






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_		:						Optional[Any]             =    logging.getLogger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= np.argmax(_UpperCAmelCase							,       axis=1)
			return np.sum(outputs == labels)






def 				lowerCamelCase__				(_UpperCAmelCase):
			with open(_UpperCAmelCase							,       encoding='utf_8') as f:
						SCREAMING_SNAKE_CASE						= csv.reader(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= []
						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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch, 2, input_len)							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch, 2)							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.full((n_batch, 2, input_len)							,       fill_value=-100							,       dtype=np.intaa)
						SCREAMING_SNAKE_CASE						= np.zeros((n_batch,)							,       dtype=np.intaa)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(_UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE						= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase) - 1
									SCREAMING_SNAKE_CASE						= len(_UpperCAmelCase) - 1
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= with_conta
									SCREAMING_SNAKE_CASE						= mc_label
						SCREAMING_SNAKE_CASE						= (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 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 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.')
			SCREAMING_SNAKE_CASE						= 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)

			SCREAMING_SNAKE_CASE						= torch.device('cuda' if torch.cuda.is_available() else 'cpu')
			SCREAMING_SNAKE_CASE						= 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
			SCREAMING_SNAKE_CASE						= ['_start_', '_delimiter_', '_classify_']
			SCREAMING_SNAKE_CASE						= OpenAIGPTTokenizer.from_pretrained(args.model_name)
			tokenizer.add_tokens(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= 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):
						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...')
			SCREAMING_SNAKE_CASE						= load_rocstories_dataset(args.train_dataset)
			SCREAMING_SNAKE_CASE						= load_rocstories_dataset(args.eval_dataset)
			SCREAMING_SNAKE_CASE						= (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE						= tokenize_and_encode(_UpperCAmelCase)

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

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

			SCREAMING_SNAKE_CASE						= TensorDataset(*_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= RandomSampler(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= DataLoader(_UpperCAmelCase							,       sampler=_UpperCAmelCase							,       batch_size=args.train_batch_size)

			SCREAMING_SNAKE_CASE						= TensorDataset(*_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= SequentialSampler(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= DataLoader(_UpperCAmelCase							,       sampler=_UpperCAmelCase							,       batch_size=args.eval_batch_size)

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

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

			if args.do_train:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs)							,       desc='Epoch'):
									SCREAMING_SNAKE_CASE						= 0
									SCREAMING_SNAKE_CASE						= 0
									SCREAMING_SNAKE_CASE						= tqdm(_UpperCAmelCase							,       desc='Training')
									for step, batch in enumerate(_UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= tuple(t.to(_UpperCAmelCase) for t in batch)
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= batch
												SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase							,       mc_token_ids=_UpperCAmelCase							,       lm_labels=_UpperCAmelCase							,       mc_labels=_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE						= (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE						= '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
						SCREAMING_SNAKE_CASE						= 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`
						SCREAMING_SNAKE_CASE						= os.path.join(args.output_dir							,       _UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= 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
						SCREAMING_SNAKE_CASE						= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
						SCREAMING_SNAKE_CASE						= OpenAIGPTTokenizer.from_pretrained(args.output_dir)
						model.to(_UpperCAmelCase)

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

									SCREAMING_SNAKE_CASE						= mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE						= mc_labels.to('cpu').numpy()
									SCREAMING_SNAKE_CASE						= 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

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

						SCREAMING_SNAKE_CASE						= 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()












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


a_		:						Optional[int]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= b.T
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=1)
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=0)
			SCREAMING_SNAKE_CASE						= np.matmul(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= aa[:, None] - 2 * ab + ba[None, :]
			return d






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= x.reshape(-1							,       3)
			SCREAMING_SNAKE_CASE						= squared_euclidean_distance(_UpperCAmelCase							,       _UpperCAmelCase)
			return np.argmin(_UpperCAmelCase							,       axis=1)







class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							['''pixel_values''']






			def __init__(       self		,					a = None		,					a = True		,					a = None		,					a = PILImageResampling.BILINEAR		,					a = True		,					a = True		,					**a		,					)      ->       None:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= np.array(a) if clusters is not None else None
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= resample
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = PILImageResampling.BILINEAR		,					a = None		,					**a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
						return resize(
						    a		,					size=(size['height'], size['width'])		,					resample=a		,					data_format=a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= rescale(image=a		,					scale=1 / 1_27.5		,					data_format=a)
						SCREAMING_SNAKE_CASE						= image - 1
						return image







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = ChannelDimension.FIRST		,					**a		,					)      ->       PIL.Image.Image:
						SCREAMING_SNAKE_CASE						= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else self.size
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE						= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						SCREAMING_SNAKE_CASE						= clusters if clusters is not None else self.clusters
						SCREAMING_SNAKE_CASE						= np.array(a)

						SCREAMING_SNAKE_CASE						= make_list_of_images(a)

						if not valid_images(a):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.')

						if do_resize and size is None or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

						if do_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE						= [to_numpy_array(a) for image in images]

						if do_resize:
									SCREAMING_SNAKE_CASE						= [self.resize(image=a		,					size=a		,					resample=a) for image in images]

						if do_normalize:
									SCREAMING_SNAKE_CASE						= [self.normalize(image=a) for image in images]

						if do_color_quantize:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE						= np.array(a)
									SCREAMING_SNAKE_CASE						= color_quantize(a		,					a).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE						= images.shape[0]
									SCREAMING_SNAKE_CASE						= images.reshape(a		,					-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE						= list(a)
						else:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					a) for image in images]

						SCREAMING_SNAKE_CASE						= {'input_ids': images}
						return BatchFeature(data=a		,					tensor_type=a)




