






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__A      =				logging.getLogger(__name__)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"summarization"
							A_						  =					["loss"]
							A_						  =					ROUGE_KEYS
							A_						  =					"rouge2"

							def __init__(  self:   List[str]		,     __A:   Any		,     **__A:   Tuple   )    ->				Optional[Any]:
												if hparams.sortish_sampler and hparams.gpus > 1:
																	_A								= False
												elif hparams.max_tokens_per_batch is not None:
																	if hparams.gpus > 1:
																						raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''   )
																	if hparams.sortish_sampler:
																						raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''   )

												super().__init__(__A		,     num_labels=__A		,     mode=self.mode		,     **__A   )
												use_task_specific_params(self.model		,     '''summarization'''   )
												save_git_info(self.hparams.output_dir   )
												_A								= Path(self.output_dir   ) / '''metrics.json'''
												_A								= Path(self.output_dir   ) / '''hparams.pkl'''
												pickle_save(self.hparams		,     self.hparams_save_path   )
												_A								= 0
												_A								= defaultdict(__A   )
												_A								= self.config.model_type
												_A								= self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

												_A								= {
												    "data_dir": self.hparams.data_dir,
												    "max_source_length": self.hparams.max_source_length,
												    "prefix": self.model.config.prefix or "",
												}
												_A								= {
												    '''train''': self.hparams.n_train,
												    '''val''': self.hparams.n_val,
												    '''test''': self.hparams.n_test,
												}
												_A								= {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

												_A								= {
												    '''train''': self.hparams.max_target_length,
												    '''val''': self.hparams.val_max_target_length,
												    '''test''': self.hparams.test_max_target_length,
												}
												assert self.target_lens["train"] <= self.target_lens["val"], f"""target_lens: {self.target_lens}"""
												assert self.target_lens["train"] <= self.target_lens["test"], f"""target_lens: {self.target_lens}"""
												if self.hparams.freeze_embeds:
																	freeze_embeds(self.model   )
												if self.hparams.freeze_encoder:
																	freeze_params(self.model.get_encoder()   )
																	assert_all_frozen(self.model.get_encoder()   )

												_A								= get_git_info()['''repo_sha''']
												_A								= hparams.num_workers
												_A								= None  # default to config
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,     __A   ):
																	_A								= self.tokenizer.lang_code_to_id[hparams.tgt_lang]
																	_A								= self.decoder_start_token_id
												_A								= (
												    SeqaSeqDataset if hasattr(self.tokenizer		,     '''prepare_seq2seq_batch'''   ) else LegacySeqaSeqDataset
												)
												_A								= False
												_A								= self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
												if self.hparams.eval_max_gen_length is not None:
																	_A								= self.hparams.eval_max_gen_length
												else:
																	_A								= self.model.config.max_length
												_A								= self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

							def       __A (  self:   List[Any]		,     __A:   Dict[str, torch.Tensor]   )    ->				Dict[str, List[str]]:
												_A								= {
												    k: self.tokenizer.batch_decode(v.tolist()   ) if '''mask''' not in k else v.shape for k, v in batch.items()
												}
												save_json(__A		,     Path(self.output_dir   ) / '''text_batch.json'''   )
												save_json({k: v.tolist() for k, v in batch.items()}		,     Path(self.output_dir   ) / '''tok_batch.json'''   )

												_A								= True
												return readable_batch

							def       __A (  self:   Optional[Any]		,     __A:   Any		,     **__A:   List[str]   )    ->				Any:
												return self.model(__A		,     **__A   )

							def       __A (  self:   Tuple		,     __A:   List[int]   )    ->				Union[str, Any]:
												_A								= self.tokenizer.batch_decode(
												    __A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )
												return lmap(str.strip		,     __A   )

							def       __A (  self:   List[str]		,     __A:   dict   )    ->				Tuple:
												_A								= self.tokenizer.pad_token_id
												_A							,_A								= batch['''input_ids'''], batch['''attention_mask''']
												_A								= batch['''labels''']
												if isinstance(self.model		,     __A   ):
																	_A								= self.model._shift_right(__A   )
												else:
																	_A								= shift_tokens_right(__A		,     __A   )
												if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
																	_A								= decoder_input_ids
																	self.save_readable_batch(__A   )

