

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase      				=     logging.get_logger(__name__)

lowerCamelCase      				=     '''▁'''

lowerCamelCase      				=     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

lowerCamelCase      				=     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

lowerCamelCase      				=     {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

lowerCamelCase      				=     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

lowerCamelCase      				=     {'''mustc''': MUSTC_LANGS}





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =VOCAB_FILES_NAMES
							__UpperCAmelCase       =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase       =MAX_MODEL_INPUT_SIZES
							__UpperCAmelCase       =["""input_ids""", """attention_mask"""]

							__UpperCAmelCase       =[]




							def __init__( self					,						_A					,						_A					,						_A="<s>"					,						_A="</s>"					,						_A="<pad>"					,						_A="<unk>"					,						_A=False					,						_A=False					,						_A=None					,						_A=None					,						_A = None					,						**_A					,						):
												__lowerCAmelCase					=							{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=_A					,						eos_token=_A					,						unk_token=_A					,						pad_token=_A					,						do_upper_case=_A					,						do_lower_case=_A					,						tgt_lang=_A					,						lang_codes=_A					,						sp_model_kwargs=self.sp_model_kwargs					,						**_A					,						)
												__lowerCAmelCase					=							do_upper_case
												__lowerCAmelCase					=							do_lower_case

												__lowerCAmelCase					=							load_json(_A							)
												__lowerCAmelCase					=							{v: k for k, v in self.encoder.items()}
												__lowerCAmelCase					=							spm_file
												__lowerCAmelCase					=							load_spm(_A					,						self.sp_model_kwargs							)

												if lang_codes is not None:
																	__lowerCAmelCase					=							lang_codes
																	__lowerCAmelCase					=							LANGUAGES[lang_codes]
																	__lowerCAmelCase					=							[F"""<lang:{lang}>""" for lang in self.langs]
																	__lowerCAmelCase					=							{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""							) for lang in self.langs}

																	__lowerCAmelCase					=							self.lang_tokens
																	__lowerCAmelCase					=							tgt_lang if tgt_lang is not None else self.langs[0]

																	self.set_tgt_lang_special_tokens(self._tgt_lang							)
												else:
																	__lowerCAmelCase					=							{}




							@property
							def        A__  ( self							):
												return len(self.encoder							)




							@property
							def        A__  ( self							):
												return self._tgt_lang




							@tgt_lang.setter
							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							new_tgt_lang
												self.set_tgt_lang_special_tokens(_A							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							self.lang_code_to_id[tgt_lang]
												__lowerCAmelCase					=							[lang_code_id]




							def        A__  ( self					,						_A							):
												return self.sp_model.encode(_A					,						out_type=_A							)




							def        A__  ( self					,						_A							):
												return self.encoder.get(_A					,						self.encoder[self.unk_token]							)




							def        A__  ( self					,						_A							):
												return self.decoder.get(_A					,						self.unk_token							)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							''
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						__lowerCAmelCase					=							self.sp_model.decode(_A							)
																						out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																						__lowerCAmelCase					=							[]
																	else:
																						current_sub_tokens.append(_A							)
												__lowerCAmelCase					=							self.sp_model.decode(_A							)
												out_string += decoded.upper() if self.do_upper_case else decoded
												return out_string.strip()




							def        A__  ( self					,						_A					,						_A=None							):
												if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




							def        A__  ( self					,						_A					,						_A = None					,						_A = False							):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_A					,						token_ids_a=_A					,						already_has_special_tokens=_A							)

												__lowerCAmelCase					=							[1] * len(self.prefix_tokens							)
												__lowerCAmelCase					=							[1]
												if token_ids_a is None:
																	return prefix_ones + ([0] * len(_A							)) + suffix_ones
												return prefix_ones + ([0] * len(_A							)) + ([0] * len(_A							)) + suffix_ones




							def        A__  ( self							):
												__lowerCAmelCase					=							self.encoder.copy()
												vocab.update(self.added_tokens_encoder							)
												return vocab




							def __getstate__( self							):
												__lowerCAmelCase					=							self.__dict__.copy()
												__lowerCAmelCase					=							None
												return state




							def __setstate__( self					,						_A							):
												__lowerCAmelCase					=							d

												# for backward compatibility
												if not hasattr(self					,						'sp_model_kwargs'							):
																	__lowerCAmelCase					=							{}

												__lowerCAmelCase					=							load_spm(self.spm_file					,						self.sp_model_kwargs							)




							def        A__  ( self					,						_A					,						_A = None							):
												__lowerCAmelCase					=							Path(_A							)
												assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
												__lowerCAmelCase					=							save_dir / (
												    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
												)
												__lowerCAmelCase					=							save_dir / (
												    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
												)

												save_json(self.encoder					,						_A							)

												if os.path.abspath(self.spm_file							) != os.path.abspath(_A							) and os.path.isfile(self.spm_file							):
																	copyfile(self.spm_file					,						_A							)
												elif not os.path.isfile(self.spm_file							):
																	with open(_A					,						'wb'							) as fi:
																						__lowerCAmelCase					=							self.sp_model.serialized_model_proto()
																						fi.write(_A							)

												return (str(_A							), str(_A							))


