


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:						Optional[int]   				=       logging.getLogger(__name__)






def   _lowercase(					__a				:						Dict    ,     __a				:						List[Any]     ):
							a__      =np.argmax(__a    ,     axis=1     )
							return np.sum(outputs == labels     )






def   _lowercase(					__a				:						Union[str, Any]     ):
							with open(__a    ,     encoding='utf_8'     ) as f:
														a__      =csv.reader(__a     )
														a__      =[]
														next(__a     )  # skip the first line
														for line in tqdm(__a     ):
																					output.append((' '.join(line[1:5]     ), line[5], line[6], int(line[-1]     ) - 1)     )
							return output






def   _lowercase(					__a				:						int    ,     __a				:						List[Any]    ,     __a				:						Dict    ,     __a				:						Tuple    ,     __a				:						List[Any]    ,     __a				:						Tuple     ):
							a__      =[]
							for dataset in encoded_datasets:
														a__      =len(__a     )
														a__      =np.zeros((n_batch, 2, input_len)    ,     dtype=np.intaa     )
														a__      =np.zeros((n_batch, 2)    ,     dtype=np.intaa     )
														a__      =np.full((n_batch, 2, input_len)    ,     fill_value=-100    ,     dtype=np.intaa     )
														a__      =np.zeros((n_batch,)    ,     dtype=np.intaa     )
														for (
														    i,
														    (story, conta, conta, mc_label),
														) in enumerate(__a     ):
																					a__      =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					a__      =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																					a__      =with_conta
																					a__      =with_conta
																					a__      =len(__a     ) - 1
																					a__      =len(__a     ) - 1
																					a__      =with_conta
																					a__      =with_conta
																					a__      =mc_label
														a__      =(input_ids, mc_token_ids, lm_labels, mc_labels)
														tensor_datasets.append(tuple(torch.tensor(__a     ) for t in all_inputs     )     )
							return tensor_datasets






def   _lowercase(					):
							a__      =argparse.ArgumentParser()
							parser.add_argument('--model_name'    ,     type=__a    ,     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=__a    ,     type=__a    ,     required=__a    ,     help='The output directory where the model predictions and checkpoints will be written.'    ,     )
							parser.add_argument('--train_dataset'    ,     type=__a    ,     default=''     )
							parser.add_argument('--eval_dataset'    ,     type=__a    ,     default=''     )
							parser.add_argument('--seed'    ,     type=__a    ,     default=42     )
							parser.add_argument('--num_train_epochs'    ,     type=__a    ,     default=3     )
							parser.add_argument('--train_batch_size'    ,     type=__a    ,     default=8     )
							parser.add_argument('--eval_batch_size'    ,     type=__a    ,     default=16     )
							parser.add_argument('--adam_epsilon'    ,     default=1e-8    ,     type=__a    ,     help='Epsilon for Adam optimizer.'     )
							parser.add_argument('--max_grad_norm'    ,     type=__a    ,     default=1     )
							parser.add_argument(
							    '--max_steps'    ,     default=-1    ,     type=__a    ,     help=(
							        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
							    )    ,     )
							parser.add_argument(
							    '--gradient_accumulation_steps'    ,     type=__a    ,     default=1    ,     help='Number of updates steps to accumulate before                        performing a backward/update pass.'    ,     )
							parser.add_argument('--learning_rate'    ,     type=__a    ,     default=6.25e-5     )
							parser.add_argument('--warmup_steps'    ,     default=0    ,     type=__a    ,     help='Linear warmup over warmup_steps.'     )
							parser.add_argument('--lr_schedule'    ,     type=__a    ,     default='warmup_linear'     )
							parser.add_argument('--weight_decay'    ,     type=__a    ,     default=0.01     )
							parser.add_argument('--lm_coef'    ,     type=__a    ,     default=0.9     )
							parser.add_argument('--n_valid'    ,     type=__a    ,     default=374     )

							parser.add_argument('--server_ip'    ,     type=__a    ,     default=''    ,     help='Can be used for distant debugging.'     )
							parser.add_argument('--server_port'    ,     type=__a    ,     default=''    ,     help='Can be used for distant debugging.'     )
							a__      =parser.parse_args()
							print(__a     )

							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=__a     )
														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__      =torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
							a__      =torch.cuda.device_count()
							logger.info('device: {}, n_gpu {}'.format(__a    ,     __a     )     )

							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__      =['_start_', '_delimiter_', '_classify_']
							a__      =OpenAIGPTTokenizer.from_pretrained(args.model_name     )
							tokenizer.add_tokens(__a     )
							a__      =tokenizer.convert_tokens_to_ids(__a     )
							a__      =OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name     )
							model.resize_token_embeddings(len(__a     )     )
							model.to(__a     )

