


'''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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Any       				=       np.argmax(UpperCamelCase__	, axis=1   )
			return np.sum(outputs == labels   )







def       _UpperCamelCase (						UpperCamelCase__   ):



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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       []
			for dataset in encoded_datasets:
						UpperCAmelCase__					:   str       				=       len(UpperCamelCase__   )
						UpperCAmelCase__					:   Union[str, Any]       				=       np.zeros((n_batch, 2, input_len)	, dtype=np.intaa   )
						UpperCAmelCase__					:   str       				=       np.zeros((n_batch, 2)	, dtype=np.intaa   )
						UpperCAmelCase__					:   List[str]       				=       np.full((n_batch, 2, input_len)	, fill_value=-1_0_0	, dtype=np.intaa   )
						UpperCAmelCase__					:   Optional[Any]       				=       np.zeros((n_batch,)	, dtype=np.intaa   )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(UpperCamelCase__   ):
									UpperCAmelCase__					:   Dict       				=       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									UpperCAmelCase__					:   Optional[int]       				=       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									UpperCAmelCase__					:   Union[str, Any]       				=       with_conta
									UpperCAmelCase__					:   List[str]       				=       with_conta
									UpperCAmelCase__					:   Any       				=       len(UpperCamelCase__   ) - 1
									UpperCAmelCase__					:   str       				=       len(UpperCamelCase__   ) - 1
									UpperCAmelCase__					:   List[Any]       				=       with_conta
									UpperCAmelCase__					:   Optional[Any]       				=       with_conta
									UpperCAmelCase__					:   Optional[Any]       				=       mc_label
						UpperCAmelCase__					:   Dict       				=       (input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(UpperCamelCase__   ) for t in all_inputs   )   )
			return tensor_datasets







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   str       				=       argparse.ArgumentParser()
			parser.add_argument("""--model_name"""	, type=UpperCamelCase__	, 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=UpperCamelCase__	, type=UpperCamelCase__	, required=UpperCamelCase__	, help="""The output directory where the model predictions and checkpoints will be written."""	, )
			parser.add_argument("""--train_dataset"""	, type=UpperCamelCase__	, default=""""""   )
			parser.add_argument("""--eval_dataset"""	, type=UpperCamelCase__	, default=""""""   )
			parser.add_argument("""--seed"""	, type=UpperCamelCase__	, default=4_2   )
			parser.add_argument("""--num_train_epochs"""	, type=UpperCamelCase__	, default=3   )
			parser.add_argument("""--train_batch_size"""	, type=UpperCamelCase__	, default=8   )
			parser.add_argument("""--eval_batch_size"""	, type=UpperCamelCase__	, default=1_6   )
			parser.add_argument("""--adam_epsilon"""	, default=1e-8	, type=UpperCamelCase__	, help="""Epsilon for Adam optimizer."""   )
			parser.add_argument("""--max_grad_norm"""	, type=UpperCamelCase__	, default=1   )
			parser.add_argument(
			    """--max_steps"""	, default=-1	, type=UpperCamelCase__	, help=(
			        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
			    )	, )
			parser.add_argument(
			    """--gradient_accumulation_steps"""	, type=UpperCamelCase__	, default=1	, help="""Number of updates steps to accumulate before                        performing a backward/update pass."""	, )
			parser.add_argument("""--learning_rate"""	, type=UpperCamelCase__	, default=6.25e-5   )
			parser.add_argument("""--warmup_steps"""	, default=0	, type=UpperCamelCase__	, help="""Linear warmup over warmup_steps."""   )
			parser.add_argument("""--lr_schedule"""	, type=UpperCamelCase__	, default="""warmup_linear"""   )
			parser.add_argument("""--weight_decay"""	, type=UpperCamelCase__	, default=0.01   )
			parser.add_argument("""--lm_coef"""	, type=UpperCamelCase__	, default=0.9   )
			parser.add_argument("""--n_valid"""	, type=UpperCamelCase__	, default=3_7_4   )

			parser.add_argument("""--server_ip"""	, type=UpperCamelCase__	, default=""""""	, help="""Can be used for distant debugging."""   )
			parser.add_argument("""--server_port"""	, type=UpperCamelCase__	, default=""""""	, help="""Can be used for distant debugging."""   )
			UpperCAmelCase__					:   List[Any]       				=       parser.parse_args()
			print(UpperCamelCase__   )

			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=UpperCamelCase__   )
						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__					:   Any       				=       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )
			UpperCAmelCase__					:   List[str]       				=       torch.cuda.device_count()
			logger.info("""device: {}, n_gpu {}""".format(UpperCamelCase__	, UpperCamelCase__   )   )

			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__					:   List[str]       				=       ["""_start_""", """_delimiter_""", """_classify_"""]
			UpperCAmelCase__					:   Any       				=       OpenAIGPTTokenizer.from_pretrained(args.model_name   )
			tokenizer.add_tokens(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       tokenizer.convert_tokens_to_ids(UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
			model.resize_token_embeddings(len(UpperCamelCase__   )   )
			model.to(UpperCamelCase__   )

