
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


_SCREAMING_SNAKE_CASE 		=						logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE_  (   __lowerCamelCase   ):
						__magic_name__:     Any	 =    'summarization'
						__magic_name__:     List[str]	 =    ['loss']
						__magic_name__:     Union[str, Any]	 =    ROUGE_KEYS
						__magic_name__:     List[str]	 =    'rouge2'






						def __init__( self : Optional[Any]						,				_A : Optional[int]						,				**_A : Any		)							->      Optional[int]:







							"""simple docstring"""


							if hparams.sortish_sampler and hparams.gpus > 1:
								snake_case_						:     int     =		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__(UpperCamelCase_						,				num_labels=UpperCamelCase_						,				mode=self.mode						,				**UpperCamelCase_		)
							use_task_specific_params(self.model						,				'summarization'		)
							save_git_info(self.hparams.output_dir		)
							snake_case_						:     Optional[int]     =		Path(self.output_dir		) / 'metrics.json'
							snake_case_						:     Dict     =		Path(self.output_dir		) / 'hparams.pkl'
							pickle_save(self.hparams						,				self.hparams_save_path		)
							snake_case_						:     Tuple     =		0
							snake_case_						:     Tuple     =		defaultdict(UpperCamelCase_		)
							snake_case_						:     int     =		self.config.model_type
							snake_case_						:     Union[str, Any]     =		self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

							snake_case_						:     List[str]     =		{
							    '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()		)

							snake_case_						:     int     =		get_git_info()['repo_sha']
							snake_case_						:     Optional[int]     =		hparams.num_workers
							snake_case_						:     Any     =		None  # default to config
							if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,				UpperCamelCase_		):
								snake_case_						:     Dict     =		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
								snake_case_						:     Union[str, Any]     =		self.decoder_start_token_id
							snake_case_						:     Any     =		(
							    SeqaSeqDataset if hasattr(self.tokenizer						,				'prepare_seq2seq_batch'		) else LegacySeqaSeqDataset
							)
							snake_case_						:     str     =		False
							snake_case_						:     List[Any]     =		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:
								snake_case_						:     int     =		self.hparams.eval_max_gen_length
							else:
								snake_case_						:     Tuple     =		self.model.config.max_length
							snake_case_						:     List[Any]     =		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






						def UpperCAmelCase_			( self : Tuple						,				_A : Dict[str, torch.Tensor]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		{
							    k: self.tokenizer.batch_decode(v.tolist()		) if 'mask' not in k else v.shape for k, v in batch.items()
							}
							save_json(UpperCamelCase_						,				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'		)

							snake_case_						:     List[str]     =		True
							return readable_batch






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : List[str]						,				**_A : List[str]		)							->      str:







							"""simple docstring"""


							return self.model(UpperCamelCase_						,				**UpperCamelCase_		)






						def UpperCAmelCase_			( self : List[Any]						,				_A : List[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.tokenizer.batch_decode(
							    UpperCamelCase_						,				skip_special_tokens=UpperCamelCase_						,				clean_up_tokenization_spaces=UpperCamelCase_		)
							return lmap(str.strip						,				UpperCamelCase_		)






						def UpperCAmelCase_			( self : Tuple						,				_A : dict		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.tokenizer.pad_token_id
							snake_case_  ,snake_case_						:     Dict     =		batch['input_ids'], batch['attention_mask']
							snake_case_						:     List[Any]     =		batch['labels']
							if isinstance(self.model						,				UpperCamelCase_		):
								snake_case_						:     int     =		self.model._shift_right(UpperCamelCase_		)
							else:
								snake_case_						:     Union[str, Any]     =		shift_tokens_right(UpperCamelCase_						,				UpperCamelCase_		)
							if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
								snake_case_						:     List[Any]     =		decoder_input_ids
								self.save_readable_batch(UpperCamelCase_		)

							snake_case_						:     Tuple     =		self(UpperCamelCase_						,				attention_mask=UpperCamelCase_						,				decoder_input_ids=UpperCamelCase_						,				use_cache=UpperCamelCase_		)
							snake_case_						:     str     =		outputs['logits']
							if self.hparams.label_smoothing == 0:
								# Same behavior as modeling_bart.py, besides ignoring pad_token_id
								snake_case_						:     Optional[Any]     =		nn.CrossEntropyLoss(ignore_index=UpperCamelCase_		)

								assert lm_logits.shape[-1] == self.vocab_size
								snake_case_						:     Dict     =		ce_loss_fct(lm_logits.view(-1						,				lm_logits.shape[-1]		)						,				tgt_ids.view(-1		)		)
							else:
								snake_case_						:     Any     =		nn.functional.log_softmax(UpperCamelCase_						,				dim=-1		)
								snake_case_  ,snake_case_						:     Union[str, Any]     =		label_smoothed_nll_loss(
								    UpperCamelCase_						,				UpperCamelCase_						,				self.hparams.label_smoothing						,				ignore_index=UpperCamelCase_		)
							return (loss,)