							# Load and encode the datasets
							def tokenize_and_encode(__a				:						Any     ):
														if isinstance(__a    ,     __a     ):
																					return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__a     )     )
														elif isinstance(__a    ,     __a     ):
																					return obj
														return [tokenize_and_encode(__a     ) for o in obj]

							logger.info('Encoding dataset...'     )
							a__      =load_rocstories_dataset(args.train_dataset     )
							a__      =load_rocstories_dataset(args.eval_dataset     )
							a__      =(train_dataset, eval_dataset)
							a__      =tokenize_and_encode(__a     )

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

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

							a__      =TensorDataset(*__a     )
							a__      =RandomSampler(__a     )
							a__      =DataLoader(__a    ,     sampler=__a    ,     batch_size=args.train_batch_size     )

							a__      =TensorDataset(*__a     )
							a__      =SequentialSampler(__a     )
							a__      =DataLoader(__a    ,     sampler=__a    ,     batch_size=args.eval_batch_size     )

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

														a__      =list(model.named_parameters()     )
														a__      =['bias', 'LayerNorm.bias', 'LayerNorm.weight']
														a__      =[
														    {
														        '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__      =AdamW(__a    ,     lr=args.learning_rate    ,     eps=args.adam_epsilon     )
														a__      =get_linear_schedule_with_warmup(
														    __a    ,     num_warmup_steps=args.warmup_steps    ,     num_training_steps=__a     )

							if args.do_train:
														a__    ,     a__    ,     a__      =0, 0, None
														model.train()
														for _ in trange(int(args.num_train_epochs     )    ,     desc='Epoch'     ):
																					a__      =0
																					a__      =0
																					a__      =tqdm(__a    ,     desc='Training'     )
																					for step, batch in enumerate(__a     ):
																												a__      =tuple(t.to(__a     ) for t in batch     )
																												a__    ,     a__    ,     a__    ,     a__      =batch
																												a__      =model(__a    ,     mc_token_ids=__a    ,     lm_labels=__a    ,     mc_labels=__a     )
																												a__      =args.lm_coef * losses[0] + losses[1]
																												loss.backward()
																												optimizer.step()
																												scheduler.step()
																												optimizer.zero_grad()
																												tr_loss += loss.item()
																												a__      =(
																												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																												)
																												nb_tr_steps += 1
																												a__      ='Training loss: {:.2e} lr: {:.2e}'.format(__a    ,     scheduler.get_lr()[0]     )

    # Save a trained model
							if args.do_train:
														# Save a trained model, configuration and tokenizer
														a__      =model.module if hasattr(__a    ,     'module'     ) else model  # Only save the model itself

														# If we save using the predefined names, we can load using `from_pretrained`
														a__      =os.path.join(args.output_dir    ,     __a     )
														a__      =os.path.join(args.output_dir    ,     __a     )

														torch.save(model_to_save.state_dict()    ,     __a     )
														model_to_save.config.to_json_file(__a     )
														tokenizer.save_vocabulary(args.output_dir     )

														# Load a trained model and vocabulary that you have fine-tuned
														a__      =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
														a__      =OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
														model.to(__a     )

							if args.do_eval:
														model.eval()
														a__    ,     a__      =0, 0
														a__    ,     a__      =0, 0
														for batch in tqdm(__a    ,     desc='Evaluating'     ):
																					a__      =tuple(t.to(__a     ) for t in batch     )
																					a__    ,     a__    ,     a__    ,     a__      =batch
																					with torch.no_grad():
																												a__    ,     a__    ,     a__    ,     a__      =model(
																												    __a    ,     mc_token_ids=__a    ,     lm_labels=__a    ,     mc_labels=__a     )

																					a__      =mc_logits.detach().cpu().numpy()
																					a__      =mc_labels.to('cpu'     ).numpy()
																					a__      =accuracy(__a    ,     __a     )

																					eval_loss += mc_loss.mean().item()
																					eval_accuracy += tmp_eval_accuracy

																					nb_eval_examples += input_ids.size(0     )
																					nb_eval_steps += 1

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

														a__      =os.path.join(args.output_dir    ,     'eval_results.txt'     )
														with open(__a    ,     'w'     ) as writer:
																					logger.info('***** Eval results *****'     )
																					for key in sorted(result.keys()     ):
																												logger.info('  %s = %s'    ,     __a    ,     str(result[key]     )     )
																												writer.write('%s = %s\n' % (key, str(result[key]     ))     )


if __name__ == "__main__":
	main()




import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,				UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




