

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		:Dict						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						np.argmax(SCREAMING_SNAKE_CASE_			,						axis=1			)
    return np.sum(outputs == labels			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    with open(SCREAMING_SNAKE_CASE_			,						encoding="utf_8"			) as f:
        UpperCamelCase_			  =						csv.reader(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						[]
        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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Dict:



    """simple docstring"""






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



    """simple docstring"""






    UpperCamelCase_			  =						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=4_2			)
    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=1_6			)
    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=3_7_4			)

    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_			  =						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_			  =						torch.device("cuda" if torch.cuda.is_available() else "cpu"			)
    UpperCamelCase_			  =						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_			  =						["_start_", "_delimiter_", "_classify_"]
    UpperCamelCase_			  =						OpenAIGPTTokenizer.from_pretrained(args.model_name			)
    tokenizer.add_tokens(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						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_			  =						load_rocstories_dataset(args.train_dataset			)
    UpperCamelCase_			  =						load_rocstories_dataset(args.eval_dataset			)
    UpperCamelCase_			  =						(train_dataset, eval_dataset)
    UpperCamelCase_			  =						tokenize_and_encode(SCREAMING_SNAKE_CASE_			)

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

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

    UpperCamelCase_			  =						TensorDataset(*SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						RandomSampler(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						sampler=SCREAMING_SNAKE_CASE_			,						batch_size=args.train_batch_size			)

    UpperCamelCase_			  =						TensorDataset(*SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						SequentialSampler(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						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_			  =						args.max_steps
            UpperCamelCase_			  =						args.max_steps // (len(SCREAMING_SNAKE_CASE_			) // args.gradient_accumulation_steps) + 1
        else:
            UpperCamelCase_			  =						len(SCREAMING_SNAKE_CASE_			) // args.gradient_accumulation_steps * args.num_train_epochs

        UpperCamelCase_			  =						list(model.named_parameters()			)
        UpperCamelCase_			  =						["bias", "LayerNorm.bias", "LayerNorm.weight"]
        UpperCamelCase_			  =						[
            {
                "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_			  =						AdamW(SCREAMING_SNAKE_CASE_			,						lr=args.learning_rate			,						eps=args.adam_epsilon			)
        UpperCamelCase_			  =						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_			  =						0, 0, None
        model.train()
        for _ in trange(int(args.num_train_epochs			)			,						desc="Epoch"			):
            UpperCamelCase_			  =						0
            UpperCamelCase_			  =						0
            UpperCamelCase_			  =						tqdm(SCREAMING_SNAKE_CASE_			,						desc="Training"			)
            for step, batch in enumerate(SCREAMING_SNAKE_CASE_			):
                UpperCamelCase_			  =						tuple(t.to(SCREAMING_SNAKE_CASE_			) for t in batch			)
                UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						batch
                UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			,						mc_token_ids=SCREAMING_SNAKE_CASE_			,						lm_labels=SCREAMING_SNAKE_CASE_			,						mc_labels=SCREAMING_SNAKE_CASE_			)
                UpperCamelCase_			  =						args.lm_coef * losses[0] + losses[1]
                loss.backward()
                optimizer.step()
                scheduler.step()
                optimizer.zero_grad()
                tr_loss += loss.item()
                UpperCamelCase_			  =						(
                    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                )
                nb_tr_steps += 1
                UpperCamelCase_			  =						"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_			  =						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_			  =						os.path.join(args.output_dir			,						SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						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_			  =						OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
        UpperCamelCase_			  =						OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
        model.to(SCREAMING_SNAKE_CASE_			)

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

            UpperCamelCase_			  =						mc_logits.detach().cpu().numpy()
            UpperCamelCase_			  =						mc_labels.to("cpu"			).numpy()
            UpperCamelCase_			  =						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_			  =						eval_loss / nb_eval_steps
        UpperCamelCase_			  =						eval_accuracy / nb_eval_examples
        UpperCamelCase_			  =						tr_loss / nb_tr_steps if args.do_train else None
        UpperCamelCase_			  =						{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

        UpperCamelCase_			  =						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()



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
