






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    :   str        =			logging.getLogger(__name__)


def 				snake_case__							(							UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Union[str, Any]       ):
    lowerCAmelCase__   :int      							=				np.argmax(UpperCAmelCase    ,			axis=1       )
    return np.sum(outputs == labels       )


def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    with open(UpperCAmelCase    ,			encoding="utf_8"       ) as f:
        lowerCAmelCase__   :Any      							=				csv.reader(UpperCAmelCase       )
        lowerCAmelCase__   :int      							=				[]
        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 				snake_case__							(							UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  Dict    ,			UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  List[Any]       ):
    lowerCAmelCase__   :Optional[int]      							=				[]
    for dataset in encoded_datasets:
        lowerCAmelCase__   :Any      							=				len(UpperCAmelCase       )
        lowerCAmelCase__   :Union[str, Any]      							=				np.zeros((n_batch, 2, input_len)    ,			dtype=np.intaa       )
        lowerCAmelCase__   :Tuple      							=				np.zeros((n_batch, 2)    ,			dtype=np.intaa       )
        lowerCAmelCase__   :Union[str, Any]      							=				np.full((n_batch, 2, input_len)    ,			fill_value=-1_0_0    ,			dtype=np.intaa       )
        lowerCAmelCase__   :Dict      							=				np.zeros((n_batch,)    ,			dtype=np.intaa       )
        for (
            i,
            (story, conta, conta, mc_label),
        ) in enumerate(UpperCAmelCase       ):
            lowerCAmelCase__   :Union[str, Any]      							=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            lowerCAmelCase__   :Dict      							=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
            lowerCAmelCase__   :Union[str, Any]      							=				with_conta
            lowerCAmelCase__   :Dict      							=				with_conta
            lowerCAmelCase__   :int      							=				len(UpperCAmelCase       ) - 1
            lowerCAmelCase__   :str      							=				len(UpperCAmelCase       ) - 1
            lowerCAmelCase__   :Tuple      							=				with_conta
            lowerCAmelCase__   :int      							=				with_conta
            lowerCAmelCase__   :Union[str, Any]      							=				mc_label
        lowerCAmelCase__   :Optional[int]      							=				(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 				snake_case__							(							):
    lowerCAmelCase__   :List[str]      							=				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=4_2       )
    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=1_6       )
    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=3_7_4       )

    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."       )
    lowerCAmelCase__   :Dict      							=				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       )

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

    # Compute the max input length for the Transformer
    lowerCAmelCase__   :Dict      							=				model.config.n_positions // 2 - 2
    lowerCAmelCase__   :Dict      							=				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       )
    lowerCAmelCase__   :Any      							=				min(UpperCAmelCase    ,			model.config.n_positions       )  # Max size of input for the pre-trained model

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

    lowerCAmelCase__   :Dict      							=				TensorDataset(*UpperCAmelCase       )
    lowerCAmelCase__   :Optional[int]      							=				RandomSampler(UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				DataLoader(UpperCAmelCase    ,			sampler=UpperCAmelCase    ,			batch_size=args.train_batch_size       )

    lowerCAmelCase__   :Tuple      							=				TensorDataset(*UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				SequentialSampler(UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				DataLoader(UpperCAmelCase    ,			sampler=UpperCAmelCase    ,			batch_size=args.eval_batch_size       )

    # Prepare optimizer
    if args.do_train:
        if args.max_steps > 0:
            lowerCAmelCase__   :Dict      							=				args.max_steps
            lowerCAmelCase__   :List[str]      							=				args.max_steps // (len(UpperCAmelCase       ) // args.gradient_accumulation_steps) + 1
        else:
            lowerCAmelCase__   :Optional[Any]      							=				len(UpperCAmelCase       ) // args.gradient_accumulation_steps * args.num_train_epochs

        lowerCAmelCase__   :Dict      							=				list(model.named_parameters()       )
        lowerCAmelCase__   :List[str]      							=				["bias", "LayerNorm.bias", "LayerNorm.weight"]
        lowerCAmelCase__   :Any      							=				[
            {
                "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},
        ]
        lowerCAmelCase__   :List[Any]      							=				AdamW(UpperCAmelCase    ,			lr=args.learning_rate    ,			eps=args.adam_epsilon       )
        lowerCAmelCase__   :Union[str, Any]      							=				get_linear_schedule_with_warmup(
            UpperCAmelCase    ,			num_warmup_steps=args.warmup_steps    ,			num_training_steps=UpperCAmelCase       )

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

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

            lowerCAmelCase__   :str      							=				mc_logits.detach().cpu().numpy()
            lowerCAmelCase__   :Optional[Any]      							=				mc_labels.to("cpu"       ).numpy()
            lowerCAmelCase__   :Optional[int]      							=				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

        lowerCAmelCase__   :Tuple      							=				eval_loss / nb_eval_steps
        lowerCAmelCase__   :List[str]      							=				eval_accuracy / nb_eval_examples
        lowerCAmelCase__   :Tuple      							=				tr_loss / nb_tr_steps if args.do_train else None
        lowerCAmelCase__   :Optional[Any]      							=				{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

        lowerCAmelCase__   :List[str]      							=				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 collections import OrderedDict
from typing import Mapping

from packaging import version

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


_a    :   Tuple        =			logging.get_logger(__name__)

_a    :   Optional[int]        =			{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''levit'''
      def __init__(   self    ,	_lowerCAmelCase=224    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=[128, 256, 384]    ,	_lowerCAmelCase=[4, 8, 12]    ,	_lowerCAmelCase=[4, 4, 4]    ,	_lowerCAmelCase=[16, 16, 16]    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=[2, 2, 2]    ,	_lowerCAmelCase=0.02    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				image_size
          lowerCAmelCase__   :Dict      							=				num_channels
          lowerCAmelCase__   :List[Any]      							=				kernel_size
          lowerCAmelCase__   :List[Any]      							=				stride
          lowerCAmelCase__   :Tuple      							=				padding
          lowerCAmelCase__   :str      							=				hidden_sizes
          lowerCAmelCase__   :Union[str, Any]      							=				num_attention_heads
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :Union[str, Any]      							=				key_dim
          lowerCAmelCase__   :str      							=				drop_path_rate
          lowerCAmelCase__   :int      							=				patch_size
          lowerCAmelCase__   :Dict      							=				attention_ratio
          lowerCAmelCase__   :Dict      							=				mlp_ratio
          lowerCAmelCase__   :Optional[int]      							=				initializer_range
          lowerCAmelCase__   :Tuple      							=				[
              ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    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



