






"""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


_UpperCamelCase	     =    logging.getLogger(__name__)







class    lowerCamelCase__   (			A__					):
    SCREAMING_SNAKE_CASE						=     """summarization"""
    SCREAMING_SNAKE_CASE						=     ["""loss"""]
    SCREAMING_SNAKE_CASE						=     ROUGE_KEYS
    SCREAMING_SNAKE_CASE						=     """rouge2"""






    def __init__(					self      ,A      ,**A   ):
          if hparams.sortish_sampler and hparams.gpus > 1:
                UpperCAmelCase							     =			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   )
          UpperCAmelCase							     =			Path(self.output_dir   ) / """metrics.json"""
          UpperCAmelCase							     =			Path(self.output_dir   ) / """hparams.pkl"""
          pickle_save(self.hparams      ,self.hparams_save_path   )
          UpperCAmelCase							     =			0
          UpperCAmelCase							     =			defaultdict(_UpperCamelCase   )
          UpperCAmelCase							     =			self.config.model_type
          UpperCAmelCase							     =			self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

          UpperCAmelCase							     =			{
              """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()   )

          UpperCAmelCase							     =			get_git_info()["""repo_sha"""]
          UpperCAmelCase							     =			hparams.num_workers
          UpperCAmelCase							     =			None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,_UpperCamelCase   ):
                UpperCAmelCase							     =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                UpperCAmelCase							     =			self.decoder_start_token_id
          UpperCAmelCase							     =			(
              SeqaSeqDataset if hasattr(self.tokenizer      ,"""prepare_seq2seq_batch"""   ) else LegacySeqaSeqDataset
          )
          UpperCAmelCase							     =			False
          UpperCAmelCase							     =			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:
                UpperCAmelCase							     =			self.hparams.eval_max_gen_length
          else:
                UpperCAmelCase							     =			self.model.config.max_length
          UpperCAmelCase							     =			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			{
              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"""   )

          UpperCAmelCase							     =			True
          return readable_batch






    def 							_UpperCamelCase				(					self      ,A      ,**A   ):
          return self.model(_UpperCamelCase      ,**_UpperCamelCase   )






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			self.tokenizer.batch_decode(
              _UpperCamelCase      ,skip_special_tokens=_UpperCamelCase      ,clean_up_tokenization_spaces=_UpperCamelCase   )
          return lmap(str.strip      ,_UpperCamelCase   )






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			self.tokenizer.pad_token_id
          UpperCAmelCase     ,   UpperCAmelCase							     =			batch["""input_ids"""], batch["""attention_mask"""]
          UpperCAmelCase							     =			batch["""labels"""]
          if isinstance(self.model      ,_UpperCamelCase   ):
                UpperCAmelCase							     =			self.model._shift_right(_UpperCamelCase   )
          else:
                UpperCAmelCase							     =			shift_tokens_right(_UpperCamelCase      ,_UpperCamelCase   )
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                UpperCAmelCase							     =			decoder_input_ids
                self.save_readable_batch(_UpperCamelCase   )

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

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






    @property
    def 							_UpperCamelCase				(					self   ):
          return self.tokenizer.pad_token_id






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          UpperCAmelCase							     =			self._step(_UpperCamelCase   )

          UpperCAmelCase							     =			dict(zip(self.loss_names      ,_UpperCamelCase   )   )
          # tokens per batch
          UpperCAmelCase							     =			batch["""input_ids"""].ne(self.pad   ).sum() + batch["""labels"""].ne(self.pad   ).sum()
          UpperCAmelCase							     =			batch["""input_ids"""].shape[0]
          UpperCAmelCase							     =			batch["""input_ids"""].eq(self.pad   ).sum()
          UpperCAmelCase							     =			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      ,A      ,A   ):
          return self._generative_step(_UpperCamelCase   )






    def 							_UpperCamelCase				(					self      ,A      ,A="val"   ):
          self.step_count += 1
          UpperCAmelCase							     =			{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
          UpperCAmelCase							     =			losses["""loss"""]
          UpperCAmelCase							     =			{
              k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
          }
          UpperCAmelCase							     =			(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          UpperCAmelCase							     =			torch.tensor(_UpperCamelCase   ).type_as(_UpperCamelCase   )
          generative_metrics.update({k: v.item() for k, v in losses.items()}   )
          losses.update(_UpperCamelCase   )
          UpperCAmelCase							     =			{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          UpperCAmelCase							     =			self.step_count
          self.metrics[prefix].append(_UpperCamelCase   )  # callback writes this to self.metrics_save_path
          UpperCAmelCase							     =			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      ,A      ,A   ):
          return calculate_rouge(_UpperCamelCase      ,_UpperCamelCase   )






