


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 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  str						):
       _A      :       Optional[int]				    = np.argmax(UpperCamelCase__				,							axis=1						)
       return np.sum(outputs == labels						)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[Any]						):
       with open(UpperCamelCase__				,							encoding="utf_8"						) as f:
              _A      :       str				    = csv.reader(UpperCamelCase__						)
              _A      :       List[Any]				    = []
              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 							_UpperCAmelCase				(UpperCamelCase__     :  Any				,							UpperCamelCase__     :  Any				,							UpperCamelCase__     :  int				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  Optional[Any]				,							UpperCamelCase__     :  Optional[int]						):
       _A      :       int				    = []
       for dataset in encoded_datasets:
              _A      :       Tuple				    = len(UpperCamelCase__						)
              _A      :       List[str]				    = np.zeros((n_batch, 2, input_len)				,							dtype=np.intaa						)
              _A      :       Optional[int]				    = np.zeros((n_batch, 2)				,							dtype=np.intaa						)
              _A      :       Any				    = np.full((n_batch, 2, input_len)				,							fill_value=-100				,							dtype=np.intaa						)
              _A      :       List[Any]				    = np.zeros((n_batch,)				,							dtype=np.intaa						)
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(UpperCamelCase__						):
                     _A      :       Dict				    = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     _A      :       List[str]				    = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     _A      :       int				    = with_conta
                     _A      :       str				    = with_conta
                     _A      :       int				    = len(UpperCamelCase__						) - 1
                     _A      :       str				    = len(UpperCamelCase__						) - 1
                     _A      :       List[Any]				    = with_conta
                     _A      :       str				    = with_conta
                     _A      :       Union[str, Any]				    = mc_label
              _A      :       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 							_UpperCAmelCase				():
       _A      :       Optional[int]				    = 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=42						)
       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=16						)
       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=374						)

       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."						)
       _A      :       Optional[int]				    = 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						)

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

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

       # Prepare inputs tensors and dataloaders
       _A      :       Dict				    = pre_process_datasets(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__				,							*UpperCamelCase__						)
       _A    ,			_A      :       List[Any]				    = tensor_datasets[0], tensor_datasets[1]

       _A      :       Dict				    = TensorDataset(*UpperCamelCase__						)
       _A      :       Optional[int]				    = RandomSampler(UpperCamelCase__						)
       _A      :       int				    = DataLoader(UpperCamelCase__				,							sampler=UpperCamelCase__				,							batch_size=args.train_batch_size						)

       _A      :       int				    = TensorDataset(*UpperCamelCase__						)
       _A      :       List[Any]				    = SequentialSampler(UpperCamelCase__						)
       _A      :       Optional[Any]				    = DataLoader(UpperCamelCase__				,							sampler=UpperCamelCase__				,							batch_size=args.eval_batch_size						)

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     _A      :       Tuple				    = args.max_steps
                     _A      :       Tuple				    = args.max_steps // (len(UpperCamelCase__						) // args.gradient_accumulation_steps) + 1
              else:
                     _A      :       Any				    = len(UpperCamelCase__						) // args.gradient_accumulation_steps * args.num_train_epochs

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

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

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

                     _A      :       List[Any]				    = mc_logits.detach().cpu().numpy()
                     _A      :       Optional[Any]				    = mc_labels.to("cpu"						).numpy()
                     _A      :       Union[str, Any]				    = 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

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

              _A      :       Union[str, Any]				    = 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()




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "OwlViTImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("CLIPTokenizer", "CLIPTokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       int				    = None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead." ,						__lowerCamelCase ,						)
                   _A      :       List[Any]				    = kwargs.pop("feature_extractor")

            _A      :       Dict				    = image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`.")
            if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`.")

            super().__init__(__lowerCamelCase ,						__lowerCamelCase)


     def __call__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="max_length" ,						__lowerCamelCase="np" ,						**__lowerCamelCase)			->				Any:

            if text is None and query_images is None and images is None:
                   raise ValueError(
                       "You have to specify at least one text or query image or image. All three cannot be none.")

            if text is not None:
                   if isinstance(__lowerCamelCase ,						__lowerCamelCase) or (isinstance(__lowerCamelCase ,						__lowerCamelCase) and not isinstance(text[0] ,						__lowerCamelCase)):
                          _A      :       Union[str, Any]				    = [self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)]

                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase) and isinstance(text[0] ,						__lowerCamelCase):
                          _A      :       Optional[Any]				    = []

                          # Maximum number of queries across batch
                          _A      :       str				    = max([len(__lowerCamelCase) for t in text])

                          # Pad all batch samples to max number of text queries
                          for t in text:
                                 if len(__lowerCamelCase) != max_num_queries:
                                        _A      :       Optional[int]				    = t + [" "] * (max_num_queries - len(__lowerCamelCase))

                                 _A      :       List[Any]				    = self.tokenizer(__lowerCamelCase ,						padding=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
                                 encodings.append(__lowerCamelCase)
                   else:
                          raise TypeError("Input text should be a string, a list of strings or a nested list of strings")

                   if return_tensors == "np":
                          _A      :       Tuple				    = np.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[Any]				    = np.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "jax" and is_flax_available():
                          import jax.numpy as jnp

                          _A      :       Optional[int]				    = jnp.concatenate([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Optional[int]				    = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   elif return_tensors == "pt" and is_torch_available():
                          import torch

                          _A      :       Optional[Any]				    = torch.cat([encoding["input_ids"] for encoding in encodings] ,						dim=0)
                          _A      :       Union[str, Any]				    = torch.cat([encoding["attention_mask"] for encoding in encodings] ,						dim=0)

                   elif return_tensors == "tf" and is_tf_available():
                          import tensorflow as tf

                          _A      :       Any				    = tf.stack([encoding["input_ids"] for encoding in encodings] ,						axis=0)
                          _A      :       Tuple				    = tf.stack([encoding["attention_mask"] for encoding in encodings] ,						axis=0)

                   else:
                          raise ValueError("Target return tensor type could not be returned")

                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       Tuple				    = input_ids
                   _A      :       Dict				    = attention_mask

            if query_images is not None:
                   _A      :       Optional[Any]				    = BatchEncoding()
                   _A      :       List[str]				    = self.image_processor(
                       __lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase).pixel_values
                   _A      :       Union[str, Any]				    = query_pixel_values

            if images is not None:
                   _A      :       int				    = self.image_processor(__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)

            if text is not None and images is not None:
                   _A      :       Tuple				    = image_features.pixel_values
                   return encoding
            elif query_images is not None and images is not None:
                   _A      :       int				    = image_features.pixel_values
                   return encoding
            elif text is not None or query_images is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**__lowerCamelCase) ,						tensor_type=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.image_processor.post_process(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.image_processor.post_process_object_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				int:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Optional[int]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				int:
            warnings.warn(
                "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,						__lowerCamelCase ,						)
            return self.image_processor_class


     @property
     def  _lowerCamelCase (						self)			->				List[str]:
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,						__lowerCamelCase ,						)
            return self.image_processor
