






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
)
__UpperCAmelCase								=						logging.getLogger(__name__)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						np.argmax(__a     ,       axis=1  )
					return np.sum(outputs == labels  )




def      lowercase__      (     __snake_case    :							Tuple  ):


					'''simple docstring'''


					with open(__a     ,       encoding='utf_8'  ) as f:
										UpperCAmelCase_    :      Any            =						csv.reader(__a  )
										UpperCAmelCase_    :      int            =						[]
										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__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							List[Any]     ,       __snake_case    :							Dict     ,       __snake_case    :							Optional[Any]     ,       __snake_case    :							Optional[Any]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						[]
					for dataset in encoded_datasets:
										UpperCAmelCase_    :      Any            =						len(__a  )
										UpperCAmelCase_    :      Optional[int]            =						np.zeros((n_batch, 2, input_len)     ,       dtype=np.intaa  )
										UpperCAmelCase_    :      List[Any]            =						np.zeros((n_batch, 2)     ,       dtype=np.intaa  )
										UpperCAmelCase_    :      Optional[Any]            =						np.full((n_batch, 2, input_len)     ,       fill_value=-100     ,       dtype=np.intaa  )
										UpperCAmelCase_    :      Tuple            =						np.zeros((n_batch,)     ,       dtype=np.intaa  )
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(__a  ):
															UpperCAmelCase_    :      Tuple            =						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															UpperCAmelCase_    :      Any            =						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															UpperCAmelCase_    :      List[Any]            =						with_conta
															UpperCAmelCase_    :      str            =						with_conta
															UpperCAmelCase_    :      List[Any]            =						len(__a  ) - 1
															UpperCAmelCase_    :      Union[str, Any]            =						len(__a  ) - 1
															UpperCAmelCase_    :      Tuple            =						with_conta
															UpperCAmelCase_    :      List[Any]            =						with_conta
															UpperCAmelCase_    :      List[str]            =						mc_label
										UpperCAmelCase_    :      str            =						(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__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						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.'  )
					UpperCAmelCase_    :      int            =						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  )

					UpperCAmelCase_    :      List[str]            =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
					UpperCAmelCase_    :      List[str]            =						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
					UpperCAmelCase_    :      Union[str, Any]            =						['_start_', '_delimiter_', '_classify_']
					UpperCAmelCase_    :      int            =						OpenAIGPTTokenizer.from_pretrained(args.model_name  )
					tokenizer.add_tokens(__a  )
					UpperCAmelCase_    :      Optional[int]            =						tokenizer.convert_tokens_to_ids(__a  )
					UpperCAmelCase_    :      List[str]            =						OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name  )
					model.resize_token_embeddings(len(__a  )  )
					model.to(__a  )

					# Load and encode the datasets
					def tokenize_and_encode(__snake_case    :							Tuple  ):
										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...'  )
					UpperCAmelCase_    :      Optional[int]            =						load_rocstories_dataset(args.train_dataset  )
					UpperCAmelCase_    :      Tuple            =						load_rocstories_dataset(args.eval_dataset  )
					UpperCAmelCase_    :      List[Any]            =						(train_dataset, eval_dataset)
					UpperCAmelCase_    :      int            =						tokenize_and_encode(__a  )

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

					# Prepare inputs tensors and dataloaders
					UpperCAmelCase_    :      List[str]            =						pre_process_datasets(__a     ,       __a     ,       __a     ,       *__a  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						tensor_datasets[0], tensor_datasets[1]

					UpperCAmelCase_    :      List[Any]            =						TensorDataset(*__a  )
					UpperCAmelCase_    :      Any            =						RandomSampler(__a  )
					UpperCAmelCase_    :      int            =						DataLoader(__a     ,       sampler=__a     ,       batch_size=args.train_batch_size  )

					UpperCAmelCase_    :      Optional[Any]            =						TensorDataset(*__a  )
					UpperCAmelCase_    :      Optional[Any]            =						SequentialSampler(__a  )
					UpperCAmelCase_    :      int            =						DataLoader(__a     ,       sampler=__a     ,       batch_size=args.eval_batch_size  )

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

										UpperCAmelCase_    :      Any            =						list(model.named_parameters()  )
										UpperCAmelCase_    :      str            =						['bias', 'LayerNorm.bias', 'LayerNorm.weight']
										UpperCAmelCase_    :      Optional[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},
										]
										UpperCAmelCase_    :      str            =						AdamW(__a     ,       lr=args.learning_rate     ,       eps=args.adam_epsilon  )
										UpperCAmelCase_    :      Optional[int]            =						get_linear_schedule_with_warmup(
										    __a     ,       num_warmup_steps=args.warmup_steps     ,       num_training_steps=__a  )

					if args.do_train:
										UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						0, 0, None
										model.train()
										for _ in trange(int(args.num_train_epochs  )     ,       desc='Epoch'  ):
															UpperCAmelCase_    :      Any            =						0
															UpperCAmelCase_    :      Optional[Any]            =						0
															UpperCAmelCase_    :      Dict            =						tqdm(__a     ,       desc='Training'  )
															for step, batch in enumerate(__a  ):
																				UpperCAmelCase_    :      Any            =						tuple(t.to(__a  ) for t in batch  )
																				UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						batch
																				UpperCAmelCase_    :      int            =						model(__a     ,       mc_token_ids=__a     ,       lm_labels=__a     ,       mc_labels=__a  )
																				UpperCAmelCase_    :      Any            =						args.lm_coef * losses[0] + losses[1]
																				loss.backward()
																				optimizer.step()
																				scheduler.step()
																				optimizer.zero_grad()
																				tr_loss += loss.item()
																				UpperCAmelCase_    :      Union[str, Any]            =						(
																				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																				)
																				nb_tr_steps += 1
																				UpperCAmelCase_    :      int            =						'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
										UpperCAmelCase_    :      Optional[int]            =						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`
										UpperCAmelCase_    :      Dict            =						os.path.join(args.output_dir     ,       __a  )
										UpperCAmelCase_    :      int            =						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
										UpperCAmelCase_    :      int            =						OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
										UpperCAmelCase_    :      Any            =						OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
										model.to(__a  )

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

															UpperCAmelCase_    :      Dict            =						mc_logits.detach().cpu().numpy()
															UpperCAmelCase_    :      Optional[Any]            =						mc_labels.to('cpu'  ).numpy()
															UpperCAmelCase_    :      Union[str, Any]            =						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

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

										UpperCAmelCase_    :      int            =						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
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
					parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



