



'''simple docstring'''

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_A				:    str     						=logging.getLogger(__name__)







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			int:
	_lowercase					:       Any       =		np.argmax(_lowercase,				axis=1					)
	return np.sum(outputs == labels					)







def 						__UpperCamelCase				(							_lowercase					) ->			Union[str, Any]:
	with open(_lowercase,				encoding='utf_8'					) as f:
		_lowercase					:       List[str]       =		csv.reader(_lowercase					)
		_lowercase					:       Tuple       =		[]
		next(_lowercase					)  # skip the first line
		for line in tqdm(_lowercase					):
			output.append((' '.join(line[1:5]					), line[5], line[6], int(line[-1]					) - 1)					)
	return output







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			Dict:
	_lowercase					:       Any       =		[]
	for dataset in encoded_datasets:
		_lowercase					:       int       =		len(_lowercase					)
		_lowercase					:       List[str]       =		np.zeros((n_batch, 2, input_len),				dtype=np.intaa					)
		_lowercase					:       Optional[int]       =		np.zeros((n_batch, 2),				dtype=np.intaa					)
		_lowercase					:       str       =		np.full((n_batch, 2, input_len),				fill_value=-100,				dtype=np.intaa					)
		_lowercase					:       Any       =		np.zeros((n_batch,),				dtype=np.intaa					)
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(_lowercase					):
			_lowercase					:       Optional[Any]       =		[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			_lowercase					:       Dict       =		[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			_lowercase					:       Optional[int]       =		with_conta
			_lowercase					:       List[Any]       =		with_conta
			_lowercase					:       Optional[int]       =		len(_lowercase					) - 1
			_lowercase					:       Optional[Any]       =		len(_lowercase					) - 1
			_lowercase					:       List[Any]       =		with_conta
			_lowercase					:       Tuple       =		with_conta
			_lowercase					:       Any       =		mc_label
		_lowercase					:       List[Any]       =		(input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(_lowercase					) for t in all_inputs					)					)
	return tensor_datasets







def 						__UpperCamelCase				(							) ->			List[Any]:
	_lowercase					:       Tuple       =		argparse.ArgumentParser()
	parser.add_argument('--model_name',				type=_lowercase,				default='openai-gpt',				help='pretrained model name'					)
	parser.add_argument('--do_train',				action='store_true',				help='Whether to run training.'					)
	parser.add_argument('--do_eval',				action='store_true',				help='Whether to run eval on the dev set.'					)
	parser.add_argument(
	    '--output_dir',				default=_lowercase,				type=_lowercase,				required=_lowercase,				help='The output directory where the model predictions and checkpoints will be written.',				)
	parser.add_argument('--train_dataset',				type=_lowercase,				default=''					)
	parser.add_argument('--eval_dataset',				type=_lowercase,				default=''					)
	parser.add_argument('--seed',				type=_lowercase,				default=42					)
	parser.add_argument('--num_train_epochs',				type=_lowercase,				default=3					)
	parser.add_argument('--train_batch_size',				type=_lowercase,				default=8					)
	parser.add_argument('--eval_batch_size',				type=_lowercase,				default=16					)
	parser.add_argument('--adam_epsilon',				default=1E-8,				type=_lowercase,				help='Epsilon for Adam optimizer.'					)
	parser.add_argument('--max_grad_norm',				type=_lowercase,				default=1					)
	parser.add_argument(
	    '--max_steps',				default=-1,				type=_lowercase,				help=(
	        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
	    ),				)
	parser.add_argument(
	    '--gradient_accumulation_steps',				type=_lowercase,				default=1,				help='Number of updates steps to accumulate before                        performing a backward/update pass.',				)
	parser.add_argument('--learning_rate',				type=_lowercase,				default=6.2_5E-5					)
	parser.add_argument('--warmup_steps',				default=0,				type=_lowercase,				help='Linear warmup over warmup_steps.'					)
	parser.add_argument('--lr_schedule',				type=_lowercase,				default='warmup_linear'					)
	parser.add_argument('--weight_decay',				type=_lowercase,				default=0.0_1					)
	parser.add_argument('--lm_coef',				type=_lowercase,				default=0.9					)
	parser.add_argument('--n_valid',				type=_lowercase,				default=374					)

	parser.add_argument('--server_ip',				type=_lowercase,				default='',				help='Can be used for distant debugging.'					)
	parser.add_argument('--server_port',				type=_lowercase,				default='',				help='Can be used for distant debugging.'					)
	_lowercase					:       Dict       =		parser.parse_args()
	print(_lowercase					)

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print('Waiting for debugger attach'					)
		ptvsd.enable_attach(address=(args.server_ip, args.server_port),				redirect_output=_lowercase					)
		ptvsd.wait_for_attach()

	random.seed(args.seed					)
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	torch.cuda.manual_seed_all(args.seed					)

	_lowercase					:       int       =		torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
	_lowercase					:       Union[str, Any]       =		torch.cuda.device_count()
	logger.info('device: {}, n_gpu {}'.format(_lowercase,				_lowercase					)					)

	if not args.do_train and not args.do_eval:
		raise ValueError('At least one of `do_train` or `do_eval` must be True.'					)

	if not os.path.exists(args.output_dir					):
		os.makedirs(args.output_dir					)