												_A								= self(__A		,     attention_mask=__A		,     decoder_input_ids=__A		,     use_cache=__A   )
												_A								= outputs['''logits''']
												if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	_A								= nn.CrossEntropyLoss(ignore_index=__A   )

																	assert lm_logits.shape[-1] == self.vocab_size
																	_A								= ce_loss_fct(lm_logits.view(-1		,     lm_logits.shape[-1]   )		,     tgt_ids.view(-1   )   )
												else:
																	_A								= nn.functional.log_softmax(__A		,     dim=-1   )
																	_A							,_A								= label_smoothed_nll_loss(
																	    __A		,     __A		,     self.hparams.label_smoothing		,     ignore_index=__A   )
												return (loss,)

							@property
							def       __A (  self:   List[Any]   )    ->				int:
												return self.tokenizer.pad_token_id

							def       __A (  self:   List[Any]		,     __A:   Union[str, Any]		,     __A:   List[str]   )    ->				Dict:
												_A								= self._step(__A   )

												_A								= dict(zip(self.loss_names		,     __A   )   )
												# tokens per batch
												_A								= batch['''input_ids'''].ne(self.pad   ).sum() + batch['''labels'''].ne(self.pad   ).sum()
												_A								= batch['''input_ids'''].shape[0]
												_A								= batch['''input_ids'''].eq(self.pad   ).sum()
												_A								= batch['''input_ids'''].eq(self.pad   ).float().mean()
												# TODO(SS): make a wandb summary metric for this
												return {"loss": loss_tensors[0], "log": logs}

							def       __A (  self:   Any		,     __A:   List[Any]		,     __A:   Dict   )    ->				Dict:
												return self._generative_step(__A   )

							def       __A (  self:   Optional[Any]		,     __A:   Union[str, Any]		,     __A:   Tuple="val"   )    ->				Dict:
												self.step_count += 1
												_A								= {k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
												_A								= losses['''loss''']
												_A								= {
												    k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
												}
												_A								= (
												    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
												)
												_A								= torch.tensor(__A   ).type_as(__A   )
												generative_metrics.update({k: v.item() for k, v in losses.items()}   )
												losses.update(__A   )
												_A								= {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
												_A								= self.step_count
												self.metrics[prefix].append(__A   )  # callback writes this to self.metrics_save_path
												_A								= flatten_list([x['''preds'''] for x in outputs]   )
												return {
												    "log": all_metrics,
												    "preds": preds,
												    f"""{prefix}_loss""": loss,
												    f"""{prefix}_{self.val_metric}""": metric_tensor,
												}

							def       __A (  self:   List[Any]		,     __A:   Tuple		,     __A:   Optional[int]   )    ->				Dict:
												return calculate_rouge(__A		,     __A   )

							def       __A (  self:   Optional[int]		,     __A:   dict   )    ->				dict:
												_A								= time.time()

												# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
												_A								= self.model.generate(
												    batch['''input_ids''']		,     attention_mask=batch['''attention_mask''']		,     use_cache=__A		,     decoder_start_token_id=self.decoder_start_token_id		,     num_beams=self.eval_beams		,     max_length=self.eval_max_length		,     )
												_A								= (time.time() - ta) / batch['''input_ids'''].shape[0]
												_A								= self.ids_to_clean_text(__A   )
												_A								= self.ids_to_clean_text(batch['''labels''']   )
												_A								= self._step(__A   )
												_A								= dict(zip(self.loss_names		,     __A   )   )
												_A								= self.calc_generative_metrics(__A		,     __A   )
												_A								= np.mean(lmap(__A		,     __A   )   )
												base_metrics.update(gen_time=__A		,     gen_len=__A		,     preds=__A		,     target=__A		,     **__A   )
												return base_metrics

							def       __A (  self:   Dict		,     __A:   int		,     __A:   str   )    ->				List[Any]:
												return self._generative_step(__A   )

							def       __A (  self:   int		,     __A:   int   )    ->				List[str]:
												return self.validation_epoch_end(__A		,     prefix='''test'''   )

							def       __A (  self:   Optional[Any]		,     __A:   int   )    ->				SeqaSeqDataset:
												_A								= self.n_obs[type_path]
												_A								= self.target_lens[type_path]
												_A								= self.dataset_class(
												    self.tokenizer		,     type_path=__A		,     n_obs=__A		,     max_target_length=__A		,     **self.dataset_kwargs		,     )
												return dataset

