



"""simple docstring"""
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 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			str						=     """summarization"""
					_A		:			Union[str, Any]						=     ["""loss"""]
					_A		:			Dict						=     ROUGE_KEYS
					_A		:			Tuple						=     """rouge2"""






					def __init__(       self:							List[Any]						,     snake_case:							List[str]						,     **snake_case:							Tuple						)   ->	str:
											if hparams.sortish_sampler and hparams.gpus > 1:
																	snake_case_ :List[str]						=			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__(snake_case						,     num_labels=snake_case						,     mode=self.mode						,     **snake_case						)
											use_task_specific_params(self.model						,     """summarization"""						)
											save_git_info(self.hparams.output_dir						)
											snake_case_ :Dict						=			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_ :Dict						=			0
											snake_case_ :List[Any]						=			defaultdict(snake_case						)
											snake_case_ :List[str]						=			self.config.model_type
											snake_case_ :Any						=			self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

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






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Dict[str, torch.Tensor]						)   ->	Dict[str, List[str]]:
											snake_case_ :List[str]						=			{
											    k: self.tokenizer.batch_decode(v.tolist()						) if """mask""" not in k else v.shape for k, v in batch.items()
											}
											save_json(snake_case						,     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_ :int						=			True
											return readable_batch






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Dict						,     **snake_case:							List[Any]						)   ->	List[Any]:
											return self.model(snake_case						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							List[int]						)   ->	Dict:
											snake_case_ :Tuple						=			self.tokenizer.batch_decode(
											    snake_case						,     skip_special_tokens=snake_case						,     clean_up_tokenization_spaces=snake_case						)
											return lmap(str.strip						,     snake_case						)






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

											snake_case_ :List[Any]						=			self(snake_case						,     attention_mask=snake_case						,     decoder_input_ids=snake_case						,     use_cache=snake_case						)
											snake_case_ :Dict						=			outputs["""logits"""]
											if self.hparams.label_smoothing == 0:
																	# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																	snake_case_ :Optional[int]						=			nn.CrossEntropyLoss(ignore_index=snake_case						)

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






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






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Union[str, Any]						,     snake_case:							int						)   ->	Dict:
											snake_case_ :Optional[Any]						=			self._step(snake_case						)

											snake_case_ :Optional[Any]						=			dict(zip(self.loss_names						,     snake_case						)						)
											# tokens per batch
											snake_case_ :List[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_ :str						=			batch["""input_ids"""].eq(self.pad						).sum()
											snake_case_ :Optional[Any]						=			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    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							str						,     snake_case:							int						)   ->	Dict:
											return self._generative_step(snake_case						)






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






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							dict						)   ->	dict:
											snake_case_ :str						=			time.time()

											# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
											snake_case_ :Tuple						=			self.model.generate(
											    batch["""input_ids"""]						,     attention_mask=batch["""attention_mask"""]						,     use_cache=snake_case						,     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_ :List[str]						=			self.ids_to_clean_text(snake_case						)
											snake_case_ :List[str]						=			self.ids_to_clean_text(batch["""labels"""]						)
											snake_case_ :Dict						=			self._step(snake_case						)
											snake_case_ :Optional[int]						=			dict(zip(self.loss_names						,     snake_case						)						)
											snake_case_ :Dict						=			self.calc_generative_metrics(snake_case						,     snake_case						)
											snake_case_ :Any						=			np.mean(lmap(snake_case						,     snake_case						)						)
											base_metrics.update(gen_time=snake_case						,     gen_len=snake_case						,     preds=snake_case						,     target=snake_case						,     **snake_case						)
											return base_metrics






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							str						,     snake_case:							List[Any]						)   ->	Optional[Any]:
											return self._generative_step(snake_case						)






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							Optional[int]						)   ->	List[Any]:
											return self.validation_epoch_end(snake_case						,     prefix="""test"""						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Optional[int]						)   ->	SeqaSeqDataset:
											snake_case_ :Optional[Any]						=			self.n_obs[type_path]
											snake_case_ :Optional[int]						=			self.target_lens[type_path]
											snake_case_ :List[str]						=			self.dataset_class(
											    self.tokenizer						,     type_path=snake_case						,     n_obs=snake_case						,     max_target_length=snake_case						,     **self.dataset_kwargs						,     )
											return dataset






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							str						,     snake_case:							int						,     snake_case:							bool = False						)   ->	DataLoader:
											snake_case_ :Any						=			self.get_dataset(snake_case						)

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

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






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	DataLoader:
											snake_case_ :Any						=			self.get_dataloader("""train"""						,     batch_size=self.hparams.train_batch_size						,     shuffle=snake_case						)
											return dataloader






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	DataLoader:
											return self.get_dataloader("""val"""						,     batch_size=self.hparams.eval_batch_size						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	DataLoader:
											return self.get_dataloader("""test"""						,     batch_size=self.hparams.eval_batch_size						)






