




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     =			logging.getLogger(__name__)





def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  np.argmax(_A ,  axis=1  )
						return np.sum(outputs == labels  )





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





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





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

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

						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=_A  )
												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  )

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

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

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

						logger.info('''Encoding dataset...'''  )
						lowerCAmelCase_      			=  load_rocstories_dataset(args.train_dataset  )
						lowerCAmelCase_      			=  load_rocstories_dataset(args.eval_dataset  )
						lowerCAmelCase_      			=  (train_dataset, eval_dataset)
						lowerCAmelCase_      			=  tokenize_and_encode(_A  )

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

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

						lowerCAmelCase_      			=  TensorDataset(*_A  )
						lowerCAmelCase_      			=  RandomSampler(_A  )
						lowerCAmelCase_      			=  DataLoader(_A ,  sampler=_A ,  batch_size=args.train_batch_size  )

						lowerCAmelCase_      			=  TensorDataset(*_A  )
						lowerCAmelCase_      			=  SequentialSampler(_A  )
						lowerCAmelCase_      			=  DataLoader(_A ,  sampler=_A ,  batch_size=args.eval_batch_size  )

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

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

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

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

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

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

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

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

																		lowerCAmelCase_      			=  mc_logits.detach().cpu().numpy()
																		lowerCAmelCase_      			=  mc_labels.to('''cpu'''  ).numpy()
																		lowerCAmelCase_      			=  accuracy(_A ,  _A  )

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

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

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

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


if __name__ == "__main__":
						main()










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  A		(  unittest.TestCase     ):



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  UNetaDModel(
								    block_out_channels=(32, 64),	layers_per_block=2,	sample_size=32,	in_channels=3,	out_channels=3,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),	)
								return model







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_uncond_unet
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=2,	generator=UpperCamelCase__,	output_type='''numpy''',	return_dict=UpperCamelCase__   )[0]

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								lowerCAmelCase_      			=  image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCAmelCase_      			=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2







@slow
@require_torch
class  A		(  unittest.TestCase     ):



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''google/ncsnpp-celebahq-256'''
								lowerCAmelCase_      			=  UNetaDModel.from_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  KarrasVeScheduler()

								lowerCAmelCase_      			=  KarrasVePipeline(unet=UpperCamelCase__,	scheduler=UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.manual_seed(0   )
								lowerCAmelCase_      			=  pipe(num_inference_steps=20,	generator=UpperCamelCase__,	output_type='''numpy'''   ).images

								lowerCAmelCase_      			=  image[0, -3:, -3:, -1]
								assert image.shape == (1, 256, 256, 3)
								lowerCAmelCase_      			=  np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