	# Load tokenizer and model
	# This loading functions also add new tokens and embeddings called `special tokens`
	# These new embeddings will be fine-tuned on the RocStories dataset
	_lowercase					:       Union[str, Any]       =		['_start_', '_delimiter_', '_classify_']
	_lowercase					:       Optional[int]       =		OpenAIGPTTokenizer.from_pretrained(args.model_name					)
	tokenizer.add_tokens(_lowercase					)
	_lowercase					:       Optional[int]       =		tokenizer.convert_tokens_to_ids(_lowercase					)
	_lowercase					:       Tuple       =		OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
	model.resize_token_embeddings(len(_lowercase					)					)
	model.to(_lowercase					)

	# Load and encode the datasets
	def tokenize_and_encode(_lowercase					):
		if isinstance(_lowercase,				_lowercase					):
			return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_lowercase					)					)
		elif isinstance(_lowercase,				_lowercase					):
			return obj
		return [tokenize_and_encode(_lowercase					) for o in obj]

	logger.info('Encoding dataset...'					)
	_lowercase					:       Any       =		load_rocstories_dataset(args.train_dataset					)
	_lowercase					:       List[str]       =		load_rocstories_dataset(args.eval_dataset					)
	_lowercase					:       Dict       =		(train_dataset, eval_dataset)
	_lowercase					:       Optional[int]       =		tokenize_and_encode(_lowercase					)

	# Compute the max input length for the Transformer
	_lowercase					:       Optional[Any]       =		model.config.n_positions // 2 - 2
	_lowercase					:       List[Any]       =		max(
	    len(story[:max_length]					) + max(len(conta[:max_length]					),				len(conta[:max_length]					)					) + 3
	    for dataset in encoded_datasets
	    for story, conta, conta, _ in dataset					)
	_lowercase					:       List[str]       =		min(_lowercase,				model.config.n_positions					)  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	_lowercase					:       Optional[int]       =		pre_process_datasets(_lowercase,				_lowercase,				_lowercase,				*_lowercase					)
	_lowercase  ,    _lowercase					:       Union[str, Any]       =		tensor_datasets[0], tensor_datasets[1]

	_lowercase					:       Any       =		TensorDataset(*_lowercase					)
	_lowercase					:       Optional[Any]       =		RandomSampler(_lowercase					)
	_lowercase					:       Union[str, Any]       =		DataLoader(_lowercase,				sampler=_lowercase,				batch_size=args.train_batch_size					)

	_lowercase					:       Optional[int]       =		TensorDataset(*_lowercase					)
	_lowercase					:       List[Any]       =		SequentialSampler(_lowercase					)
	_lowercase					:       Optional[Any]       =		DataLoader(_lowercase,				sampler=_lowercase,				batch_size=args.eval_batch_size					)

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			_lowercase					:       Tuple       =		args.max_steps
			_lowercase					:       List[str]       =		args.max_steps // (len(_lowercase					) // args.gradient_accumulation_steps) + 1
		else:
			_lowercase					:       Dict       =		len(_lowercase					) // args.gradient_accumulation_steps * args.num_train_epochs

		_lowercase					:       Optional[int]       =		list(model.named_parameters()					)
		_lowercase					:       Any       =		['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		_lowercase					:       Tuple       =		[
		    {
		        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay					)],
		        'weight_decay': args.weight_decay,
		    },
		    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay					)], 'weight_decay': 0.0},
		]
		_lowercase					:       Tuple       =		AdamW(_lowercase,				lr=args.learning_rate,				eps=args.adam_epsilon					)
		_lowercase					:       Optional[int]       =		get_linear_schedule_with_warmup(
		    _lowercase,				num_warmup_steps=args.warmup_steps,				num_training_steps=_lowercase					)

	if args.do_train:
		_lowercase  ,    _lowercase  ,    _lowercase					:       int       =		0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs					),				desc='Epoch'					):
			_lowercase					:       Optional[Any]       =		0
			_lowercase					:       Union[str, Any]       =		0
			_lowercase					:       Dict       =		tqdm(_lowercase,				desc='Training'					)
			for step, batch in enumerate(_lowercase					):
				_lowercase					:       Dict       =		tuple(t.to(_lowercase					) for t in batch					)
				_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Dict       =		batch
				_lowercase					:       Optional[Any]       =		model(_lowercase,				mc_token_ids=_lowercase,				lm_labels=_lowercase,				mc_labels=_lowercase					)
				_lowercase					:       Any       =		args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				_lowercase					:       str       =		(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				_lowercase					:       Dict       =		'Training loss: {:.2e} lr: {:.2e}'.format(_lowercase,				scheduler.get_lr()[0]					)

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		_lowercase					:       List[str]       =		model.module if hasattr(_lowercase,				'module'					) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		_lowercase					:       Optional[int]       =		os.path.join(args.output_dir,				_lowercase					)
		_lowercase					:       List[Any]       =		os.path.join(args.output_dir,				_lowercase					)

		torch.save(model_to_save.state_dict(),				_lowercase					)
		model_to_save.config.to_json_file(_lowercase					)
		tokenizer.save_vocabulary(args.output_dir					)

