SCREAMING_SNAKE_CASE       ,    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_								=						accelerator.prepare(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

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

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

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

						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(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

def lowercase    (  )			->							int:


			'''simple docstring'''







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

			training_function(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
