





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def    __lowerCamelCase					(							__a   :str    )		->				None:






		"""simple docstring"""

		A__      ,			A__        =   analyze_text(__a    )
		A__        =   list(""" """ + ascii_lowercase    )
		# what is our total sum of probabilities.
		A__        =   sum(single_char_strings.values()    )

		# one length string
		A__        =   0
		# for each alpha we go in our dict and if it is in it we calculate entropy
		for ch in my_alphas:
				if ch in single_char_strings:
						A__        =   single_char_strings[ch]
						A__        =   my_str / all_sum
						my_fir_sum += prob * math.loga(__a    )  # entropy formula.

    # print entropy
		print(F'{round(-1 * my_fir_sum    ):.1f}'    )

		# two len string
		A__        =   sum(two_char_strings.values()    )
		A__        =   0
		# for each alpha (two in size) calculate entropy.
		for cha in my_alphas:
				for cha in my_alphas:
						A__        =   cha + cha
						if sequence in two_char_strings:
								A__        =   two_char_strings[sequence]
								A__        =   int(__a    ) / all_sum
								my_sec_sum += prob * math.loga(__a    )

    # print second entropy
		print(F'{round(-1 * my_sec_sum    ):.1f}'    )

		# print the difference between them
		print(F'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)    ):.1f}'    )







def    __lowerCamelCase					(							__a   :str    )		->				tuple[dict, dict]:






		"""simple docstring"""

		A__        =   Counter()  # type: ignore
		A__        =   Counter()  # type: ignore
		single_char_strings[text[-1]] += 1

		# first case when we have space at start.
		two_char_strings[" " + text[0]] += 1
		for i in range(0    ,     len(__a    ) - 1    ):
				single_char_strings[text[i]] += 1
				two_char_strings[text[i : i + 2]] += 1
		return single_char_strings, two_char_strings







def    __lowerCamelCase					(							)		->				List[str]:






		"""simple docstring"""

		import doctest

		doctest.testmod()
		# text = (
		#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
		#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
		#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
		#     "beloved evident through no service elderly is. Blind there if every no so "
		#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
		#     "of message cottage windows do besides against uncivil.  Delightful "
		#     "unreserved impossible few estimating men favourable see entreaties. She "
		#     "propriety immediate was improving. He or entrance humoured likewise "
		#     "moderate. Much nor game son say feel. Fat make met can must form into "
		#     "gate. Me we offending prevailed discovery. "
		# )

		# calculate_prob(text)


if __name__ == "__main__":
					main()










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    :					Tuple =      logging.getLogger(__name__)







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[str]    )		->				Tuple:






		"""simple docstring"""

		A__        =   np.argmax(__a    ,     axis=1    )
		return np.sum(outputs == labels    )







def    __lowerCamelCase					(							__a   :Tuple    )		->				Dict:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[Any]    ,     __a   :Dict    ,     __a   :Optional[Any]    ,     __a   :Optional[Any]    ,     __a   :int    )		->				Union[str, Any]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

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

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

		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=__a    )
				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    )

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

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

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

		logger.info("""Encoding dataset..."""    )
		A__        =   load_rocstories_dataset(args.train_dataset    )
		A__        =   load_rocstories_dataset(args.eval_dataset    )
		A__        =   (train_dataset, eval_dataset)
		A__        =   tokenize_and_encode(__a    )

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

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

		A__        =   TensorDataset(*__a    )
		A__        =   RandomSampler(__a    )
		A__        =   DataLoader(__a    ,     sampler=__a    ,     batch_size=args.train_batch_size    )

		A__        =   TensorDataset(*__a    )
		A__        =   SequentialSampler(__a    )
		A__        =   DataLoader(__a    ,     sampler=__a    ,     batch_size=args.eval_batch_size    )

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

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

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

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

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

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

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

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

						A__        =   mc_logits.detach().cpu().numpy()
						A__        =   mc_labels.to("""cpu"""    ).numpy()
						A__        =   accuracy(__a    ,     __a    )

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

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

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

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


if __name__ == "__main__":
					main()