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

			logger.info("""Encoding dataset..."""   )
			UpperCAmelCase__					:   Tuple       				=       load_rocstories_dataset(args.train_dataset   )
			UpperCAmelCase__					:   int       				=       load_rocstories_dataset(args.eval_dataset   )
			UpperCAmelCase__					:   str       				=       (train_dataset, eval_dataset)
			UpperCAmelCase__					:   Optional[int]       				=       tokenize_and_encode(UpperCamelCase__   )

			# Compute the max input length for the Transformer
			UpperCAmelCase__					:   Optional[Any]       				=       model.config.n_positions // 2 - 2
			UpperCAmelCase__					:   Optional[int]       				=       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__					:   Union[str, Any]       				=       min(UpperCamelCase__	, model.config.n_positions   )  # Max size of input for the pre-trained model

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

			UpperCAmelCase__					:   Dict       				=       TensorDataset(*UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       RandomSampler(UpperCamelCase__   )
			UpperCAmelCase__					:   Dict       				=       DataLoader(UpperCamelCase__	, sampler=UpperCamelCase__	, batch_size=args.train_batch_size   )

			UpperCAmelCase__					:   int       				=       TensorDataset(*UpperCamelCase__   )
			UpperCAmelCase__					:   List[Any]       				=       SequentialSampler(UpperCamelCase__   )
			UpperCAmelCase__					:   Any       				=       DataLoader(UpperCamelCase__	, sampler=UpperCamelCase__	, batch_size=args.eval_batch_size   )

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									UpperCAmelCase__					:   Dict       				=       args.max_steps
									UpperCAmelCase__					:   Dict       				=       args.max_steps // (len(UpperCamelCase__   ) // args.gradient_accumulation_steps) + 1
						else:
									UpperCAmelCase__					:   Optional[Any]       				=       len(UpperCamelCase__   ) // args.gradient_accumulation_steps * args.num_train_epochs

						UpperCAmelCase__					:   Tuple       				=       list(model.named_parameters()   )
						UpperCAmelCase__					:   Dict       				=       ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
						UpperCAmelCase__					:   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__					:   Dict       				=       AdamW(UpperCamelCase__	, lr=args.learning_rate	, eps=args.adam_epsilon   )
						UpperCAmelCase__					:   Any       				=       get_linear_schedule_with_warmup(
						    UpperCamelCase__	, num_warmup_steps=args.warmup_steps	, num_training_steps=UpperCamelCase__   )

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

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						UpperCAmelCase__					:   Optional[Any]       				=       model.module if hasattr(UpperCamelCase__	, """module"""   ) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						UpperCAmelCase__					:   Any       				=       os.path.join(args.output_dir	, UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       os.path.join(args.output_dir	, UpperCamelCase__   )

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

						# Load a trained model and vocabulary that you have fine-tuned
						UpperCAmelCase__					:   Optional[int]       				=       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
						UpperCAmelCase__					:   Any       				=       OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
						model.to(UpperCamelCase__   )

			if args.do_eval:
						model.eval()
						UpperCAmelCase__					:   Optional[Any]       				=       0, 0
						UpperCAmelCase__					:   str       				=       0, 0
						for batch in tqdm(UpperCamelCase__	, desc="""Evaluating"""   ):
									UpperCAmelCase__					:   Optional[int]       				=       tuple(t.to(UpperCamelCase__   ) for t in batch   )
									UpperCAmelCase__					:   Dict       				=       batch
									with torch.no_grad():
												UpperCAmelCase__					:   Any       				=       model(
												    UpperCamelCase__	, mc_token_ids=UpperCamelCase__	, lm_labels=UpperCamelCase__	, mc_labels=UpperCamelCase__   )

									UpperCAmelCase__					:   Union[str, Any]       				=       mc_logits.detach().cpu().numpy()
									UpperCAmelCase__					:   int       				=       mc_labels.to("""cpu"""   ).numpy()
									UpperCAmelCase__					:   str       				=       accuracy(UpperCamelCase__	, UpperCamelCase__   )

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

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

						UpperCAmelCase__					:   Optional[int]       				=       eval_loss / nb_eval_steps
						UpperCAmelCase__					:   Union[str, Any]       				=       eval_accuracy / nb_eval_examples
						UpperCAmelCase__					:   Any       				=       tr_loss / nb_tr_steps if args.do_train else None
						UpperCAmelCase__					:   Optional[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(UpperCamelCase__	, """w"""   ) as writer:
									logger.info("""***** Eval results *****"""   )
									for key in sorted(result.keys()   ):
												logger.info("""  %s = %s"""	, UpperCamelCase__	, str(result[key]   )   )
												writer.write("""%s = %s\n""" % (key, str(result[key]   ))   )


if __name__ == "__main__":
		main()



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)