




"""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
)
a      =							logging.getLogger(__name__)





def 							lowercase					(snake_case__   : List[Any]      ,       snake_case__   : Any )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				np.argmax(snake_case__      ,       axis=1 )
				return np.sum(outputs == labels )





def 							lowercase					(snake_case__   : int )     ->  Any:


				'''simple docstring'''






				with open(snake_case__      ,       encoding="""utf_8""" ) as f:
								lowerCAmelCase  =				csv.reader(snake_case__ )
								lowerCAmelCase  =				[]
								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 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : int      ,       snake_case__   : Any      ,       snake_case__   : Optional[Any] )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				for dataset in encoded_datasets:
								lowerCAmelCase  =				len(snake_case__ )
								lowerCAmelCase  =				np.zeros((n_batch, 2, input_len)      ,       dtype=np.intaa )
								lowerCAmelCase  =				np.zeros((n_batch, 2)      ,       dtype=np.intaa )
								lowerCAmelCase  =				np.full((n_batch, 2, input_len)      ,       fill_value=-100      ,       dtype=np.intaa )
								lowerCAmelCase  =				np.zeros((n_batch,)      ,       dtype=np.intaa )
								for (
								    i,
								    (story, conta, conta, mc_label),
								) in enumerate(snake_case__ ):
												lowerCAmelCase  =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												lowerCAmelCase  =				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												lowerCAmelCase  =				with_conta
												lowerCAmelCase  =				with_conta
												lowerCAmelCase  =				len(snake_case__ ) - 1
												lowerCAmelCase  =				len(snake_case__ ) - 1
												lowerCAmelCase  =				with_conta
												lowerCAmelCase  =				with_conta
												lowerCAmelCase  =				mc_label
								lowerCAmelCase  =				(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 							lowercase					()     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				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=374 )

				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.""" )
				lowerCAmelCase  =				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 )

				lowerCAmelCase  =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
				lowerCAmelCase  =				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
				lowerCAmelCase  =				["""_start_""", """_delimiter_""", """_classify_"""]
				lowerCAmelCase  =				OpenAIGPTTokenizer.from_pretrained(args.model_name )
				tokenizer.add_tokens(snake_case__ )
				lowerCAmelCase  =				tokenizer.convert_tokens_to_ids(snake_case__ )
				lowerCAmelCase  =				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__   : str ):
								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...""" )
				lowerCAmelCase  =				load_rocstories_dataset(args.train_dataset )
				lowerCAmelCase  =				load_rocstories_dataset(args.eval_dataset )
				lowerCAmelCase  =				(train_dataset, eval_dataset)
				lowerCAmelCase  =				tokenize_and_encode(snake_case__ )

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

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

				lowerCAmelCase  =				TensorDataset(*snake_case__ )
				lowerCAmelCase  =				RandomSampler(snake_case__ )
				lowerCAmelCase  =				DataLoader(snake_case__      ,       sampler=snake_case__      ,       batch_size=args.train_batch_size )

				lowerCAmelCase  =				TensorDataset(*snake_case__ )
				lowerCAmelCase  =				SequentialSampler(snake_case__ )
				lowerCAmelCase  =				DataLoader(snake_case__      ,       sampler=snake_case__      ,       batch_size=args.eval_batch_size )

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

								lowerCAmelCase  =				list(model.named_parameters() )
								lowerCAmelCase  =				["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
								lowerCAmelCase  =				[
								    {
								        """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  =				AdamW(snake_case__      ,       lr=args.learning_rate      ,       eps=args.adam_epsilon )
								lowerCAmelCase  =				get_linear_schedule_with_warmup(
								    snake_case__      ,       num_warmup_steps=args.warmup_steps      ,       num_training_steps=snake_case__ )

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

				if args.do_eval:
								model.eval()
								lowerCAmelCase ,						lowerCAmelCase  =				0, 0
								lowerCAmelCase ,						lowerCAmelCase  =				0, 0
								for batch in tqdm(snake_case__      ,       desc="""Evaluating""" ):
												lowerCAmelCase  =				tuple(t.to(snake_case__ ) for t in batch )
												lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				batch
												with torch.no_grad():
																lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				model(
																    snake_case__      ,       mc_token_ids=snake_case__      ,       lm_labels=snake_case__      ,       mc_labels=snake_case__ )

												lowerCAmelCase  =				mc_logits.detach().cpu().numpy()
												lowerCAmelCase  =				mc_labels.to("""cpu""" ).numpy()
												lowerCAmelCase  =				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

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

								lowerCAmelCase  =				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 List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      =							logging.get_logger(__name__)

a      =							{
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			str = "student_t"						,						lowerCAmelCase				:			str = "nll"						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			List[int] = [1, 2, 3, 4, 5, 6, 7]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			int = 64						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			str = "gelu"						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			float = 0.02						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			int = 10						,						lowerCAmelCase				:			int = 25						,						lowerCAmelCase				:			int = 3						,						**lowerCAmelCase				:			Tuple						,						):
										# time series specific configuration
										lowerCAmelCase  =				prediction_length
										lowerCAmelCase  =				context_length if context_length is not None else prediction_length
										lowerCAmelCase  =				distribution_output
										lowerCAmelCase  =				loss
										lowerCAmelCase  =				input_size
										lowerCAmelCase  =				num_time_features
										lowerCAmelCase  =				lags_sequence
										lowerCAmelCase  =				scaling
										lowerCAmelCase  =				num_dynamic_real_features
										lowerCAmelCase  =				num_static_real_features
										lowerCAmelCase  =				num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The cardinality should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				cardinality
										else:
														lowerCAmelCase  =				[0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				embedding_dimension
										else:
														lowerCAmelCase  =				[min(50						,						(cat + 1) // 2					) for cat in self.cardinality]
										lowerCAmelCase  =				num_parallel_samples

										# Transformer architecture configuration
										lowerCAmelCase  =				input_size * len(self.lags_sequence					) + self._number_of_features
										lowerCAmelCase  =				d_model
										lowerCAmelCase  =				encoder_attention_heads
										lowerCAmelCase  =				decoder_attention_heads
										lowerCAmelCase  =				encoder_ffn_dim
										lowerCAmelCase  =				decoder_ffn_dim
										lowerCAmelCase  =				encoder_layers
										lowerCAmelCase  =				decoder_layers

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

										super().__init__(is_encoder_decoder=lowerCAmelCase						,						**lowerCAmelCase					)




						@property
						def   __lowercase (							self				:			Tuple					):
										return (
										    sum(self.embedding_dimension					)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)





