


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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_vision_available():
 import PIL





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Union[str, Any]       					=			['pixel_values']
 def __init__(       self    ,			lowerCAmelCase__ = True    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = PILImageResampling.BICUBIC    ,			lowerCAmelCase__ = True    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = True    ,			lowerCAmelCase__ = 1 / 255    ,			lowerCAmelCase__ = True    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = True    ,			**lowerCAmelCase__    ,			) ->    None:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__			)
        lowercase__:       List[str]        =       size if size is not None else {'shortest_edge': 224}
        lowercase__:       Dict        =       get_size_dict(lowerCAmelCase__    ,			default_to_square=lowerCAmelCase__			)
        lowercase__:       int        =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase__:       Union[str, Any]        =       get_size_dict(lowerCAmelCase__    ,			default_to_square=lowerCAmelCase__    ,			param_name='crop_size'			)

        lowercase__:       Optional[Any]        =       do_resize
        lowercase__:       List[Any]        =       size
        lowercase__:       int        =       resample
        lowercase__:       List[str]        =       do_center_crop
        lowercase__:       str        =       crop_size
        lowercase__:       Dict        =       do_rescale
        lowercase__:       int        =       rescale_factor
        lowercase__:       Optional[Any]        =       do_normalize
        lowercase__:       Dict        =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__:       str        =       image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__:       Union[str, Any]        =       do_convert_rgb
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = PILImageResampling.BICUBIC    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    np.ndarray:




        '''simple docstring'''
        lowercase__:       Dict        =       get_size_dict(lowerCAmelCase__    ,			default_to_square=lowerCAmelCase__			)
        if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'			)
        lowercase__:       Dict        =       get_resize_output_image_size(lowerCAmelCase__    ,			size=size['shortest_edge']    ,			default_to_square=lowerCAmelCase__			)
        return resize(lowerCAmelCase__    ,			size=lowerCAmelCase__    ,			resample=lowerCAmelCase__    ,			data_format=lowerCAmelCase__    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    np.ndarray:




        '''simple docstring'''
        lowercase__:       Any        =       get_size_dict(lowerCAmelCase__			)
        if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'			)
        return center_crop(lowerCAmelCase__    ,			size=(size['height'], size['width'])    ,			data_format=lowerCAmelCase__    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    List[str]:




        '''simple docstring'''
        return rescale(lowerCAmelCase__    ,			scale=lowerCAmelCase__    ,			data_format=lowerCAmelCase__    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			**lowerCAmelCase__    ,			) ->    np.ndarray:




        '''simple docstring'''
        return normalize(lowerCAmelCase__    ,			mean=lowerCAmelCase__    ,			std=lowerCAmelCase__    ,			data_format=lowerCAmelCase__    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = ChannelDimension.FIRST    ,			**lowerCAmelCase__    ,			) ->    PIL.Image.Image:




        '''simple docstring'''
        lowercase__:       Any        =       do_resize if do_resize is not None else self.do_resize
        lowercase__:       int        =       size if size is not None else self.size
        lowercase__:       str        =       get_size_dict(lowerCAmelCase__    ,			param_name='size'    ,			default_to_square=lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       resample if resample is not None else self.resample
        lowercase__:       Union[str, Any]        =       do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase__:       Dict        =       crop_size if crop_size is not None else self.crop_size
        lowercase__:       Optional[Any]        =       get_size_dict(lowerCAmelCase__    ,			param_name='crop_size'    ,			default_to_square=lowerCAmelCase__			)
        lowercase__:       Dict        =       do_rescale if do_rescale is not None else self.do_rescale
        lowercase__:       List[Any]        =       rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__:       Optional[int]        =       do_normalize if do_normalize is not None else self.do_normalize
        lowercase__:       List[str]        =       image_mean if image_mean is not None else self.image_mean
        lowercase__:       List[Any]        =       image_std if image_std is not None else self.image_std
        lowercase__:       List[Any]        =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__:       Tuple        =       make_list_of_images(lowerCAmelCase__			)

        if not valid_images(lowerCAmelCase__			):
               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:
               raise ValueError('Size must be specified if do_resize is True.'			)

        if do_center_crop and crop_size is None:
               raise ValueError('Crop size must be specified if do_center_crop is True.'			)

        if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
               lowercase__:       int        =       [convert_to_rgb(lowerCAmelCase__			) for image in images]

        # All transformations expect numpy arrays.
        lowercase__:       List[str]        =       [to_numpy_array(lowerCAmelCase__			) for image in images]

        if do_resize:
               lowercase__:       Union[str, Any]        =       [self.resize(image=lowerCAmelCase__    ,			size=lowerCAmelCase__    ,			resample=lowerCAmelCase__			) for image in images]

        if do_center_crop:
               lowercase__:       Union[str, Any]        =       [self.center_crop(image=lowerCAmelCase__    ,			size=lowerCAmelCase__			) for image in images]

        if do_rescale:
               lowercase__:       Tuple        =       [self.rescale(image=lowerCAmelCase__    ,			scale=lowerCAmelCase__			) for image in images]

        if do_normalize:
               lowercase__:       str        =       [self.normalize(image=lowerCAmelCase__    ,			mean=lowerCAmelCase__    ,			std=lowerCAmelCase__			) for image in images]

        lowercase__:       str        =       [to_channel_dimension_format(lowerCAmelCase__    ,			lowerCAmelCase__			) for image in images]

        lowercase__:       Any        =       {'pixel_values': images}
        return BatchFeature(data=lowerCAmelCase__    ,			tensor_type=lowerCAmelCase__			)






