




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 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       np.argmax(SCREAMING_SNAKE_CASE_      ,      axis=1     )
					return np.sum(outputs == labels     )







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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       []
					for dataset in encoded_datasets:
										__lowerCAmelCase				   =       len(SCREAMING_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=-1_00      ,      dtype=np.intaa     )
										__lowerCAmelCase				   =       np.zeros((n_batch,)      ,      dtype=np.intaa     )
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(SCREAMING_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(SCREAMING_SNAKE_CASE_     ) - 1
															__lowerCAmelCase				   =       len(SCREAMING_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(SCREAMING_SNAKE_CASE_     ) for t in all_inputs     )     )
					return tensor_datasets







def 	_a      (						):
					__lowerCAmelCase				   =       argparse.ArgumentParser()
					parser.add_argument("--model_name"      ,      type=SCREAMING_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=SCREAMING_SNAKE_CASE_      ,      type=SCREAMING_SNAKE_CASE_      ,      required=SCREAMING_SNAKE_CASE_      ,      help="The output directory where the model predictions and checkpoints will be written."      ,      )
					parser.add_argument("--train_dataset"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""     )
					parser.add_argument("--eval_dataset"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""     )
					parser.add_argument("--seed"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=42     )
					parser.add_argument("--num_train_epochs"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=3     )
					parser.add_argument("--train_batch_size"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=8     )
					parser.add_argument("--eval_batch_size"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=16     )
					parser.add_argument("--adam_epsilon"      ,      default=1E-8      ,      type=SCREAMING_SNAKE_CASE_      ,      help="Epsilon for Adam optimizer."     )
					parser.add_argument("--max_grad_norm"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=1     )
					parser.add_argument(
					    "--max_steps"      ,      default=-1      ,      type=SCREAMING_SNAKE_CASE_      ,      help=(
					        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
					    )      ,      )
					parser.add_argument(
					    "--gradient_accumulation_steps"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=1      ,      help="Number of updates steps to accumulate before                        performing a backward/update pass."      ,      )
					parser.add_argument("--learning_rate"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=6.25E-5     )
					parser.add_argument("--warmup_steps"      ,      default=0      ,      type=SCREAMING_SNAKE_CASE_      ,      help="Linear warmup over warmup_steps."     )
					parser.add_argument("--lr_schedule"      ,      type=SCREAMING_SNAKE_CASE_      ,      default="warmup_linear"     )
					parser.add_argument("--weight_decay"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=0.01     )
					parser.add_argument("--lm_coef"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=0.9     )
					parser.add_argument("--n_valid"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=3_74     )

					parser.add_argument("--server_ip"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""      ,      help="Can be used for distant debugging."     )
					parser.add_argument("--server_port"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""      ,      help="Can be used for distant debugging."     )
					__lowerCAmelCase				   =       parser.parse_args()
					print(SCREAMING_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=SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_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(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name     )
					model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_     )     )
					model.to(SCREAMING_SNAKE_CASE_     )

					# Load and encode the datasets
					def tokenize_and_encode(SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
										if isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
															return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE_     )     )
										elif isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
															return obj
										return [tokenize_and_encode(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      model.config.n_positions     )  # Max size of input for the pre-trained model

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

					__lowerCAmelCase				   =       TensorDataset(*SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       RandomSampler(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       DataLoader(SCREAMING_SNAKE_CASE_      ,      sampler=SCREAMING_SNAKE_CASE_      ,      batch_size=args.train_batch_size     )

					__lowerCAmelCase				   =       TensorDataset(*SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       SequentialSampler(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       DataLoader(SCREAMING_SNAKE_CASE_      ,      sampler=SCREAMING_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(SCREAMING_SNAKE_CASE_     ) // args.gradient_accumulation_steps) + 1
										else:
															__lowerCAmelCase				   =       len(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      lr=args.learning_rate      ,      eps=args.adam_epsilon     )
										__lowerCAmelCase				   =       get_linear_schedule_with_warmup(
										    SCREAMING_SNAKE_CASE_      ,      num_warmup_steps=args.warmup_steps      ,      num_training_steps=SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      desc="Training"     )
															for step, batch in enumerate(SCREAMING_SNAKE_CASE_     ):
																				__lowerCAmelCase				   =       tuple(t.to(SCREAMING_SNAKE_CASE_     ) for t in batch     )
																				__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       batch
																				__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_      ,      mc_token_ids=SCREAMING_SNAKE_CASE_      ,      lm_labels=SCREAMING_SNAKE_CASE_      ,      mc_labels=SCREAMING_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(SCREAMING_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(SCREAMING_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      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       os.path.join(args.output_dir      ,      SCREAMING_SNAKE_CASE_     )

										torch.save(model_to_save.state_dict()      ,      SCREAMING_SNAKE_CASE_     )
										model_to_save.config.to_json_file(SCREAMING_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(SCREAMING_SNAKE_CASE_     )

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

															__lowerCAmelCase				   =       mc_logits.detach().cpu().numpy()
															__lowerCAmelCase				   =       mc_labels.to("cpu"     ).numpy()
															__lowerCAmelCase				   =       accuracy(SCREAMING_SNAKE_CASE_      ,      SCREAMING_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(SCREAMING_SNAKE_CASE_      ,      "w"     ) as writer:
															logger.info("***** Eval results *****"     )
															for key in sorted(result.keys()     ):
																				logger.info("  %s = %s"      ,      SCREAMING_SNAKE_CASE_      ,      str(result[key]     )     )
																				writer.write("%s = %s\n" % (key, str(result[key]     ))     )


if __name__ == "__main__":
					main()










import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "down_block_types": down_block_types,
					    "block_out_channels": block_out_channels,
					    "up_block_types": up_block_types,
					    "layers_per_block": 1,
					    "use_timestep_embedding": True,
					    "out_block_type": "OutConv1DBlock",
					    "norm_num_groups": 8,
					    "downsample_each_block": False,
					    "in_channels": 14,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "sample_size": 6_55_36,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""     )
					with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       {
					    "in_channels": 14,
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": (),
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 6_55_36,
					    "out_channels": 14,
					    "extra_in_channels": 0,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1,
					    "norm_num_groups": 8,
					    "act_fn": "mish",
					}

					__lowerCAmelCase				   =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"     )
					__lowerCAmelCase				   =       model
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )

					__lowerCAmelCase				   =       dict(zip(state_dict.keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )

					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					torch.save(hf_value_function.state_dict()      ,      "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"     )
					with open("hub/hopper-medium-v2/value_function/config.json"      ,      "w"     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()




