




'''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
)
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			logging.getLogger(__name__)


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	str					):

       '''simple docstring'''






       snake_case_      =				np.argmax(snake_case							,    axis=1					)
       return np.sum(outputs == labels					)


def 					UpperCamelCase_(     snake_case							:	int					):

       '''simple docstring'''






       with open(snake_case							,    encoding="utf_8"					) as f:
              snake_case_      =				csv.reader(snake_case					)
              snake_case_      =				[]
              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 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	List[Any]							,    snake_case							:	Union[str, Any]							,    snake_case							:	List[Any]							,    snake_case							:	Tuple							,    snake_case							:	Optional[int]					):

       '''simple docstring'''






       snake_case_      =				[]
       for dataset in encoded_datasets:
              snake_case_      =				len(snake_case					)
              snake_case_      =				np.zeros((n_batch, 2, input_len)							,    dtype=np.intaa					)
              snake_case_      =				np.zeros((n_batch, 2)							,    dtype=np.intaa					)
              snake_case_      =				np.full((n_batch, 2, input_len)							,    fill_value=-1_0_0							,    dtype=np.intaa					)
              snake_case_      =				np.zeros((n_batch,)							,    dtype=np.intaa					)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(snake_case					):
                     snake_case_      =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     snake_case_      =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     snake_case_      =				with_conta
                     snake_case_      =				with_conta
                     snake_case_      =				len(snake_case					) - 1
                     snake_case_      =				len(snake_case					) - 1
                     snake_case_      =				with_conta
                     snake_case_      =				with_conta
                     snake_case_      =				mc_label
              snake_case_      =				(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 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				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=4_2					)
       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=1_6					)
       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=0.01					)
       parser.add_argument("--lm_coef"							,    type=snake_case							,    default=0.9					)
       parser.add_argument("--n_valid"							,    type=snake_case							,    default=3_7_4					)

       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."					)
       snake_case_      =				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					)

       snake_case_      =				torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
       snake_case_      =				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
       snake_case_      =				["_start_", "_delimiter_", "_classify_"]
       snake_case_      =				OpenAIGPTTokenizer.from_pretrained(args.model_name					)
       tokenizer.add_tokens(snake_case					)
       snake_case_      =				tokenizer.convert_tokens_to_ids(snake_case					)
       snake_case_      =				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							:	Optional[Any]					):
              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..."					)
       snake_case_      =				load_rocstories_dataset(args.train_dataset					)
       snake_case_      =				load_rocstories_dataset(args.eval_dataset					)
       snake_case_      =				(train_dataset, eval_dataset)
       snake_case_      =				tokenize_and_encode(snake_case					)

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

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

       snake_case_      =				TensorDataset(*snake_case					)
       snake_case_      =				RandomSampler(snake_case					)
       snake_case_      =				DataLoader(snake_case							,    sampler=snake_case							,    batch_size=args.train_batch_size					)

       snake_case_      =				TensorDataset(*snake_case					)
       snake_case_      =				SequentialSampler(snake_case					)
       snake_case_      =				DataLoader(snake_case							,    sampler=snake_case							,    batch_size=args.eval_batch_size					)

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

              snake_case_      =				list(model.named_parameters()					)
              snake_case_      =				["bias", "LayerNorm.bias", "LayerNorm.weight"]
              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},
              ]
              snake_case_      =				AdamW(snake_case							,    lr=args.learning_rate							,    eps=args.adam_epsilon					)
              snake_case_      =				get_linear_schedule_with_warmup(
                  snake_case							,    num_warmup_steps=args.warmup_steps							,    num_training_steps=snake_case					)

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

       if args.do_eval:
              model.eval()
              snake_case_ ,				snake_case_      =				0, 0
              snake_case_ ,				snake_case_      =				0, 0
              for batch in tqdm(snake_case							,    desc="Evaluating"					):
                     snake_case_      =				tuple(t.to(snake_case					) for t in batch					)
                     snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				batch
                     with torch.no_grad():
                            snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				model(
                                snake_case							,    mc_token_ids=snake_case							,    lm_labels=snake_case							,    mc_labels=snake_case					)

                     snake_case_      =				mc_logits.detach().cpu().numpy()
                     snake_case_      =				mc_labels.to("cpu"					).numpy()
                     snake_case_      =				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

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

              snake_case_      =				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()










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