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
)
__lowerCAmelCase			=							logging.getLogger(__name__)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Optional[int]:
       lowercase__:       Optional[int]        =       np.argmax(snake_case      ,							axis=1					)
       return np.sum(outputs == labels					)




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




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




def        snake_case_					(  )  ->    Union[str, Any]:
       lowercase__:       Optional[Any]        =       argparse.ArgumentParser()
       parser.add_argument('--model_name'      ,							type=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=snake_case      ,							type=snake_case      ,							required=snake_case      ,							help='The output directory where the model predictions and checkpoints will be written.'      ,							)
       parser.add_argument('--train_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--eval_dataset'      ,							type=snake_case      ,							default=''					)
       parser.add_argument('--seed'      ,							type=snake_case      ,							default=42					)
       parser.add_argument('--num_train_epochs'      ,							type=snake_case      ,							default=3					)
       parser.add_argument('--train_batch_size'      ,							type=snake_case      ,							default=8					)
       parser.add_argument('--eval_batch_size'      ,							type=snake_case      ,							default=16					)
       parser.add_argument('--adam_epsilon'      ,							default=1e-8      ,							type=snake_case      ,							help='Epsilon for Adam optimizer.'					)
       parser.add_argument('--max_grad_norm'      ,							type=snake_case      ,							default=1					)
       parser.add_argument(
           '--max_steps'      ,							default=-1      ,							type=snake_case      ,							help=(
               'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
           )      ,							)
       parser.add_argument(
           '--gradient_accumulation_steps'      ,							type=snake_case      ,							default=1      ,							help='Number of updates steps to accumulate before                        performing a backward/update pass.'      ,							)
       parser.add_argument('--learning_rate'      ,							type=snake_case      ,							default=6.25e-5					)
       parser.add_argument('--warmup_steps'      ,							default=0      ,							type=snake_case      ,							help='Linear warmup over warmup_steps.'					)
       parser.add_argument('--lr_schedule'      ,							type=snake_case      ,							default='warmup_linear'					)
       parser.add_argument('--weight_decay'      ,							type=snake_case      ,							default=0.0_1					)
       parser.add_argument('--lm_coef'      ,							type=snake_case      ,							default=0.9					)
       parser.add_argument('--n_valid'      ,							type=snake_case      ,							default=3_74					)

       parser.add_argument('--server_ip'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       parser.add_argument('--server_port'      ,							type=snake_case      ,							default=''      ,							help='Can be used for distant debugging.'					)
       lowercase__:       List[str]        =       parser.parse_args()
       print(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=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					)

       lowercase__:       Any        =       torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)
       lowercase__:       Tuple        =       torch.cuda.device_count()
       logger.info('device: {}, n_gpu {}'.format(snake_case      ,							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
       lowercase__:       Any        =       ['_start_', '_delimiter_', '_classify_']
       lowercase__:       Any        =       OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       lowercase__:       int        =       tokenizer.convert_tokens_to_ids(snake_case					)
       lowercase__:       int        =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
       model.resize_token_embeddings(len(snake_case					)					)
       model.to(snake_case					)

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

       logger.info('Encoding dataset...'					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.train_dataset					)
       lowercase__:       Dict        =       load_rocstories_dataset(args.eval_dataset					)
       lowercase__:       str        =       (train_dataset, eval_dataset)
       lowercase__:       Any        =       tokenize_and_encode(snake_case					)

       # Compute the max input length for the Transformer
       lowercase__:       Optional[Any]        =       model.config.n_positions // 2 - 2
       lowercase__:       Optional[int]        =       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(snake_case      ,							model.config.n_positions					)  # Max size of input for the pre-trained model

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

       lowercase__:       List[str]        =       TensorDataset(*snake_case					)
       lowercase__:       Dict        =       RandomSampler(snake_case					)
       lowercase__:       Optional[int]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.train_batch_size					)

       lowercase__:       str        =       TensorDataset(*snake_case					)
       lowercase__:       str        =       SequentialSampler(snake_case					)
       lowercase__:       Optional[Any]        =       DataLoader(snake_case      ,							sampler=snake_case      ,							batch_size=args.eval_batch_size					)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowercase__:       Union[str, Any]        =       args.max_steps
                     lowercase__:       Tuple        =       args.max_steps // (len(snake_case					) // args.gradient_accumulation_steps) + 1
              else:
                     lowercase__:       Optional[Any]        =       len(snake_case					) // args.gradient_accumulation_steps * args.num_train_epochs

              lowercase__:       str        =       list(model.named_parameters()					)
              lowercase__:       Any        =       ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
              lowercase__:       str        =       [
                  {
                      '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(snake_case      ,							lr=args.learning_rate      ,							eps=args.adam_epsilon					)
              lowercase__:       Tuple        =       get_linear_schedule_with_warmup(
                  snake_case      ,							num_warmup_steps=args.warmup_steps      ,							num_training_steps=snake_case					)

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

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

              # If we save using the predefined names, we can load using `from_pretrained`
              lowercase__:       Tuple        =       os.path.join(args.output_dir      ,							snake_case					)
              lowercase__:       List[str]        =       os.path.join(args.output_dir      ,							snake_case					)

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

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

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

                     lowercase__:       Dict        =       mc_logits.detach().cpu().numpy()
                     lowercase__:       Tuple        =       mc_labels.to('cpu'					).numpy()
                     lowercase__:       Dict        =       accuracy(snake_case      ,							snake_case					)

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

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

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

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


if __name__ == "__main__":
 main()


