"""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  :  int=				logging.getLogger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       Dict						):

	'''simple docstring'''





	__snake_case    :							Tuple   	=				np.argmax(SCREAMING_SNAKE_CASE_    ,							axis=1						)
	return np.sum(outputs == labels						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[Any]						):

	'''simple docstring'''





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

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				[]
	for dataset in encoded_datasets:
		__snake_case    :							Any   	=				len(SCREAMING_SNAKE_CASE_						)
		__snake_case    :							List[str]   	=				np.zeros((n_batch, 2, input_len)    ,							dtype=np.intaa						)
		__snake_case    :							List[Any]   	=				np.zeros((n_batch, 2)    ,							dtype=np.intaa						)
		__snake_case    :							Union[str, Any]   	=				np.full((n_batch, 2, input_len)    ,							fill_value=-1_00    ,							dtype=np.intaa						)
		__snake_case    :							str   	=				np.zeros((n_batch,)    ,							dtype=np.intaa						)
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(SCREAMING_SNAKE_CASE_						):
			__snake_case    :							Dict   	=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__snake_case    :							Optional[Any]   	=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__snake_case    :							Dict   	=				with_conta
			__snake_case    :							List[Any]   	=				with_conta
			__snake_case    :							Tuple   	=				len(SCREAMING_SNAKE_CASE_						) - 1
			__snake_case    :							List[str]   	=				len(SCREAMING_SNAKE_CASE_						) - 1
			__snake_case    :							str   	=				with_conta
			__snake_case    :							List[str]   	=				with_conta
			__snake_case    :							Dict   	=				mc_label
		__snake_case    :							str   	=				(input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_						) for t in all_inputs						)						)
	return tensor_datasets
def 						__UpperCAmelCase       (					):

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				argparse.ArgumentParser()
	parser.add_argument('--model_name'    ,							type=SCREAMING_SNAKE_CASE_    ,							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=SCREAMING_SNAKE_CASE_    ,							type=SCREAMING_SNAKE_CASE_    ,							required=SCREAMING_SNAKE_CASE_    ,							help='The output directory where the model predictions and checkpoints will be written.'    ,							)
	parser.add_argument('--train_dataset'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=''						)
	parser.add_argument('--eval_dataset'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=''						)
	parser.add_argument('--seed'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=42						)
	parser.add_argument('--num_train_epochs'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=3						)
	parser.add_argument('--train_batch_size'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=8						)
	parser.add_argument('--eval_batch_size'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=16						)
	parser.add_argument('--adam_epsilon'    ,							default=1E-8    ,							type=SCREAMING_SNAKE_CASE_    ,							help='Epsilon for Adam optimizer.'						)
	parser.add_argument('--max_grad_norm'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=1						)
	parser.add_argument(
	    '--max_steps'    ,							default=-1    ,							type=SCREAMING_SNAKE_CASE_    ,							help=(
	        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
	    )    ,							)
	parser.add_argument(
	    '--gradient_accumulation_steps'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=1    ,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'    ,							)
	parser.add_argument('--learning_rate'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=6.25E-5						)
	parser.add_argument('--warmup_steps'    ,							default=0    ,							type=SCREAMING_SNAKE_CASE_    ,							help='Linear warmup over warmup_steps.'						)
	parser.add_argument('--lr_schedule'    ,							type=SCREAMING_SNAKE_CASE_    ,							default='warmup_linear'						)
	parser.add_argument('--weight_decay'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=0.01						)
	parser.add_argument('--lm_coef'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=0.9						)
	parser.add_argument('--n_valid'    ,							type=SCREAMING_SNAKE_CASE_    ,							default=3_74						)

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

	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=SCREAMING_SNAKE_CASE_						)
		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						)

	__snake_case    :							Optional[int]   	=				torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
	__snake_case    :							Dict   	=				torch.cuda.device_count()
	logger.info('device: {}, n_gpu {}'.format(SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_						)						)

	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
	__snake_case    :							Dict   	=				['_start_', '_delimiter_', '_classify_']
	__snake_case    :							List[Any]   	=				OpenAIGPTTokenizer.from_pretrained(args.model_name						)
	tokenizer.add_tokens(SCREAMING_SNAKE_CASE_						)
	__snake_case    :							Any   	=				tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_						)
	__snake_case    :							List[Any]   	=				OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name						)
	model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_						)						)
	model.to(SCREAMING_SNAKE_CASE_						)

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

	logger.info('Encoding dataset...'						)
	__snake_case    :							Tuple   	=				load_rocstories_dataset(args.train_dataset						)
	__snake_case    :							List[str]   	=				load_rocstories_dataset(args.eval_dataset						)
	__snake_case    :							Tuple   	=				(train_dataset, eval_dataset)
	__snake_case    :							Any   	=				tokenize_and_encode(SCREAMING_SNAKE_CASE_						)

