




'''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
)
_snake_case				:				Optional[int]       							=		logging.getLogger(__name__)
def 		snake_case_   (UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    np.argmax(UpperCamelCase			,						axis=1				)
							return np.sum(outputs == labels				)
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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





							'''simple docstring'''


							_a     					=    []
							for dataset in encoded_datasets:
														_a     					=    len(UpperCamelCase				)
														_a     					=    np.zeros((n_batch, 2, input_len)			,						dtype=np.intaa				)
														_a     					=    np.zeros((n_batch, 2)			,						dtype=np.intaa				)
														_a     					=    np.full((n_batch, 2, input_len)			,						fill_value=-100			,						dtype=np.intaa				)
														_a     					=    np.zeros((n_batch,)			,						dtype=np.intaa				)
														for (
														    i,
														    (story, conta, conta, mc_label),
														) in enumerate(UpperCamelCase				):
																					_a     					=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					_a     					=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					_a     					=    with_conta
																					_a     					=    with_conta
																					_a     					=    len(UpperCamelCase				) - 1
																					_a     					=    len(UpperCamelCase				) - 1
																					_a     					=    with_conta
																					_a     					=    with_conta
																					_a     					=    mc_label
														_a     					=    (input_ids, mc_token_ids, lm_labels, mc_labels)
														tensor_datasets.append(tuple(torch.tensor(UpperCamelCase				) for t in all_inputs				)				)
							return tensor_datasets
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    argparse.ArgumentParser()
							parser.add_argument('''--model_name'''			,						type=UpperCamelCase			,						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=UpperCamelCase			,						type=UpperCamelCase			,						required=UpperCamelCase			,						help='''The output directory where the model predictions and checkpoints will be written.'''			,						)
							parser.add_argument('''--train_dataset'''			,						type=UpperCamelCase			,						default=''''''				)
							parser.add_argument('''--eval_dataset'''			,						type=UpperCamelCase			,						default=''''''				)
							parser.add_argument('''--seed'''			,						type=UpperCamelCase			,						default=42				)
							parser.add_argument('''--num_train_epochs'''			,						type=UpperCamelCase			,						default=3				)
							parser.add_argument('''--train_batch_size'''			,						type=UpperCamelCase			,						default=8				)
							parser.add_argument('''--eval_batch_size'''			,						type=UpperCamelCase			,						default=16				)
							parser.add_argument('''--adam_epsilon'''			,						default=1e-8			,						type=UpperCamelCase			,						help='''Epsilon for Adam optimizer.'''				)
							parser.add_argument('''--max_grad_norm'''			,						type=UpperCamelCase			,						default=1				)
							parser.add_argument(
							    '''--max_steps'''			,						default=-1			,						type=UpperCamelCase			,						help=(
							        '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
							    )			,						)
							parser.add_argument(
							    '''--gradient_accumulation_steps'''			,						type=UpperCamelCase			,						default=1			,						help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''			,						)
							parser.add_argument('''--learning_rate'''			,						type=UpperCamelCase			,						default=6.25e-5				)
							parser.add_argument('''--warmup_steps'''			,						default=0			,						type=UpperCamelCase			,						help='''Linear warmup over warmup_steps.'''				)
							parser.add_argument('''--lr_schedule'''			,						type=UpperCamelCase			,						default='''warmup_linear'''				)
							parser.add_argument('''--weight_decay'''			,						type=UpperCamelCase			,						default=0.01				)
							parser.add_argument('''--lm_coef'''			,						type=UpperCamelCase			,						default=0.9				)
							parser.add_argument('''--n_valid'''			,						type=UpperCamelCase			,						default=374				)

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

							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=UpperCamelCase				)
														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				)

							_a     					=    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
							_a     					=    torch.cuda.device_count()
							logger.info('''device: {}, n_gpu {}'''.format(UpperCamelCase			,						UpperCamelCase				)				)