					@staticmethod
					def    lowerCAmelCase_				(       snake_case:							List[Any]						,     snake_case:							List[str]						)   ->	List[Any]:
											BaseTransformer.add_model_specific_args(snake_case						,     snake_case						)
											add_generic_args(snake_case						,     snake_case						)
											parser.add_argument(
											    """--max_source_length"""						,     default=1_024						,     type=snake_case						,     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=snake_case						,     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=snake_case						,     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=snake_case						,     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=snake_case						)
											parser.add_argument("""--overwrite_output_dir"""						,     action="""store_true"""						,     default=snake_case						)
											parser.add_argument("""--max_tokens_per_batch"""						,     type=snake_case						,     default=snake_case						)
											parser.add_argument("""--logger_name"""						,     type=snake_case						,     choices=["""default""", """wandb""", """wandb_shared"""]						,     default="""default"""						)
											parser.add_argument("""--n_train"""						,     type=snake_case						,     default=-1						,     required=snake_case						,     help="""# examples. -1 means use all."""						)
											parser.add_argument("""--n_val"""						,     type=snake_case						,     default=500						,     required=snake_case						,     help="""# examples. -1 means use all."""						)
											parser.add_argument("""--n_test"""						,     type=snake_case						,     default=-1						,     required=snake_case						,     help="""# examples. -1 means use all."""						)
											parser.add_argument(
											    """--task"""						,     type=snake_case						,     default="""summarization"""						,     required=snake_case						,     help="""# examples. -1 means use all."""						)
											parser.add_argument("""--label_smoothing"""						,     type=snake_case						,     default=0.0						,     required=snake_case						)
											parser.add_argument("""--src_lang"""						,     type=snake_case						,     default=""""""						,     required=snake_case						)
											parser.add_argument("""--tgt_lang"""						,     type=snake_case						,     default=""""""						,     required=snake_case						)
											parser.add_argument("""--eval_beams"""						,     type=snake_case						,     default=snake_case						,     required=snake_case						)
											parser.add_argument(
											    """--val_metric"""						,     type=snake_case						,     default=snake_case						,     required=snake_case						,     choices=["""bleu""", """rouge2""", """loss""", None]						)
											parser.add_argument("""--eval_max_gen_length"""						,     type=snake_case						,     default=snake_case						,     help="""never generate more than n tokens"""						)
											parser.add_argument("""--save_top_k"""						,     type=snake_case						,     default=1						,     required=snake_case						,     help="""How many checkpoints to save"""						)
											parser.add_argument(
											    """--early_stopping_patience"""						,     type=snake_case						,     default=-1						,     required=snake_case						,     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 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Any						=     """translation"""
					_A		:			Tuple						=     ["""loss"""]
					_A		:			List[Any]						=     ["""bleu"""]
					_A		:			Tuple						=     """bleu"""






					def __init__(       self:							str						,     snake_case:							Tuple						,     **snake_case:							int						)   ->	List[Any]:
											super().__init__(snake_case						,     **snake_case						)
											snake_case_ :Dict						=			hparams.src_lang
											snake_case_ :List[Any]						=			hparams.tgt_lang






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Dict						,     snake_case:							Optional[int]						)   ->	dict:
											return calculate_bleu(snake_case						,     snake_case						)

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:
																		snake_case_ :SummarizationModule						=			SummarizationModule(_lowercase	)
												else:
																		snake_case_ :SummarizationModule						=			TranslationModule(_lowercase	)
						snake_case_ :Tuple						=			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_ :List[str]						=			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""",					_lowercase	)
												snake_case_ :Optional[int]						=			WandbLogger(name=model.output_dir.name,					project=_lowercase	)

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

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

						if args.early_stopping_patience >= 0:
												snake_case_ :Any						=			get_early_stopping_callback(model.val_metric,					args.early_stopping_patience	)
						else:
												snake_case_ :List[Any]						=			False

						snake_case_ :int						=			args.val_metric == """loss"""
						snake_case_ :pl.Trainer						=			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

						snake_case_ :Tuple						=			""""""
						snake_case_ :Union[str, Any]						=			sorted(glob.glob(os.path.join(args.output_dir,					"""*.ckpt"""	),					recursive=_lowercase	)	)
						if checkpoints:
												snake_case_ :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__":
						__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)





"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__a      =				"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__a      =				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__a      =				re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__a      =				re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__a      =				re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a      =				[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",					_lowercase	)
						return [m.group(0	) for m in matches]
def    A_  (			):







						'''simple docstring'''


						snake_case_ :int						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_ :Dict						=			{
						    config.replace("""Config""",					""""""	): model_type for model_type, config in config_maping_names.items()
						}

						# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
						snake_case_ :Optional[Any]						=			collections.defaultdict(_lowercase	)
						snake_case_ :int						=			collections.defaultdict(_lowercase	)
						snake_case_ :List[str]						=			collections.defaultdict(_lowercase	)

						# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
						for attr_name in dir(_lowercase	):
												snake_case_ :int						=			None
												if _re_tf_models.match(_lowercase	) is not None:
																		snake_case_ :int						=			tf_models
																		snake_case_ :List[str]						=			_re_tf_models.match(_lowercase	).groups()[0]
												elif _re_flax_models.match(_lowercase	) is not None:
																		snake_case_ :List[Any]						=			flax_models
																		snake_case_ :Any						=			_re_flax_models.match(_lowercase	).groups()[0]
												elif _re_pt_models.match(_lowercase	) is not None:
																		snake_case_ :Optional[Any]						=			pt_models
																		snake_case_ :int						=			_re_pt_models.match(_lowercase	).groups()[0]

												if lookup_dict is not None:
																		while len(_lowercase	) > 0:
																								if attr_name in model_prefix_to_model_type:
																														snake_case_ :Optional[int]						=			True
																														break
																								# Try again after removing the last word in the name
																								snake_case_ :Optional[Any]						=			"""""".join(camel_case_split(_lowercase	)[:-1]	)

						snake_case_ :Optional[int]						=			set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
						snake_case_ :Optional[Any]						=			list(_lowercase	)
						all_models.sort()

						snake_case_ :Optional[int]						=			{"""model_type""": all_models}
						snake_case_ :Optional[int]						=			[pt_models[t] for t in all_models]
						snake_case_ :Any						=			[tf_models[t] for t in all_models]
						snake_case_ :Dict						=			[flax_models[t] for t in all_models]

						# Now let's use the auto-mapping names to make sure
						snake_case_ :Dict						=			{}
						for t in all_models:
												if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																		snake_case_ :Optional[Any]						=			"""AutoProcessor"""
												elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoTokenizer"""
												elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoFeatureExtractor"""
												else:
																		# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																		snake_case_ :str						=			"""AutoTokenizer"""

						snake_case_ :int						=			[processors[t] for t in all_models]

						return pd.DataFrame(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    transformers_module.models.auto.modeling_auto,
						    transformers_module.models.auto.modeling_tf_auto,
						    transformers_module.models.auto.modeling_flax_auto,
						]
						for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
												snake_case_ :Optional[int]						=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												snake_case_ :List[str]						=			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
												# Loop through all three frameworks
												for module, cls, mapping in zip(_lowercase,					_lowercase,					_lowercase	):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(_lowercase,					_lowercase	):
																								continue
																		# First extract all model_names
																		snake_case_ :Tuple						=			[]
																		for name in getattr(_lowercase,					_lowercase	).values():
																								if isinstance(_lowercase,					_lowercase	):
																														model_names.append(_lowercase	)
																								else:
																														model_names.extend(list(_lowercase	)	)

            # Add pipeline tag and auto model class for those models
																		table.update({model_name: (pipeline_tag, cls) for model_name in model_names}	)

						return table
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			get_frameworks_table()
						snake_case_ :str						=			Dataset.from_pandas(_lowercase	)

						snake_case_ :List[Any]						=			hf_hub_download(
						    """huggingface/transformers-metadata""",					"""pipeline_tags.json""",					repo_type="""dataset""",					token=_lowercase	)
						snake_case_ :List[str]						=			Dataset.from_json(_lowercase	)
						snake_case_ :int						=			{
						    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
						    for i in range(len(_lowercase	)	)
						}
						snake_case_ :Optional[int]						=			update_pipeline_and_auto_class_table(_lowercase	)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_ :Tuple						=			sorted(table.keys()	)
						snake_case_ :Tuple						=			pd.DataFrame(
						    {
						        """model_class""": model_classes,
						        """pipeline_tag""": [table[m][0] for m in model_classes],
						        """auto_class""": [table[m][1] for m in model_classes],
						    }	)
						snake_case_ :Union[str, Any]						=			Dataset.from_pandas(_lowercase	)

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(_lowercase,					"""frameworks.json"""	)	)
												tags_dataset.to_json(os.path.join(_lowercase,					"""pipeline_tags.json"""	)	)

												if commit_sha is not None:
																		snake_case_ :Union[str, Any]						=			(
																		    f"""Update with commit {commit_sha}\n\nSee: """
																		    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		snake_case_ :List[Any]						=			"""Update"""

												upload_folder(
												    repo_id="""huggingface/transformers-metadata""",					folder_path=_lowercase,					repo_type="""dataset""",					token=_lowercase,					commit_message=_lowercase,					)
def    A_  (			):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_ :Dict						=			transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_ :List[str]						=			[]
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_ :int						=			pipeline_tasks[key]["""pt"""]
																		if isinstance(_lowercase,					(list, tuple)	):
																								snake_case_ :Any						=			model[0]
																		snake_case_ :str						=			model.__name__
																		if model not in in_table.values():
																								missing.append(_lowercase	)

						if len(_lowercase	) > 0:
												snake_case_ :Optional[int]						=			""", """.join(_lowercase	)
												raise ValueError(
												    """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
												    f"""`utils/update_metadata.py`: {msg}. Please add them!"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
						parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
						parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
						__a      =				parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)
