





"""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
)
__magic_name__       :					Tuple						=			logging.getLogger(__name__)

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     encoding="""utf_8"""     ) as f:
												UpperCamelCase				:		Union[str, Any]				   =			csv.reader(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Optional[int]				   =			[]
												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							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			[]
						for dataset in encoded_datasets:
												UpperCamelCase				:		str				   =			len(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Optional[Any]				   =			np.zeros((n_batch, 2, input_len)							,     dtype=np.intaa     )
												UpperCamelCase				:		Tuple				   =			np.zeros((n_batch, 2)							,     dtype=np.intaa     )
												UpperCamelCase				:		Optional[Any]				   =			np.full((n_batch, 2, input_len)							,     fill_value=-100							,     dtype=np.intaa     )
												UpperCamelCase				:		int				   =			np.zeros((n_batch,)							,     dtype=np.intaa     )
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(SCREAMING_SNAKE_CASE     ):
																		UpperCamelCase				:		Optional[Any]				   =			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		UpperCamelCase				:		str				   =			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		UpperCamelCase				:		Tuple				   =			with_conta
																		UpperCamelCase				:		List[str]				   =			with_conta
																		UpperCamelCase				:		str				   =			len(SCREAMING_SNAKE_CASE     ) - 1
																		UpperCamelCase				:		Any				   =			len(SCREAMING_SNAKE_CASE     ) - 1
																		UpperCamelCase				:		List[Any]				   =			with_conta
																		UpperCamelCase				:		List[str]				   =			with_conta
																		UpperCamelCase				:		Union[str, Any]				   =			mc_label
												UpperCamelCase				:		Any				   =			(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							():
						UpperCamelCase				:		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=374     )

						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."""     )
						UpperCamelCase				:		Any				   =			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     )

						UpperCamelCase				:		List[Any]				   =			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )
						UpperCamelCase				:		Any				   =			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
						UpperCamelCase				:		List[str]				   =			["""_start_""", """_delimiter_""", """_classify_"""]
						UpperCamelCase				:		Dict				   =			OpenAIGPTTokenizer.from_pretrained(args.model_name     )
						tokenizer.add_tokens(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[int]				   =			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, 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(SCREAMING_SNAKE_CASE     ):
												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..."""     )
						UpperCamelCase				:		str				   =			load_rocstories_dataset(args.train_dataset     )
						UpperCamelCase				:		Any				   =			load_rocstories_dataset(args.eval_dataset     )
						UpperCamelCase				:		Union[str, Any]				   =			(train_dataset, eval_dataset)
						UpperCamelCase				:		str				   =			tokenize_and_encode(SCREAMING_SNAKE_CASE     )

						# Compute the max input length for the Transformer
						UpperCamelCase				:		Dict				   =			model.config.n_positions // 2 - 2
						UpperCamelCase				:		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     )
						UpperCamelCase				:		Union[str, Any]				   =			min(SCREAMING_SNAKE_CASE							,     model.config.n_positions     )  # Max size of input for the pre-trained model

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

						UpperCamelCase				:		List[Any]				   =			TensorDataset(*SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		int				   =			RandomSampler(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[Any]				   =			DataLoader(SCREAMING_SNAKE_CASE							,     sampler=SCREAMING_SNAKE_CASE							,     batch_size=args.train_batch_size     )

						UpperCamelCase				:		str				   =			TensorDataset(*SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[str]				   =			SequentialSampler(SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Dict				   =			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:
																		UpperCamelCase				:		Optional[int]				   =			args.max_steps
																		UpperCamelCase				:		Dict				   =			args.max_steps // (len(SCREAMING_SNAKE_CASE     ) // args.gradient_accumulation_steps) + 1
												else:
																		UpperCamelCase				:		Union[str, Any]				   =			len(SCREAMING_SNAKE_CASE     ) // args.gradient_accumulation_steps * args.num_train_epochs

												UpperCamelCase				:		Optional[int]				   =			list(model.named_parameters()     )
												UpperCamelCase				:		Dict				   =			["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
												UpperCamelCase				:		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},
												]
												UpperCamelCase				:		Dict				   =			AdamW(SCREAMING_SNAKE_CASE							,     lr=args.learning_rate							,     eps=args.adam_epsilon     )
												UpperCamelCase				:		Dict				   =			get_linear_schedule_with_warmup(
												    SCREAMING_SNAKE_CASE							,     num_warmup_steps=args.warmup_steps							,     num_training_steps=SCREAMING_SNAKE_CASE     )

						if args.do_train:
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			0, 0, None
												model.train()
												for _ in trange(int(args.num_train_epochs     )							,     desc="""Epoch"""     ):
																		UpperCamelCase				:		Optional[int]				   =			0
																		UpperCamelCase				:		Union[str, Any]				   =			0
																		UpperCamelCase				:		Dict				   =			tqdm(SCREAMING_SNAKE_CASE							,     desc="""Training"""     )
																		for step, batch in enumerate(SCREAMING_SNAKE_CASE     ):
																								UpperCamelCase				:		Tuple				   =			tuple(t.to(SCREAMING_SNAKE_CASE     ) for t in batch     )
																								UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		int				   =			batch
																								UpperCamelCase				:		List[Any]				   =			model(SCREAMING_SNAKE_CASE							,     mc_token_ids=SCREAMING_SNAKE_CASE							,     lm_labels=SCREAMING_SNAKE_CASE							,     mc_labels=SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		int				   =			args.lm_coef * losses[0] + losses[1]
																								loss.backward()
																								optimizer.step()
																								scheduler.step()
																								optimizer.zero_grad()
																								tr_loss += loss.item()
																								UpperCamelCase				:		Tuple				   =			(
																								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																								)
																								nb_tr_steps += 1
																								UpperCamelCase				:		Dict				   =			"""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
												UpperCamelCase				:		int				   =			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`
												UpperCamelCase				:		Optional[int]				   =			os.path.join(args.output_dir							,     SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Tuple				   =			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
												UpperCamelCase				:		List[Any]				   =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
												UpperCamelCase				:		Optional[Any]				   =			OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
												model.to(SCREAMING_SNAKE_CASE     )

						if args.do_eval:
												model.eval()
												UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0
												UpperCamelCase   ,						UpperCamelCase				:		int				   =			0, 0
												for batch in tqdm(SCREAMING_SNAKE_CASE							,     desc="""Evaluating"""     ):
																		UpperCamelCase				:		Optional[Any]				   =			tuple(t.to(SCREAMING_SNAKE_CASE     ) for t in batch     )
																		UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Union[str, Any]				   =			batch
																		with torch.no_grad():
																								UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		int				   =			model(
																								    SCREAMING_SNAKE_CASE							,     mc_token_ids=SCREAMING_SNAKE_CASE							,     lm_labels=SCREAMING_SNAKE_CASE							,     mc_labels=SCREAMING_SNAKE_CASE     )

																		UpperCamelCase				:		Tuple				   =			mc_logits.detach().cpu().numpy()
																		UpperCamelCase				:		List[Any]				   =			mc_labels.to("""cpu"""     ).numpy()
																		UpperCamelCase				:		List[str]				   =			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

												UpperCamelCase				:		int				   =			eval_loss / nb_eval_steps
												UpperCamelCase				:		str				   =			eval_accuracy / nb_eval_examples
												UpperCamelCase				:		Tuple				   =			tr_loss / nb_tr_steps if args.do_train else None
												UpperCamelCase				:		Tuple				   =			{"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

												UpperCamelCase				:		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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