		# Load a trained model and vocabulary that you have fine-tuned
		_lowercase					:       List[str]       =		OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir					)
		_lowercase					:       Dict       =		OpenAIGPTTokenizer.from_pretrained(args.output_dir					)
		model.to(_lowercase					)

	if args.do_eval:
		model.eval()
		_lowercase  ,    _lowercase					:       List[Any]       =		0, 0
		_lowercase  ,    _lowercase					:       List[str]       =		0, 0
		for batch in tqdm(_lowercase,				desc='Evaluating'					):
			_lowercase					:       str       =		tuple(t.to(_lowercase					) for t in batch					)
			_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Any       =		batch
			with torch.no_grad():
				_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Tuple       =		model(
				    _lowercase,				mc_token_ids=_lowercase,				lm_labels=_lowercase,				mc_labels=_lowercase					)

			_lowercase					:       List[str]       =		mc_logits.detach().cpu().numpy()
			_lowercase					:       Any       =		mc_labels.to('cpu'					).numpy()
			_lowercase					:       int       =		accuracy(_lowercase,				_lowercase					)

			eval_loss += mc_loss.mean().item()
			eval_accuracy += tmp_eval_accuracy

			nb_eval_examples += input_ids.size(0					)
			nb_eval_steps += 1

		_lowercase					:       Tuple       =		eval_loss / nb_eval_steps
		_lowercase					:       Optional[int]       =		eval_accuracy / nb_eval_examples
		_lowercase					:       Tuple       =		tr_loss / nb_tr_steps if args.do_train else None
		_lowercase					:       List[Any]       =		{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

		_lowercase					:       Optional[Any]       =		os.path.join(args.output_dir,				'eval_results.txt'					)
		with open(_lowercase,				'w'					) as writer:
			logger.info('***** Eval results *****'					)
			for key in sorted(result.keys()					):
				logger.info('  %s = %s',				_lowercase,				str(result[key]					)					)
				writer.write('%s = %s\n' % (key, str(result[key]					))					)


if __name__ == "__main__":
		main()





'''simple docstring'''

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[str]=13     ,  UpperCamelCase_				:	Union[str, Any]=7     ,  UpperCamelCase_				:	Union[str, Any]=True     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	int=99     ,  UpperCamelCase_				:	Tuple=32     ,  UpperCamelCase_				:	List[str]=5     ,  UpperCamelCase_				:	Dict=4     ,  UpperCamelCase_				:	Tuple=37     ,  UpperCamelCase_				:	int="gelu"     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=2     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Union[str, Any]=4     ,  )  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       int       =		parent
						_lowercase					:       str       =		batch_size
						_lowercase					:       List[str]       =		seq_length
						_lowercase					:       Dict       =		is_training
						_lowercase					:       Optional[int]       =		use_attention_mask
						_lowercase					:       List[Any]       =		use_token_type_ids
						_lowercase					:       Union[str, Any]       =		use_labels
						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       List[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       int       =		num_attention_heads
						_lowercase					:       Optional[int]       =		intermediate_size
						_lowercase					:       Any       =		hidden_act
						_lowercase					:       List[str]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       Optional[int]       =		max_position_embeddings
						_lowercase					:       int       =		type_vocab_size
						_lowercase					:       Any       =		type_sequence_label_size
						_lowercase					:       Any       =		initializer_range
						_lowercase					:       str       =		num_choices




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

						_lowercase					:       int       =		None
						if self.use_attention_mask:
							_lowercase					:       Optional[int]       =		random_attention_mask([self.batch_size, self.seq_length]					)

						_lowercase					:       Any       =		None
						if self.use_token_type_ids:
							_lowercase					:       Optional[Any]       =		ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

						_lowercase					:       str       =		RoFormerConfig(
						    vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  is_decoder=UpperCamelCase_     ,  initializer_range=self.initializer_range     ,  )

						return config, input_ids, token_type_ids, attention_mask




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		self.prepare_config_and_inputs()
						_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		config_and_inputs
						_lowercase					:       Optional[int]       =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict






@require_flax
class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						True

					A_						    =						(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		FlaxRoFormerModelTester(self					)




					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[int]:



						'''simple docstring'''





						for model_class_name in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class_name.from_pretrained('junnyu/roformer_chinese_small'     ,  from_pt=UpperCamelCase_					)
							_lowercase					:       str       =		model(np.ones((1, 1)					)					)
							self.assertIsNotNone(UpperCamelCase_					)






@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
						_lowercase					:       Any       =		jnp.array([[0, 1, 2, 3, 4, 5]]					)
						_lowercase					:       int       =		model(UpperCamelCase_					)[0]

						_lowercase					:       Union[str, Any]       =		5_0000

						_lowercase					:       str       =		(1, 6, vocab_size)
						self.assertEqual(output.shape     ,  UpperCamelCase_					)

						_lowercase					:       int       =		jnp.array(
						    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

						self.assertTrue(jnp.allclose(output[:, :3, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)					)