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          UpperCAmelCase							     =			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      ,)
          UpperCAmelCase							     =			(time.time() - ta) / batch["""input_ids"""].shape[0]
          UpperCAmelCase							     =			self.ids_to_clean_text(_UpperCamelCase   )
          UpperCAmelCase							     =			self.ids_to_clean_text(batch["""labels"""]   )
          UpperCAmelCase							     =			self._step(_UpperCamelCase   )
          UpperCAmelCase							     =			dict(zip(self.loss_names      ,_UpperCamelCase   )   )
          UpperCAmelCase							     =			self.calc_generative_metrics(_UpperCamelCase      ,_UpperCamelCase   )
          UpperCAmelCase							     =			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      ,A      ,A   ):
          return self._generative_step(_UpperCamelCase   )






    def 							_UpperCamelCase				(					self      ,A   ):
          return self.validation_epoch_end(_UpperCamelCase      ,prefix="""test"""   )






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			self.n_obs[type_path]
          UpperCAmelCase							     =			self.target_lens[type_path]
          UpperCAmelCase							     =			self.dataset_class(
              self.tokenizer      ,type_path=_UpperCamelCase      ,n_obs=_UpperCamelCase      ,max_target_length=_UpperCamelCase      ,**self.dataset_kwargs      ,)
          return dataset






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A = False   ):
          UpperCAmelCase							     =			self.get_dataset(_UpperCamelCase   )

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                UpperCAmelCase							     =			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":
                UpperCAmelCase							     =			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   ):
          UpperCAmelCase							     =			self.get_dataloader("""train"""      ,batch_size=self.hparams.train_batch_size      ,shuffle=_UpperCamelCase   )
          return dataloader






    def 							_UpperCamelCase				(					self   ):
          return self.get_dataloader("""val"""      ,batch_size=self.hparams.eval_batch_size   )






    def 							_UpperCamelCase				(					self   ):
          return self.get_dataloader("""test"""      ,batch_size=self.hparams.eval_batch_size   )






    @staticmethod
    def 							_UpperCamelCase				(					A      ,A   ):
          BaseTransformer.add_model_specific_args(_UpperCamelCase      ,_UpperCamelCase   )
          add_generic_args(_UpperCamelCase      ,_UpperCamelCase   )
          parser.add_argument(
              """--max_source_length"""      ,default=1_024      ,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    lowerCamelCase__   (			A__					):
    SCREAMING_SNAKE_CASE						=     """translation"""
    SCREAMING_SNAKE_CASE						=     ["""loss"""]
    SCREAMING_SNAKE_CASE						=     ["""bleu"""]
    SCREAMING_SNAKE_CASE						=     """bleu"""






    def __init__(					self      ,A      ,**A   ):
          super().__init__(_UpperCamelCase      ,**_UpperCamelCase   )
          UpperCAmelCase							     =			hparams.src_lang
          UpperCAmelCase							     =			hparams.tgt_lang






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          return calculate_bleu(_UpperCamelCase      ,_UpperCamelCase   )







def _a					(						_snake_case ,			_snake_case=None				):







      """simple docstring"""






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

      if model is None:
            if "summarization" in args.task:
                  UpperCAmelCase							     =			SummarizationModule(SCREAMING_SNAKE_CASE_				)
            else:
                  UpperCAmelCase							     =			TranslationModule(SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase							     =			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"""				)
      ):
            UpperCAmelCase							     =			True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            UpperCAmelCase							     =			os.environ.get("""WANDB_PROJECT""" ,			SCREAMING_SNAKE_CASE_				)
            UpperCAmelCase							     =			WandbLogger(name=model.output_dir.name ,			project=SCREAMING_SNAKE_CASE_				)

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

            UpperCAmelCase							     =			WandbLogger(name=model.output_dir.name ,			project=F'''hf_{dataset}'''				)

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

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

      UpperCAmelCase							     =			""""""
      UpperCAmelCase							     =			sorted(glob.glob(os.path.join(args.output_dir ,			"""*.ckpt"""				) ,			recursive=SCREAMING_SNAKE_CASE_				)				)
      if checkpoints:
            UpperCAmelCase							     =			checkpoints[-1]
            UpperCAmelCase							     =			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__":
  _UpperCamelCase	     =    argparse.ArgumentParser()
  _UpperCamelCase	     =    pl.Trainer.add_argparse_args(parser)
  _UpperCamelCase	     =    SummarizationModule.add_model_specific_args(parser, os.getcwd())

  _UpperCamelCase	     =    parser.parse_args()

  main(args)










from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase_           =	logging.get_logger(__name__)


UpperCAmelCase_           =	OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

UpperCAmelCase_           =	OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_           =	_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModel)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[Any]							  =   FLAX_MODEL_FOR_PRETRAINING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[Any]							  =   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_MASKED_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  List[str]							  =   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Union[str, Any]							  =   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Any							  =   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Dict							  =   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  Optional[int]							  =   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")




class 			__UpperCamelCase    (							_BaseAutoModelClass    ):
     __A	:  str							  =   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




UpperCAmelCase_           =	auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)