						@property
						def UpperCAmelCase_			( self : Any		)							->      List[Any]:







							"""simple docstring"""


							return self.tokenizer.pad_token_id






						def UpperCAmelCase_			( self : List[Any]						,				_A : Union[str, Any]						,				_A : Tuple		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     int     =		self._step(UpperCamelCase_		)

							snake_case_						:     Optional[int]     =		dict(zip(self.loss_names						,				UpperCamelCase_		)		)
							# tokens per batch
							snake_case_						:     Any     =		batch['input_ids'].ne(self.pad		).sum() + batch['labels'].ne(self.pad		).sum()
							snake_case_						:     List[Any]     =		batch['input_ids'].shape[0]
							snake_case_						:     List[Any]     =		batch['input_ids'].eq(self.pad		).sum()
							snake_case_						:     str     =		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 UpperCAmelCase_			( self : Union[str, Any]						,				_A : Dict						,				_A : str		)							->      Union[str, Any]:







							"""simple docstring"""


							return self._generative_step(UpperCamelCase_		)






						def UpperCAmelCase_			( self : Any						,				_A : List[Any]						,				_A : Optional[Any]="val"		)							->      List[str]:







							"""simple docstring"""


							self.step_count += 1
							snake_case_						:     Any     =		{k: torch.stack([x[k] for x in outputs]		).mean() for k in self.loss_names}
							snake_case_						:     Tuple     =		losses['loss']
							snake_case_						:     Dict     =		{
							    k: np.array([x[k] for x in outputs]		).mean() for k in self.metric_names + ['gen_time', 'gen_len']
							}
							snake_case_						:     List[str]     =		(
							    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
							)
							snake_case_						:     List[str]     =		torch.tensor(UpperCamelCase_		).type_as(UpperCamelCase_		)
							generative_metrics.update({k: v.item() for k, v in losses.items()}		)
							losses.update(UpperCamelCase_		)
							snake_case_						:     str     =		{F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
							snake_case_						:     Union[str, Any]     =		self.step_count
							self.metrics[prefix].append(UpperCamelCase_		)  # callback writes this to self.metrics_save_path
							snake_case_						:     List[Any]     =		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 UpperCAmelCase_			( self : List[str]						,				_A : Union[str, Any]						,				_A : Optional[int]		)							->      str:







							"""simple docstring"""


							return calculate_rouge(UpperCamelCase_						,				UpperCamelCase_		)






						def UpperCAmelCase_			( self : Optional[int]						,				_A : dict		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Dict     =		time.time()

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






						def UpperCAmelCase_			( self : int						,				_A : Any						,				_A : Dict		)							->      int:







							"""simple docstring"""


							return self._generative_step(UpperCamelCase_		)






						def UpperCAmelCase_			( self : Dict						,				_A : Tuple		)							->      Optional[Any]:







							"""simple docstring"""


							return self.validation_epoch_end(UpperCamelCase_						,				prefix='test'		)






						def UpperCAmelCase_			( self : Optional[int]						,				_A : str		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.n_obs[type_path]
							snake_case_						:     Optional[Any]     =		self.target_lens[type_path]
							snake_case_						:     List[str]     =		self.dataset_class(
							    self.tokenizer						,				type_path=UpperCamelCase_						,				n_obs=UpperCamelCase_						,				max_target_length=UpperCamelCase_						,				**self.dataset_kwargs						,				)
							return dataset






						def UpperCAmelCase_			( self : Union[str, Any]						,				_A : str						,				_A : int						,				_A : bool = False		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.get_dataset(UpperCamelCase_		)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
								snake_case_						:     Optional[Any]     =		dataset.make_sortish_sampler(UpperCamelCase_						,				distributed=self.hparams.gpus > 1		)
								return DataLoader(
								    UpperCamelCase_						,				batch_size=UpperCamelCase_						,				collate_fn=dataset.collate_fn						,				shuffle=UpperCamelCase_						,				num_workers=self.num_workers						,				sampler=UpperCamelCase_						,				)

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






						def UpperCAmelCase_			( self : Optional[int]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.get_dataloader('train'						,				batch_size=self.hparams.train_batch_size						,				shuffle=UpperCamelCase_		)
							return dataloader






