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
)
__lowerCamelCase					:	List[str]       =  logging.getLogger(__name__)




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   int	):
		"""simple docstring"""







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




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		with open(lowerCAmelCase_   , encoding="utf_8"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    csv.reader(lowerCAmelCase_	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    []
				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       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    []
		for dataset in encoded_datasets:
				SCREAMING_SNAKE_CASE_  :		Any				      =    len(lowerCAmelCase_	)
				SCREAMING_SNAKE_CASE_  :		str				      =    np.zeros((n_batch, 2, input_len)   , dtype=np.intaa	)
				SCREAMING_SNAKE_CASE_  :		Dict				      =    np.zeros((n_batch, 2)   , dtype=np.intaa	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.full((n_batch, 2, input_len)   , fill_value=-1_0_0   , dtype=np.intaa	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    np.zeros((n_batch,)   , dtype=np.intaa	)
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(lowerCAmelCase_	):
						SCREAMING_SNAKE_CASE_  :		int				      =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    with_conta
						SCREAMING_SNAKE_CASE_  :		Dict				      =    with_conta
						SCREAMING_SNAKE_CASE_  :		int				      =    len(lowerCAmelCase_	) - 1
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    len(lowerCAmelCase_	) - 1
						SCREAMING_SNAKE_CASE_  :		str				      =    with_conta
						SCREAMING_SNAKE_CASE_  :		Any				      =    with_conta
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    mc_label
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    (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       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    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=6.2_5E-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=0.01	)
		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."	)
		SCREAMING_SNAKE_CASE_  :		str				      =    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	)

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.device("cuda" if torch.cuda.is_available() else "cpu"	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    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
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    ['_start_', '_delimiter_', '_classify_']
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    OpenAIGPTTokenizer.from_pretrained(args.model_name	)
		tokenizer.add_tokens(lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tokenizer.convert_tokens_to_ids(lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    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							:   int	):
				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..."	)
		SCREAMING_SNAKE_CASE_  :		str				      =    load_rocstories_dataset(args.train_dataset	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    load_rocstories_dataset(args.eval_dataset	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (train_dataset, eval_dataset)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    tokenize_and_encode(lowerCAmelCase_	)

		# Compute the max input length for the Transformer
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model.config.n_positions // 2 - 2
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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	)
		SCREAMING_SNAKE_CASE_  :		str				      =    min(lowerCAmelCase_   , model.config.n_positions	)  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		SCREAMING_SNAKE_CASE_  :		Any				      =    pre_process_datasets(lowerCAmelCase_   , lowerCAmelCase_   , lowerCAmelCase_   , *lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tensor_datasets[0], tensor_datasets[1]

		SCREAMING_SNAKE_CASE_  :		str				      =    TensorDataset(*lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    RandomSampler(lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    DataLoader(lowerCAmelCase_   , sampler=lowerCAmelCase_   , batch_size=args.train_batch_size	)

		SCREAMING_SNAKE_CASE_  :		Dict				      =    TensorDataset(*lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    SequentialSampler(lowerCAmelCase_	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    DataLoader(lowerCAmelCase_   , sampler=lowerCAmelCase_   , batch_size=args.eval_batch_size	)

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

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

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

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

						SCREAMING_SNAKE_CASE_  :		Tuple				      =    mc_logits.detach().cpu().numpy()
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    mc_labels.to("cpu"	).numpy()
						SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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

				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    eval_loss / nb_eval_steps
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    eval_accuracy / nb_eval_examples
				SCREAMING_SNAKE_CASE_  :		int				      =    tr_loss / nb_tr_steps if args.do_train else None
				SCREAMING_SNAKE_CASE_  :		Dict				      =    {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

				SCREAMING_SNAKE_CASE_  :		str				      =    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'''

__lowerCAmelCase									=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      list[str]:
		_a     :      List[Any]        =							set()
		# keep track of all the paths to be checked
		_a     :      Any        =							[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				_a     :      Tuple        =							queue.pop(0  )
				# get the last node from the path
				_a     :      Tuple        =							path[-1]
				if node not in explored:
						_a     :      Optional[Any]        =							graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								_a     :      Any        =							list(lowerCAmelCase_  )
								new_path.append(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(lowerCAmelCase_  )

    # in case there's no path between the 2 nodes
		return []



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		_a     :      Optional[int]        =							[start]
		_a     :      Dict        =							set(lowerCAmelCase_  )
		# Keep tab on distances from `start` node.
		_a     :      Dict        =							{start: 0, target: -1}
		while queue:
				_a     :      List[str]        =							queue.pop(0  )
				if node == target:
						_a     :      Any        =							(
						    dist[node] if dist[target] == -1 else min(dist[target]						,			dist[node]  )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(lowerCAmelCase_  )
								queue.append(lowerCAmelCase_  )
								_a     :      Any        =							dist[node] + 1
		return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




