






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






def _lowerCAmelCase	(  __lowerCamelCase:int ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     np.argmax(__lowerCamelCase ,						axis=1   )
						return np.sum(outputs == labels   )






def _lowerCAmelCase	(  __lowerCamelCase:List[Any]   ):

						'''simple docstring'''






						with open(__lowerCamelCase ,						encoding="utf_8"   ) as f:
												__magic_name__            =     csv.reader(__lowerCamelCase   )
												__magic_name__            =     []
												next(__lowerCamelCase   )  # skip the first line
												for line in tqdm(__lowerCamelCase   ):
																		output.append((" ".join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
						return output






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int] ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     []
						for dataset in encoded_datasets:
												__magic_name__            =     len(__lowerCamelCase   )
												__magic_name__            =     np.zeros((n_batch, 2, input_len) ,						dtype=np.intaa   )
												__magic_name__            =     np.zeros((n_batch, 2) ,						dtype=np.intaa   )
												__magic_name__            =     np.full((n_batch, 2, input_len) ,						fill_value=-1_0_0 ,						dtype=np.intaa   )
												__magic_name__            =     np.zeros((n_batch,) ,						dtype=np.intaa   )
												for (
												    i,
												    (story, conta, conta, mc_label),
												) in enumerate(__lowerCamelCase   ):
																		__magic_name__            =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		__magic_name__            =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
																		__magic_name__            =     with_conta
																		__magic_name__            =     with_conta
																		__magic_name__            =     len(__lowerCamelCase   ) - 1
																		__magic_name__            =     len(__lowerCamelCase   ) - 1
																		__magic_name__            =     with_conta
																		__magic_name__            =     with_conta
																		__magic_name__            =     mc_label
												__magic_name__            =     (input_ids, mc_token_ids, lm_labels, mc_labels)
												tensor_datasets.append(tuple(torch.tensor(__lowerCamelCase   ) for t in all_inputs   )   )
						return tensor_datasets






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     argparse.ArgumentParser()
						parser.add_argument("--model_name" ,						type=__lowerCamelCase ,						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=__lowerCamelCase ,						type=__lowerCamelCase ,						required=__lowerCamelCase ,						help="The output directory where the model predictions and checkpoints will be written." ,						)
						parser.add_argument("--train_dataset" ,						type=__lowerCamelCase ,						default=""   )
						parser.add_argument("--eval_dataset" ,						type=__lowerCamelCase ,						default=""   )
						parser.add_argument("--seed" ,						type=__lowerCamelCase ,						default=4_2   )
						parser.add_argument("--num_train_epochs" ,						type=__lowerCamelCase ,						default=3   )
						parser.add_argument("--train_batch_size" ,						type=__lowerCamelCase ,						default=8   )
						parser.add_argument("--eval_batch_size" ,						type=__lowerCamelCase ,						default=1_6   )
						parser.add_argument("--adam_epsilon" ,						default=1E-8 ,						type=__lowerCamelCase ,						help="Epsilon for Adam optimizer."   )
						parser.add_argument("--max_grad_norm" ,						type=__lowerCamelCase ,						default=1   )
						parser.add_argument(
						    "--max_steps" ,						default=-1 ,						type=__lowerCamelCase ,						help=(
						        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
						    ) ,						)
						parser.add_argument(
						    "--gradient_accumulation_steps" ,						type=__lowerCamelCase ,						default=1 ,						help="Number of updates steps to accumulate before                        performing a backward/update pass." ,						)
						parser.add_argument("--learning_rate" ,						type=__lowerCamelCase ,						default=6.25E-5   )
						parser.add_argument("--warmup_steps" ,						default=0 ,						type=__lowerCamelCase ,						help="Linear warmup over warmup_steps."   )
						parser.add_argument("--lr_schedule" ,						type=__lowerCamelCase ,						default="warmup_linear"   )
						parser.add_argument("--weight_decay" ,						type=__lowerCamelCase ,						default=0.0_1   )
						parser.add_argument("--lm_coef" ,						type=__lowerCamelCase ,						default=0.9   )
						parser.add_argument("--n_valid" ,						type=__lowerCamelCase ,						default=3_7_4   )

						parser.add_argument("--server_ip" ,						type=__lowerCamelCase ,						default="" ,						help="Can be used for distant debugging."   )
						parser.add_argument("--server_port" ,						type=__lowerCamelCase ,						default="" ,						help="Can be used for distant debugging."   )
						__magic_name__            =     parser.parse_args()
						print(__lowerCamelCase   )

						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=__lowerCamelCase   )
												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   )

						__magic_name__            =     torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
						__magic_name__            =     torch.cuda.device_count()
						logger.info("device: {}, n_gpu {}".format(__lowerCamelCase ,						__lowerCamelCase   )   )