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							sentencepiece.SentencePieceProcessor(**UpperCAmelCase__      )
					spm.Load(str(UpperCAmelCase__      )      )
					return spm


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					'r'      ) as f:
										return json.load(UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					'w'      ) as f:
										json.dump(UpperCAmelCase__		,					UpperCAmelCase__		,					indent=2      )









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      				=     logging.getLogger(__name__)


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							np.argmax(UpperCAmelCase__		,					axis=1      )
					return np.sum(outputs == labels      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					encoding='utf_8'      ) as f:
										__lowerCAmelCase					=							csv.reader(UpperCAmelCase__      )
										__lowerCAmelCase					=							[]
										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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]
					for dataset in encoded_datasets:
										__lowerCAmelCase					=							len(UpperCAmelCase__      )
										__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=-100		,					dtype=np.intaa      )
										__lowerCAmelCase					=							np.zeros((n_batch,)		,					dtype=np.intaa      )
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(UpperCAmelCase__      ):
															__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(UpperCAmelCase__      ) - 1
															__lowerCAmelCase					=							len(UpperCAmelCase__      ) - 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(UpperCAmelCase__      ) for t in all_inputs      )      )
					return tensor_datasets


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							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=42      )
					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=16      )
					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=374      )

					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.'      )
					__lowerCAmelCase					=							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      )

					__lowerCAmelCase					=							torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							['_start_', '_delimiter_', '_classify_']
					__lowerCAmelCase					=							OpenAIGPTTokenizer.from_pretrained(args.model_name      )
					tokenizer.add_tokens(UpperCAmelCase__      )
					__lowerCAmelCase					=							tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )
					__lowerCAmelCase					=							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...'      )
					__lowerCAmelCase					=							load_rocstories_dataset(args.train_dataset      )
					__lowerCAmelCase					=							load_rocstories_dataset(args.eval_dataset      )
					__lowerCAmelCase					=							(train_dataset, eval_dataset)
					__lowerCAmelCase					=							tokenize_and_encode(UpperCAmelCase__      )

					# 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(UpperCAmelCase__		,					model.config.n_positions      )  # Max size of input for the pre-trained model

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

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

					__lowerCAmelCase					=							TensorDataset(*UpperCAmelCase__      )
					__lowerCAmelCase					=							SequentialSampler(UpperCAmelCase__      )
					__lowerCAmelCase					=							DataLoader(UpperCAmelCase__		,					sampler=UpperCAmelCase__		,					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(UpperCAmelCase__      ) // args.gradient_accumulation_steps) + 1
										else:
															__lowerCAmelCase					=							len(UpperCAmelCase__      ) // 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(UpperCAmelCase__		,					lr=args.learning_rate		,					eps=args.adam_epsilon      )
										__lowerCAmelCase					=							get_linear_schedule_with_warmup(
										    UpperCAmelCase__		,					num_warmup_steps=args.warmup_steps		,					num_training_steps=UpperCAmelCase__      )

					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(UpperCAmelCase__		,					desc='Training'      )
															for step, batch in enumerate(UpperCAmelCase__      ):
																				__lowerCAmelCase					=							tuple(t.to(UpperCAmelCase__      ) for t in batch      )
																				__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							batch
																				__lowerCAmelCase					=							model(UpperCAmelCase__		,					mc_token_ids=UpperCAmelCase__		,					lm_labels=UpperCAmelCase__		,					mc_labels=UpperCAmelCase__      )
																				__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(UpperCAmelCase__		,					scheduler.get_lr()[0]      )

    # Save a trained model
					if args.do_train:
										# Save a trained model, configuration and tokenizer
										__lowerCAmelCase					=							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`
										__lowerCAmelCase					=							os.path.join(args.output_dir		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							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
										__lowerCAmelCase					=							OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir      )
										__lowerCAmelCase					=							OpenAIGPTTokenizer.from_pretrained(args.output_dir      )
										model.to(UpperCAmelCase__      )

					if args.do_eval:
										model.eval()
										__lowerCAmelCase,	__lowerCAmelCase					=							0, 0
										__lowerCAmelCase,	__lowerCAmelCase					=							0, 0
										for batch in tqdm(UpperCAmelCase__		,					desc='Evaluating'      ):
															__lowerCAmelCase					=							tuple(t.to(UpperCAmelCase__      ) for t in batch      )
															__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							batch
															with torch.no_grad():
																				__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							model(
																				    UpperCAmelCase__		,					mc_token_ids=UpperCAmelCase__		,					lm_labels=UpperCAmelCase__		,					mc_labels=UpperCAmelCase__      )

															__lowerCAmelCase					=							mc_logits.detach().cpu().numpy()
															__lowerCAmelCase					=							mc_labels.to('cpu'      ).numpy()
															__lowerCAmelCase					=							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

										__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(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()