							def       __A (  self:   Optional[int]		,     __A:   str		,     __A:   int		,     __A:   bool = False   )    ->				DataLoader:
												_A								= self.get_dataset(__A   )

												if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
																	_A								= dataset.make_sortish_sampler(__A		,     distributed=self.hparams.gpus > 1   )
																	return DataLoader(
																	    __A		,     batch_size=__A		,     collate_fn=dataset.collate_fn		,     shuffle=__A		,     num_workers=self.num_workers		,     sampler=__A		,     )

												elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
																	_A								= dataset.make_dynamic_sampler(
																	    self.hparams.max_tokens_per_batch		,     distributed=self.hparams.gpus > 1   )
																	return DataLoader(
																	    __A		,     batch_sampler=__A		,     collate_fn=dataset.collate_fn		,     num_workers=self.num_workers		,     )
												else:
																	return DataLoader(
																	    __A		,     batch_size=__A		,     collate_fn=dataset.collate_fn		,     shuffle=__A		,     num_workers=self.num_workers		,     sampler=__A		,     )

							def       __A (  self:   Tuple   )    ->				DataLoader:
												_A								= self.get_dataloader('''train'''		,     batch_size=self.hparams.train_batch_size		,     shuffle=__A   )
												return dataloader

							def       __A (  self:   Optional[Any]   )    ->				DataLoader:
												return self.get_dataloader('''val'''		,     batch_size=self.hparams.eval_batch_size   )

							def       __A (  self:   str   )    ->				DataLoader:
												return self.get_dataloader('''test'''		,     batch_size=self.hparams.eval_batch_size   )



							@staticmethod
							def       __A (  __A:   Dict		,     __A:   Tuple   )    ->				List[str]:
												BaseTransformer.add_model_specific_args(__A		,     __A   )
												add_generic_args(__A		,     __A   )
												parser.add_argument(
												    '''--max_source_length'''		,     default=10_24		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )
												parser.add_argument(
												    '''--max_target_length'''		,     default=56		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )
												parser.add_argument(
												    '''--val_max_target_length'''		,     default=1_42		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )
												parser.add_argument(
												    '''--test_max_target_length'''		,     default=1_42		,     type=__A		,     help=(
												        '''The maximum total input sequence length after tokenization. Sequences longer '''
												        '''than this will be truncated, sequences shorter will be padded.'''
												    )		,     )
												parser.add_argument('''--freeze_encoder'''		,     action='''store_true'''   )
												parser.add_argument('''--freeze_embeds'''		,     action='''store_true'''   )
												parser.add_argument('''--sortish_sampler'''		,     action='''store_true'''		,     default=__A   )
												parser.add_argument('''--overwrite_output_dir'''		,     action='''store_true'''		,     default=__A   )
												parser.add_argument('''--max_tokens_per_batch'''		,     type=__A		,     default=__A   )
												parser.add_argument('''--logger_name'''		,     type=__A		,     choices=['''default''', '''wandb''', '''wandb_shared''']		,     default='''default'''   )
												parser.add_argument('''--n_train'''		,     type=__A		,     default=-1		,     required=__A		,     help='''# examples. -1 means use all.'''   )
												parser.add_argument('''--n_val'''		,     type=__A		,     default=5_00		,     required=__A		,     help='''# examples. -1 means use all.'''   )
												parser.add_argument('''--n_test'''		,     type=__A		,     default=-1		,     required=__A		,     help='''# examples. -1 means use all.'''   )
												parser.add_argument(
												    '''--task'''		,     type=__A		,     default='''summarization'''		,     required=__A		,     help='''# examples. -1 means use all.'''   )
												parser.add_argument('''--label_smoothing'''		,     type=__A		,     default=0.0		,     required=__A   )
												parser.add_argument('''--src_lang'''		,     type=__A		,     default=''''''		,     required=__A   )
												parser.add_argument('''--tgt_lang'''		,     type=__A		,     default=''''''		,     required=__A   )
												parser.add_argument('''--eval_beams'''		,     type=__A		,     default=__A		,     required=__A   )
												parser.add_argument(
												    '''--val_metric'''		,     type=__A		,     default=__A		,     required=__A		,     choices=['''bleu''', '''rouge2''', '''loss''', None]   )
												parser.add_argument('''--eval_max_gen_length'''		,     type=__A		,     default=__A		,     help='''never generate more than n tokens'''   )
												parser.add_argument('''--save_top_k'''		,     type=__A		,     default=1		,     required=__A		,     help='''How many checkpoints to save'''   )
												parser.add_argument(
												    '''--early_stopping_patience'''		,     type=__A		,     default=-1		,     required=__A		,     help=(
												        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
												        ''' val_check_interval will effect it.'''
												    )		,     )
												return parser