	# Compute the max input length for the Transformer
	__snake_case    :							int   	=				model.config.n_positions // 2 - 2
	__snake_case    :							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						)
	__snake_case    :							int   	=				min(SCREAMING_SNAKE_CASE_    ,							model.config.n_positions						)  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	__snake_case    :							Dict   	=				pre_process_datasets(SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_    ,							*SCREAMING_SNAKE_CASE_						)
	__snake_case    :							Tuple   	=				tensor_datasets[0], tensor_datasets[1]

	__snake_case    :							str   	=				TensorDataset(*SCREAMING_SNAKE_CASE_						)
	__snake_case    :							Tuple   	=				RandomSampler(SCREAMING_SNAKE_CASE_						)
	__snake_case    :							Union[str, Any]   	=				DataLoader(SCREAMING_SNAKE_CASE_    ,							sampler=SCREAMING_SNAKE_CASE_    ,							batch_size=args.train_batch_size						)

	__snake_case    :							int   	=				TensorDataset(*SCREAMING_SNAKE_CASE_						)
	__snake_case    :							List[str]   	=				SequentialSampler(SCREAMING_SNAKE_CASE_						)
	__snake_case    :							Optional[int]   	=				DataLoader(SCREAMING_SNAKE_CASE_    ,							sampler=SCREAMING_SNAKE_CASE_    ,							batch_size=args.eval_batch_size						)

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

		__snake_case    :							Optional[Any]   	=				list(model.named_parameters()						)
		__snake_case    :							int   	=				['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		__snake_case    :							int   	=				[
		    {
		        '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},
		]
		__snake_case    :							Union[str, Any]   	=				AdamW(SCREAMING_SNAKE_CASE_    ,							lr=args.learning_rate    ,							eps=args.adam_epsilon						)
		__snake_case    :							Optional[int]   	=				get_linear_schedule_with_warmup(
		    SCREAMING_SNAKE_CASE_    ,							num_warmup_steps=args.warmup_steps    ,							num_training_steps=SCREAMING_SNAKE_CASE_						)

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

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		__snake_case    :							Any   	=				model.module if hasattr(SCREAMING_SNAKE_CASE_    ,							'module'						) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		__snake_case    :							str   	=				os.path.join(args.output_dir    ,							SCREAMING_SNAKE_CASE_						)
		__snake_case    :							str   	=				os.path.join(args.output_dir    ,							SCREAMING_SNAKE_CASE_						)

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

		# Load a trained model and vocabulary that you have fine-tuned
		__snake_case    :							Optional[Any]   	=				OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir						)
		__snake_case    :							int   	=				OpenAIGPTTokenizer.from_pretrained(args.output_dir						)
		model.to(SCREAMING_SNAKE_CASE_						)

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

			__snake_case    :							int   	=				mc_logits.detach().cpu().numpy()
			__snake_case    :							Optional[Any]   	=				mc_labels.to('cpu'						).numpy()
			__snake_case    :							Dict   	=				accuracy(SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_						)

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

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

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

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


if __name__ == "__main__":
	main()






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a  :  Optional[Any]=				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Optional[Any]=				[
	    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Wav2Vec2ForAudioFrameClassification",
	    "Wav2Vec2ForCTC",
	    "Wav2Vec2ForMaskedLM",
	    "Wav2Vec2ForPreTraining",
	    "Wav2Vec2ForSequenceClassification",
	    "Wav2Vec2ForXVector",
	    "Wav2Vec2Model",
	    "Wav2Vec2PreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[str]=				[
	    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFWav2Vec2ForCTC",
	    "TFWav2Vec2Model",
	    "TFWav2Vec2PreTrainedModel",
	    "TFWav2Vec2ForSequenceClassification",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[Any]=				[
	    "FlaxWav2Vec2ForCTC",
	    "FlaxWav2Vec2ForPreTraining",
	    "FlaxWav2Vec2Model",
	    "FlaxWav2Vec2PreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
	from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
	from .processing_wavaveca import WavaVecaProcessor
	from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca import (
		    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaForAudioFrameClassification,
		    WavaVecaForCTC,
		    WavaVecaForMaskedLM,
		    WavaVecaForPreTraining,
		    WavaVecaForSequenceClassification,
		    WavaVecaForXVector,
		    WavaVecaModel,
		    WavaVecaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWavaVecaForCTC,
		    TFWavaVecaForSequenceClassification,
		    TFWavaVecaModel,
		    TFWavaVecaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    FlaxWavaVecaForCTC,
		    FlaxWavaVecaForPreTraining,
		    FlaxWavaVecaModel,
		    FlaxWavaVecaPreTrainedModel,
		)


else:
	import sys

	_a  :  Tuple=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