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Dict:







							"""simple docstring"""


							return self.get_dataloader('val'						,				batch_size=self.hparams.eval_batch_size		)






						def UpperCAmelCase_			( self : Any		)							->      str:







							"""simple docstring"""


							return self.get_dataloader('test'						,				batch_size=self.hparams.eval_batch_size		)






						@staticmethod
						def UpperCAmelCase_			( _A : Any						,				_A : int		)							->      Union[str, Any]:







							"""simple docstring"""


							BaseTransformer.add_model_specific_args(UpperCamelCase_						,				UpperCamelCase_		)
							add_generic_args(UpperCamelCase_						,				UpperCamelCase_		)
							parser.add_argument(
							    '--max_source_length'						,				default=1024						,				type=UpperCamelCase_						,				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=UpperCamelCase_						,				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=142						,				type=UpperCamelCase_						,				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=142						,				type=UpperCamelCase_						,				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=UpperCamelCase_		)
							parser.add_argument('--overwrite_output_dir'						,				action='store_true'						,				default=UpperCamelCase_		)
							parser.add_argument('--max_tokens_per_batch'						,				type=UpperCamelCase_						,				default=UpperCamelCase_		)
							parser.add_argument('--logger_name'						,				type=UpperCamelCase_						,				choices=['default', 'wandb', 'wandb_shared']						,				default='default'		)
							parser.add_argument('--n_train'						,				type=UpperCamelCase_						,				default=-1						,				required=UpperCamelCase_						,				help='# examples. -1 means use all.'		)
							parser.add_argument('--n_val'						,				type=UpperCamelCase_						,				default=500						,				required=UpperCamelCase_						,				help='# examples. -1 means use all.'		)
							parser.add_argument('--n_test'						,				type=UpperCamelCase_						,				default=-1						,				required=UpperCamelCase_						,				help='# examples. -1 means use all.'		)
							parser.add_argument(
							    '--task'						,				type=UpperCamelCase_						,				default='summarization'						,				required=UpperCamelCase_						,				help='# examples. -1 means use all.'		)
							parser.add_argument('--label_smoothing'						,				type=UpperCamelCase_						,				default=0.0						,				required=UpperCamelCase_		)
							parser.add_argument('--src_lang'						,				type=UpperCamelCase_						,				default=''						,				required=UpperCamelCase_		)
							parser.add_argument('--tgt_lang'						,				type=UpperCamelCase_						,				default=''						,				required=UpperCamelCase_		)
							parser.add_argument('--eval_beams'						,				type=UpperCamelCase_						,				default=UpperCamelCase_						,				required=UpperCamelCase_		)
							parser.add_argument(
							    '--val_metric'						,				type=UpperCamelCase_						,				default=UpperCamelCase_						,				required=UpperCamelCase_						,				choices=['bleu', 'rouge2', 'loss', None]		)
							parser.add_argument('--eval_max_gen_length'						,				type=UpperCamelCase_						,				default=UpperCamelCase_						,				help='never generate more than n tokens'		)
							parser.add_argument('--save_top_k'						,				type=UpperCamelCase_						,				default=1						,				required=UpperCamelCase_						,				help='How many checkpoints to save'		)
							parser.add_argument(
							    '--early_stopping_patience'						,				type=UpperCamelCase_						,				default=-1						,				required=UpperCamelCase_						,				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_  (   __lowerCamelCase   ):
						__magic_name__:     Optional[int]	 =    'translation'
						__magic_name__:     Any	 =    ['loss']
						__magic_name__:     List[str]	 =    ['bleu']
						__magic_name__:     List[str]	 =    'bleu'






						def __init__( self : Tuple						,				_A : Union[str, Any]						,				**_A : Dict		)							->      List[str]:







							"""simple docstring"""


							super().__init__(UpperCamelCase_						,				**UpperCamelCase_		)
							snake_case_						:     Union[str, Any]     =		hparams.src_lang
							snake_case_						:     Optional[Any]     =		hparams.tgt_lang






						def UpperCAmelCase_			( self : Optional[int]						,				_A : int						,				_A : Any		)							->      Any:







							"""simple docstring"""


							return calculate_bleu(UpperCamelCase_						,				UpperCamelCase_		)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	Path(args.output_dir				).mkdir(exist_ok=A__				)
	check_output_dir(A__				,	expected_items=3				)

	if model is None:
		if "summarization" in args.task:
			snake_case_						:     List[str]     =		SummarizationModule(A__				)
		else:
			snake_case_						:     Any     =		TranslationModule(A__				)
	snake_case_						:     int     =		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'				)
	):
		snake_case_						:     int     =		True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		snake_case_						:     List[Any]     =		os.environ.get('WANDB_PROJECT'				,	A__				)
		snake_case_						:     Optional[Any]     =		WandbLogger(name=model.output_dir.name				,	project=A__				)

