
"""simple docstring"""


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_lowercase							   =				16
_lowercase							   =				32






def       _snake_case				(		snake_case__   :     Accelerator	,      snake_case__   :     DatasetDict	,      snake_case__   :     List[int]	,      snake_case__   :     List[int]	,      snake_case__   :     int = 16     ):
	A						   =   AutoTokenizer.from_pretrained('bert-base-cased'     )
	A						   =   DatasetDict(
	    {
	        'train': dataset['train'].select(snake_case__     ),
	        'validation': dataset['train'].select(snake_case__     ),
	        'test': dataset['validation'],
	    }     )

	def tokenize_function(snake_case__   :     Optional[Any]     ):
		# max_length=None => use the model max length (it's actually the default)
		A						   =   tokenizer(examples['sentence1']	,      examples['sentence2']	,      truncation=snake_case__	,      max_length=snake_case__     )
		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():
		A						   =   datasets.map(
		    snake_case__	,      batched=snake_case__	,      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
	A						   =   tokenized_datasets.rename_column('label'	,      'labels'     )

	def collate_fn(snake_case__   :     Optional[Any]     ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A						   =   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":
			A						   =   16
		elif accelerator.mixed_precision != "no":
			A						   =   8
		else:
			A						   =   None

		return tokenizer.pad(
		    snake_case__	,      padding='longest'	,      max_length=snake_case__	,      pad_to_multiple_of=snake_case__	,      return_tensors='pt'	,      )

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

	A						   =   DataLoader(
	    tokenized_datasets['test']	,      shuffle=snake_case__	,      collate_fn=snake_case__	,      batch_size=snake_case__     )

	return train_dataloader, eval_dataloader, test_dataloader






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     List[str]     ):
	# New Code #
	A						   =   []
	# Download the dataset
	A						   =   load_dataset('glue'	,      'mrpc'     )
	# Create our splits
	A						   =   StratifiedKFold(n_splits=int(args.num_folds     )     )
	# Initialize accelerator
	A						   =   Accelerator(cpu=args.cpu	,      mixed_precision=args.mixed_precision     )
	# 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						   =   evaluate.load('glue'	,      'mrpc'     )

	# If the batch size is too big we use gradient accumulation
	A						   =   1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A						   =   batch_size // MAX_GPU_BATCH_SIZE
		A						   =   MAX_GPU_BATCH_SIZE

	set_seed(snake_case__     )

	# New Code #
	# Create our folds:
	A						   =   kfold.split(np.zeros(datasets['train'].num_rows     )	,      datasets['train']['label']     )
	A						   =   []
	# Iterate over them
	for i, (train_idxs, valid_idxs) in enumerate(snake_case__     ):
		A   ,      A   ,      A						   =   get_fold_dataloaders(
		    snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A						   =   AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,      return_dict=snake_case__     )

		# 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).
		A						   =   model.to(accelerator.device     )

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

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

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

		# Now we train the model
		for epoch in range(snake_case__     ):
			model.train()
			for step, batch in enumerate(snake_case__     ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device     )
				A						   =   model(**snake_case__     )
				A						   =   outputs.loss
				A						   =   loss / gradient_accumulation_steps
				accelerator.backward(snake_case__     )
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()

			model.eval()
			for step, batch in enumerate(snake_case__     ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device     )
				with torch.no_grad():
					A						   =   model(**snake_case__     )
				A						   =   outputs.logits.argmax(dim=-1     )
				A   ,      A						   =   accelerator.gather_for_metrics((predictions, batch['labels'])     )
				metric.add_batch(
				    predictions=snake_case__	,      references=snake_case__	,      )

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

		# New Code #
		# We also run predictions on the test set at the very end
		A						   =   []
		for step, batch in enumerate(snake_case__     ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device     )
			with torch.no_grad():
				A						   =   model(**snake_case__     )
			A						   =   outputs.logits
			A   ,      A						   =   accelerator.gather_for_metrics((predictions, batch['labels'])     )
			fold_predictions.append(predictions.cpu()     )
			if i == 0:
				# We need all of the test predictions
				test_references.append(references.cpu()     )
        # Use accelerator.print to print only on the main process.
		test_predictions.append(torch.cat(snake_case__	,      dim=0     )     )
		# We now need to release all our memory and get rid of the current model, optimizer, etc
		accelerator.free_memory()
	# New Code #
	# Finally we check the accuracy of our folded results:
	A						   =   torch.cat(snake_case__	,      dim=0     )
	A						   =   torch.stack(snake_case__	,      dim=0     ).sum(dim=0     ).div(int(args.num_folds     )     ).argmax(dim=-1     )
	A						   =   metric.compute(predictions=snake_case__	,      references=snake_case__     )
	accelerator.print('Average test metrics from all folds:'	,      snake_case__     )






def       _snake_case				(		):
	A						   =   argparse.ArgumentParser(description='Simple example of training script.'     )
	parser.add_argument(
	    '--mixed_precision'	,      type=snake_case__	,      default=snake_case__	,      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.'	,      )
	parser.add_argument('--cpu'	,      action='store_true'	,      help='If passed, will train on the CPU.'     )
	# New Code #
	parser.add_argument('--num_folds'	,      type=snake_case__	,      default=3	,      help='The number of splits to perform across the dataset'     )
	A						   =   parser.parse_args()
	A						   =   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__	,      snake_case__     )


if __name__ == "__main__":
		main()

"""simple docstring"""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowercase							   =				data_utils.TransfoXLTokenizer
_lowercase							   =				data_utils.TransfoXLCorpus
_lowercase							   =				data_utils
_lowercase							   =				data_utils






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Dict	,      snake_case__   :     Tuple	,      snake_case__   :     int     ):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(snake_case__	,      'rb'     ) as fp:
			A						   =   pickle.load(snake_case__	,      encoding='latin1'     )
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		A						   =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F'Save vocabulary to {pytorch_vocab_dump_path}'     )
		A						   =   corpus.vocab.__dict__
		torch.save(snake_case__	,      snake_case__     )

		A						   =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab'	,      snake_case__     )
		A						   =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F'Save dataset to {pytorch_dataset_dump_path}'     )
		torch.save(snake_case__	,      snake_case__     )

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		A						   =   os.path.abspath(snake_case__     )
		A						   =   os.path.abspath(snake_case__     )

		print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'     )
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			A						   =   TransfoXLConfig()
		else:
			A						   =   TransfoXLConfig.from_json_file(snake_case__     )
		print(F'Building PyTorch model from configuration: {config}'     )
		A						   =   TransfoXLLMHeadModel(snake_case__     )

		A						   =   load_tf_weights_in_transfo_xl(snake_case__	,      snake_case__	,      snake_case__     )
		# Save pytorch-model
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		A						   =   os.path.join(snake_case__	,      snake_case__     )
		print(F'Save PyTorch model to {os.path.abspath(snake_case__     )}'     )
		torch.save(model.state_dict()	,      snake_case__     )
		print(F'Save configuration file to {os.path.abspath(snake_case__     )}'     )
		with open(snake_case__	,      'w'	,      encoding='utf-8'     ) as f:
			f.write(config.to_json_string()     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''',
		    default='''''',
		    type=str,
		    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
		)
		parser.add_argument(
		    '''--transfo_xl_config_file''',
		    default='''''',
		    type=str,
		    help=(
		        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--transfo_xl_dataset_file''',
		    default='''''',
		    type=str,
		    help='''An optional dataset file to be converted in a vocabulary.''',
		)
		_lowercase							   =				parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)