						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
						__magic_name__            =     ["_start_", "_delimiter_", "_classify_"]
						__magic_name__            =     OpenAIGPTTokenizer.from_pretrained(args.model_name   )
						tokenizer.add_tokens(__lowerCamelCase   )
						__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase   )
						__magic_name__            =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
						model.resize_token_embeddings(len(__lowerCamelCase   )   )
						model.to(__lowerCamelCase   )

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

						logger.info("Encoding dataset..."   )
						__magic_name__            =     load_rocstories_dataset(args.train_dataset   )
						__magic_name__            =     load_rocstories_dataset(args.eval_dataset   )
						__magic_name__            =     (train_dataset, eval_dataset)
						__magic_name__            =     tokenize_and_encode(__lowerCamelCase   )

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

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

						__magic_name__            =     TensorDataset(*__lowerCamelCase   )
						__magic_name__            =     RandomSampler(__lowerCamelCase   )
						__magic_name__            =     DataLoader(__lowerCamelCase ,						sampler=__lowerCamelCase ,						batch_size=args.train_batch_size   )

						__magic_name__            =     TensorDataset(*__lowerCamelCase   )
						__magic_name__            =     SequentialSampler(__lowerCamelCase   )
						__magic_name__            =     DataLoader(__lowerCamelCase ,						sampler=__lowerCamelCase ,						batch_size=args.eval_batch_size   )

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

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

						if args.do_train:
												__magic_name__      ,    __magic_name__      ,    __magic_name__            =     0, 0, None
												model.train()
												for _ in trange(int(args.num_train_epochs   ) ,						desc="Epoch"   ):
																		__magic_name__            =     0
																		__magic_name__            =     0
																		__magic_name__            =     tqdm(__lowerCamelCase ,						desc="Training"   )
																		for step, batch in enumerate(__lowerCamelCase   ):
																								__magic_name__            =     tuple(t.to(__lowerCamelCase   ) for t in batch   )
																								__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     batch
																								__magic_name__            =     model(__lowerCamelCase ,						mc_token_ids=__lowerCamelCase ,						lm_labels=__lowerCamelCase ,						mc_labels=__lowerCamelCase   )
																								__magic_name__            =     args.lm_coef * losses[0] + losses[1]
																								loss.backward()
																								optimizer.step()
																								scheduler.step()
																								optimizer.zero_grad()
																								tr_loss += loss.item()
																								__magic_name__            =     (
																								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																								)
																								nb_tr_steps += 1
																								__magic_name__            =     "Training loss: {:.2e} lr: {:.2e}".format(__lowerCamelCase ,						scheduler.get_lr()[0]   )

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

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

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

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

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

																		__magic_name__            =     mc_logits.detach().cpu().numpy()
																		__magic_name__            =     mc_labels.to("cpu"   ).numpy()
																		__magic_name__            =     accuracy(__lowerCamelCase ,						__lowerCamelCase   )

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

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

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

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


if __name__ == "__main__":
			main()











"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(UnCLIPScheduler,)







				def     _snake_case					(							self							:						Any						,    **__lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     {
										    "num_train_timesteps": 1_0_0_0,
										    "variance_type": "fixed_small_log",
										    "clip_sample": True,
										    "clip_sample_range": 1.0,
										    "prediction_type": "epsilon",
										}

										config.update(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Any:
										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										for clip_sample_range in [1, 5, 1_0, 2_0]:
																self.check_over_configs(clip_sample_range=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										for time_step in [0, 5_0_0, 9_9_9]:
																for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																												continue

																						self.check_over_forward(time_step=__lowerCamelCase						,    prev_timestep=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="fixed_small_log"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.054_9625	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.999_4987	)	) < 1e-5







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="learned_range"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     0.5

										assert scheduler._get_variance(1						,    predicted_variance=__lowerCamelCase	) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_8_7						,    predicted_variance=__lowerCamelCase	) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_9_9						,    predicted_variance=__lowerCamelCase	) - -0.001_0011 < 1e-5







				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 252.268_2495	) < 1e-2
										assert abs(result_mean.item() - 0.328_4743	) < 1e-3







				def     _snake_case					(							self							:						int	)				->					int:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(2_5	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																if i + 1 == timesteps.shape[0]:
																						__magic_name__            =     None
																else:
																						__magic_name__            =     timesteps[i + 1]

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(
																    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    prev_timestep=__lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 258.204_4983	) < 1e-2
										assert abs(result_mean.item() - 0.336_2038	) < 1e-3







				def     _snake_case					(							self							:						List[str]	)				->					Any:
										pass







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										pass



