

'''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
)
_snake_case     =  logging.getLogger(__name__)







def 					_A		(	snake_case		,							snake_case     )			->	str:
   _lowercase			:      List[Any]			     =   np.argmax(snake_case		,							axis=1     )
   return np.sum(outputs == labels     )







def 					_A		(	snake_case     )			->	Optional[int]:
   with open(snake_case		,							encoding="utf_8"     ) as f:
      _lowercase			:      int			     =   csv.reader(snake_case     )
      _lowercase			:      str			     =   []
      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 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case     )			->	int:
   _lowercase			:      Optional[Any]			     =   []
   for dataset in encoded_datasets:
      _lowercase			:      List[Any]			     =   len(snake_case     )
      _lowercase			:      List[Any]			     =   np.zeros((n_batch, 2, input_len)		,							dtype=np.intaa     )
      _lowercase			:      int			     =   np.zeros((n_batch, 2)		,							dtype=np.intaa     )
      _lowercase			:      List[str]			     =   np.full((n_batch, 2, input_len)		,							fill_value=-1_00		,							dtype=np.intaa     )
      _lowercase			:      List[str]			     =   np.zeros((n_batch,)		,							dtype=np.intaa     )
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(snake_case     ):
         _lowercase			:      Optional[int]			     =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _lowercase			:      List[Any]			     =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         _lowercase			:      str			     =   with_conta
         _lowercase			:      Union[str, Any]			     =   with_conta
         _lowercase			:      Optional[int]			     =   len(snake_case     ) - 1
         _lowercase			:      List[str]			     =   len(snake_case     ) - 1
         _lowercase			:      Union[str, Any]			     =   with_conta
         _lowercase			:      Dict			     =   with_conta
         _lowercase			:      Dict			     =   mc_label
      _lowercase			:      Union[str, Any]			     =   (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 					_A		(	)			->	Union[str, Any]:
   _lowercase			:      str			     =   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=42     )
   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=16     )
   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_74     )

   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."     )
   _lowercase			:      Tuple			     =   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     )

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

   # Compute the max input length for the Transformer
   _lowercase			:      List[Any]			     =   model.config.n_positions // 2 - 2
   _lowercase			:      str			     =   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     )
   _lowercase			:      Tuple			     =   min(snake_case		,							model.config.n_positions     )  # Max size of input for the pre-trained model

   # Prepare inputs tensors and dataloaders
   _lowercase			:      Tuple			     =   pre_process_datasets(snake_case		,							snake_case		,							snake_case		,							*snake_case     )
   _lowercase			:      Optional[int]			     =   tensor_datasets[0], tensor_datasets[1]

   _lowercase			:      Optional[int]			     =   TensorDataset(*snake_case     )
   _lowercase			:      Optional[int]			     =   RandomSampler(snake_case     )
   _lowercase			:      Optional[int]			     =   DataLoader(snake_case		,							sampler=snake_case		,							batch_size=args.train_batch_size     )

   _lowercase			:      Union[str, Any]			     =   TensorDataset(*snake_case     )
   _lowercase			:      Any			     =   SequentialSampler(snake_case     )
   _lowercase			:      Optional[Any]			     =   DataLoader(snake_case		,							sampler=snake_case		,							batch_size=args.eval_batch_size     )

   # Prepare optimizer
   if args.do_train:
      if args.max_steps > 0:
         _lowercase			:      str			     =   args.max_steps
         _lowercase			:      Union[str, Any]			     =   args.max_steps // (len(snake_case     ) // args.gradient_accumulation_steps) + 1
      else:
         _lowercase			:      Optional[int]			     =   len(snake_case     ) // args.gradient_accumulation_steps * args.num_train_epochs

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

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

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

         _lowercase			:      List[str]			     =   mc_logits.detach().cpu().numpy()
         _lowercase			:      Dict			     =   mc_labels.to("cpu"     ).numpy()
         _lowercase			:      str			     =   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

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

      _lowercase			:      Optional[int]			     =   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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A:   str								=		{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		["DistilBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[Any]								=		[
          "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DistilBertForMaskedLM",
          "DistilBertForMultipleChoice",
          "DistilBertForQuestionAnswering",
          "DistilBertForSequenceClassification",
          "DistilBertForTokenClassification",
          "DistilBertModel",
          "DistilBertPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Dict								=		[
          "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDistilBertForMaskedLM",
          "TFDistilBertForMultipleChoice",
          "TFDistilBertForQuestionAnswering",
          "TFDistilBertForSequenceClassification",
          "TFDistilBertForTokenClassification",
          "TFDistilBertMainLayer",
          "TFDistilBertModel",
          "TFDistilBertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   int								=		[
          "FlaxDistilBertForMaskedLM",
          "FlaxDistilBertForMultipleChoice",
          "FlaxDistilBertForQuestionAnswering",
          "FlaxDistilBertForSequenceClassification",
          "FlaxDistilBertForTokenClassification",
          "FlaxDistilBertModel",
          "FlaxDistilBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      A:   Optional[int]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