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

		snake_case_						:     Optional[int]     =		WandbLogger(name=model.output_dir.name				,	project=f"""hf_{dataset}"""				)

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

	snake_case_						:     Optional[Any]     =		args.val_metric == 'loss'
	snake_case_						:     int     =		generic_train(
	    A__				,	A__				,	logging_callback=SeqaSeqLoggingCallback()				,	checkpoint_callback=get_checkpoint_callback(
	        args.output_dir				,	model.val_metric				,	args.save_top_k				,	A__				)				,	early_stopping_callback=A__				,	logger=A__				,	)
	pickle_save(model.hparams				,	model.output_dir / 'hparams.pkl'				)
	if not args.do_predict:
		return model

	snake_case_						:     Dict     =		''
	snake_case_						:     int     =		sorted(glob.glob(os.path.join(args.output_dir				,	'*.ckpt'				)				,	recursive=A__				)				)
	if checkpoints:
		snake_case_						:     Optional[Any]     =		checkpoints[-1]
		snake_case_						:     int     =		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__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						_SCREAMING_SNAKE_CASE 		=						pl.Trainer.add_argparse_args(parser)
						_SCREAMING_SNAKE_CASE 		=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

						main(args)








import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE 		=						"""docs/source/en/_toctree.yml"""


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[Any]     =		defaultdict(__a				)
	snake_case_						:     Optional[Any]     =		[]
	snake_case_						:     Optional[Any]     =		[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
		else:
			new_doc_list.append(__a				)

	snake_case_						:     Any     =		new_doc_list
	snake_case_						:     str     =		[key for key, value in counts.items() if value > 1]

	snake_case_						:     Any     =		[]
	for duplicate_key in duplicates:
		snake_case_						:     Any     =		list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
		if len(__a				) > 1:
			raise ValueError(
			    f"""{duplicate_key} is present several times in the documentation table of content at """
			    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
			    'others.'				)
		# Only add this once
		new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
	snake_case_						:     str     =		sorted(__a				,	key=lambda __a				: s["title"].lower()				)

	# "overview" gets special treatment and is always first
	if len(__a				) > 1:
		raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

	overview_doc.extend(__a				)

	# Sort
	return overview_doc


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     int     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     str     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     Dict     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     Tuple     =		0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	snake_case_						:     Union[str, Any]     =		api_doc[scheduler_idx]['sections']
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	snake_case_						:     int     =		False
	if new_scheduler_doc != scheduler_doc:
		snake_case_						:     int     =		True
		if overwrite:
			snake_case_						:     Union[str, Any]     =		new_scheduler_doc

	if diff:
		if overwrite:
			snake_case_						:     Optional[int]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


def 							SCREAMING_SNAKE_CASE__  (      __a=False				):
	with open(__a				,	encoding='utf-8'				) as f:
		snake_case_						:     Dict     =		yaml.safe_load(f.read()				)

	# Get to the API doc
	snake_case_						:     Any     =		0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	snake_case_						:     str     =		content[api_idx]['sections']

	# Then to the model doc
	snake_case_						:     List[Any]     =		0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	snake_case_						:     Dict     =		False
	snake_case_						:     Union[str, Any]     =		api_doc[pipeline_idx]['sections']
	snake_case_						:     Union[str, Any]     =		[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			snake_case_						:     Optional[Any]     =		pipeline_doc['section']
			snake_case_						:     Optional[int]     =		clean_doc_toc(__a				)
			if overwrite:
				snake_case_						:     Tuple     =		new_sub_pipeline_doc
		new_pipeline_docs.append(__a				)

	# sort overall pipeline doc
	snake_case_						:     Optional[Any]     =		clean_doc_toc(__a				)

	if new_pipeline_docs != pipeline_docs:
		snake_case_						:     List[str]     =		True
		if overwrite:
			snake_case_						:     List[str]     =		new_pipeline_docs

	if diff:
		if overwrite:
			snake_case_						:     List[Any]     =		api_doc
			with open(__a				,	'w'				,	encoding='utf-8'				) as f:
				f.write(yaml.dump(__a				,	allow_unicode=__a				)				)
		else:
			raise ValueError(
			    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

						check_scheduler_doc(args.fix_and_overwrite)
						check_pipeline_doc(args.fix_and_overwrite)