							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
							_a     					=    ['''_start_''', '''_delimiter_''', '''_classify_''']
							_a     					=    OpenAIGPTTokenizer.from_pretrained(args.model_name				)
							tokenizer.add_tokens(UpperCamelCase				)
							_a     					=    tokenizer.convert_tokens_to_ids(UpperCamelCase				)
							_a     					=    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
							model.resize_token_embeddings(len(UpperCamelCase				)				)
							model.to(UpperCamelCase				)

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

							logger.info('''Encoding dataset...'''				)
							_a     					=    load_rocstories_dataset(args.train_dataset				)
							_a     					=    load_rocstories_dataset(args.eval_dataset				)
							_a     					=    (train_dataset, eval_dataset)
							_a     					=    tokenize_and_encode(UpperCamelCase				)

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

							# Prepare inputs tensors and dataloaders
							_a     					=    pre_process_datasets(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						*UpperCamelCase				)
							_a					,							_a     					=    tensor_datasets[0], tensor_datasets[1]

							_a     					=    TensorDataset(*UpperCamelCase				)
							_a     					=    RandomSampler(UpperCamelCase				)
							_a     					=    DataLoader(UpperCamelCase			,						sampler=UpperCamelCase			,						batch_size=args.train_batch_size				)

							_a     					=    TensorDataset(*UpperCamelCase				)
							_a     					=    SequentialSampler(UpperCamelCase				)
							_a     					=    DataLoader(UpperCamelCase			,						sampler=UpperCamelCase			,						batch_size=args.eval_batch_size				)

							# Prepare optimizer
							if args.do_train:
														if args.max_steps > 0:
																					_a     					=    args.max_steps
																					_a     					=    args.max_steps // (len(UpperCamelCase				) // args.gradient_accumulation_steps) + 1
														else:
																					_a     					=    len(UpperCamelCase				) // args.gradient_accumulation_steps * args.num_train_epochs

														_a     					=    list(model.named_parameters()				)
														_a     					=    ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
														_a     					=    [
														    {
														        '''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},
														]
														_a     					=    AdamW(UpperCamelCase			,						lr=args.learning_rate			,						eps=args.adam_epsilon				)
														_a     					=    get_linear_schedule_with_warmup(
														    UpperCamelCase			,						num_warmup_steps=args.warmup_steps			,						num_training_steps=UpperCamelCase				)

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

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

														# If we save using the predefined names, we can load using `from_pretrained`
														_a     					=    os.path.join(args.output_dir			,						UpperCamelCase				)
														_a     					=    os.path.join(args.output_dir			,						UpperCamelCase				)

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

														# Load a trained model and vocabulary that you have fine-tuned
														_a     					=    OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
														_a     					=    OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
														model.to(UpperCamelCase				)

							if args.do_eval:
														model.eval()
														_a					,							_a     					=    0, 0
														_a					,							_a     					=    0, 0
														for batch in tqdm(UpperCamelCase			,						desc='''Evaluating'''				):
																					_a     					=    tuple(t.to(UpperCamelCase				) for t in batch				)
																					_a					,							_a					,							_a					,							_a     					=    batch
																					with torch.no_grad():
																												_a					,							_a					,							_a					,							_a     					=    model(
																												    UpperCamelCase			,						mc_token_ids=UpperCamelCase			,						lm_labels=UpperCamelCase			,						mc_labels=UpperCamelCase				)

																					_a     					=    mc_logits.detach().cpu().numpy()
																					_a     					=    mc_labels.to('''cpu'''				).numpy()
																					_a     					=    accuracy(UpperCamelCase			,						UpperCamelCase				)

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

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

														_a     					=    eval_loss / nb_eval_steps
														_a     					=    eval_accuracy / nb_eval_examples
														_a     					=    tr_loss / nb_tr_steps if args.do_train else None
														_a     					=    {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

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


if __name__ == "__main__":
				main()







'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id] )
																					for i in range(0							,      len(lowerCAmelCase_ )							,      self.seq_length ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    outputs.loss.repeat(args.batch_size				)
														losses.append(accelerator.gather(UpperCamelCase				)				)

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

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

# Load model and tokenizer
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_snake_case							,    _snake_case				:				int       							=		evaluate(args)

logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')