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"translation"
							A_						  =					["loss"]
							A_						  =					["bleu"]
							A_						  =					"bleu"

							def __init__(  self:   List[Any]		,     __A:   Tuple		,     **__A:   Optional[int]   )    ->				int:
												super().__init__(__A		,     **__A   )
												_A								= hparams.src_lang
												_A								= hparams.tgt_lang



							def       __A (  self:   List[str]		,     __A:   int		,     __A:   str   )    ->				dict:
												return calculate_bleu(__A		,     __A   )




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







					Path(args.output_dir ).mkdir(exist_ok=_lowercase )
					check_output_dir(_lowercase					,			expected_items=3 )

					if model is None:
										if "summarization" in args.task:
															_A								= SummarizationModule(_lowercase )
										else:
															_A								= TranslationModule(_lowercase )
					_A								= Path(args.data_dir ).name
					if (
					    args.logger_name == "default"
					    or args.fast_dev_run
					    or str(args.output_dir ).startswith('''/tmp''' )
					    or str(args.output_dir ).startswith('''/var''' )
					):
										_A								= True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										_A								= os.environ.get('''WANDB_PROJECT'''					,			_lowercase )
										_A								= WandbLogger(name=model.output_dir.name					,			project=_lowercase )

					elif args.logger_name == "wandb_shared":
										from pytorch_lightning.loggers import WandbLogger

										_A								= WandbLogger(name=model.output_dir.name					,			project=f"""hf_{dataset}""" )

					if args.early_stopping_patience >= 0:
										_A								= get_early_stopping_callback(model.val_metric					,			args.early_stopping_patience )
					else:
										_A								= False

					_A								= args.val_metric == '''loss'''
					_A								= generic_train(
					    _lowercase					,			_lowercase					,			logging_callback=SeqaSeqLoggingCallback()					,			checkpoint_callback=get_checkpoint_callback(
					        args.output_dir					,			model.val_metric					,			args.save_top_k					,			_lowercase )					,			early_stopping_callback=_lowercase					,			logger=_lowercase					,			)
					pickle_save(model.hparams					,			model.output_dir / '''hparams.pkl''' )
					if not args.do_predict:
										return model

					_A								= ''''''
					_A								= sorted(glob.glob(os.path.join(args.output_dir					,			'''*.ckpt''' )					,			recursive=_lowercase ) )
					if checkpoints:
										_A								= checkpoints[-1]
										_A								= checkpoints[-1]
					trainer.logger.log_hyperparams(model.hparams )

					# test() without a model tests using the best checkpoint automatically
					trainer.test()
					return model


if __name__ == "__main__":
				__A      =				argparse.ArgumentParser()
				__A      =				pl.Trainer.add_argparse_args(parser)
				__A      =				SummarizationModule.add_model_specific_args(parser, os.getcwd())

				__A      =				parser.parse_args()

				main(args)











from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def      __A					(     _lowercase = "" ):

					'''simple docstring'''







					_A								= url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					_A								= BeautifulSoup(requests.get(_lowercase ).text					,			'''html.parser''' )
					_A								= soup.find_all('''td'''					,			attrs='''titleColumn''' )
					_A								= soup.find_all('''td'''					,			class_='''ratingColumn imdbRating''' )
					return {
					    title.a.text: float(rating.strong.text )
					    for title, rating in zip(_lowercase					,			_lowercase )
					}




def      __A					(     _lowercase = "IMDb_Top_250_Movies.csv" ):

					'''simple docstring'''







					_A								= get_imdb_top_aaa_movies()
					with open(_lowercase					,			'''w'''					,			newline='''''' ) as out_file:
										_A								= csv.writer(_lowercase )
										writer.writerow(['''Movie title''', '''IMDb rating'''] )
										for title, rating in movies.items():
															writer.writerow([title, rating] )


if __name__ == "__main__":
				write_movies()



