'''simple docstring'''





import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE_:					List[str]  =getLogger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			int = 8 ,   snake_case_	:			int = 10_24 ,   snake_case_	:			List[Any]="val" ,   snake_case_	:			int=None ,   snake_case_	:			Tuple=False ,   snake_case_	:			Optional[Any]="summarization" ,   snake_case_	:			Optional[Any]=None ,   snake_case_	:			Any=1 ,   snake_case_	:			Dict = None ,   snake_case_	:			Union[str, Any]="" ,   **snake_case_	:			Any ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       str(snake_case_      )
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl" ,   rank=snake_case_      )

     UpperCAmelCase_	   =       Path(snake_case_      )
     UpperCAmelCase_	   =       save_dir.joinpath(f"""rank_{local_rank}_output.json"""      )
     torch.cuda.set_device(snake_case_      )
     UpperCAmelCase_	   =       AutoModelForSeqaSeqLM.from_pretrained(snake_case_      ).cuda()
     if fpaa:
          UpperCAmelCase_	   =       model.half()
     # determine if we need to increase num_beams
     use_task_specific_params(snake_case_ ,   snake_case_      )  # update config with task specific params
     UpperCAmelCase_	   =       generate_kwargs.pop("num_beams" ,   model.config.num_beams      )  # AttributeError risk?
     if num_return_sequences > num_beams:
          UpperCAmelCase_	   =       num_return_sequences

     UpperCAmelCase_	   =       AutoTokenizer.from_pretrained(snake_case_      )
     logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""      )  # if this is wrong, check config.model_type.

     if max_source_length is None:
          UpperCAmelCase_	   =       tokenizer.model_max_length
     if prefix is None:
          UpperCAmelCase_	   =       prefix or getattr(model.config ,   "prefix" ,   ""      ) or ""
     UpperCAmelCase_	   =       SeqaSeqDataset(
         snake_case_ ,   snake_case_ ,   snake_case_ ,   max_target_length=10_24 ,   type_path=snake_case_ ,   n_obs=snake_case_ ,   prefix=snake_case_ ,   **snake_case_ ,   )
     # I set shuffle=True for a more accurate progress bar.
     # If all the longest samples are first, the prog bar estimate is too high at the beginning.
     UpperCAmelCase_	   =       ds.make_sortish_sampler(snake_case_ ,   distributed=snake_case_ ,   add_extra_examples=snake_case_ ,   shuffle=snake_case_      )
     UpperCAmelCase_	   =       DataLoader(snake_case_ ,   sampler=snake_case_ ,   batch_size=snake_case_ ,   collate_fn=ds.collate_fn      )
     UpperCAmelCase_	   =       []
     for batch in tqdm(snake_case_      ):
          UpperCAmelCase_	   =       model.generate(
              input_ids=batch["input_ids"].to(model.device      ) ,   attention_mask=batch["attention_mask"].to(model.device      ) ,   num_return_sequences=snake_case_ ,   num_beams=snake_case_ ,   **snake_case_ ,   )
          UpperCAmelCase_	   =       tokenizer.batch_decode(snake_case_ ,   skip_special_tokens=snake_case_ ,   clean_up_tokenization_spaces=snake_case_      )
          UpperCAmelCase_	   =       batch["ids"]
          if num_return_sequences > 1:
               UpperCAmelCase_	   =       chunks(snake_case_ ,   snake_case_      )  # batch size chunks, each of size num_return_seq
          for i, pred in enumerate(snake_case_      ):
               results.append({"pred": pred, "id": ids[i].item()}      )
     save_json(snake_case_ ,   snake_case_      )
     return results, sampler.num_replicas





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser(
         epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"      )
     parser.add_argument("--data_dir" ,   type=snake_case_ ,   help="like cnn_dm/test.source"      )
     parser.add_argument(
         "--model_name" ,   type=snake_case_ ,   help="like facebook/bart-large-cnn,t5-base, etc." ,   default="sshleifer/distilbart-xsum-12-3" ,   )
     parser.add_argument("--save_dir" ,   type=snake_case_ ,   help="where to save" ,   default="tmp_gen"      )
     parser.add_argument("--max_source_length" ,   type=snake_case_ ,   default=snake_case_      )
     parser.add_argument(
         "--type_path" ,   type=snake_case_ ,   default="test" ,   help="which subset to evaluate typically train/val/test"      )
     parser.add_argument("--task" ,   type=snake_case_ ,   default="summarization" ,   help="used for task_specific_params + metrics"      )
     parser.add_argument("--bs" ,   type=snake_case_ ,   default=8 ,   required=snake_case_ ,   help="batch size"      )
     parser.add_argument(
         "--local_rank" ,   type=snake_case_ ,   default=-1 ,   required=snake_case_ ,   help="should be passed by distributed.launch"      )

     parser.add_argument(
         "--n_obs" ,   type=snake_case_ ,   default=snake_case_ ,   required=snake_case_ ,   help="How many observations. Defaults to all."      )
     parser.add_argument(
         "--num_return_sequences" ,   type=snake_case_ ,   default=1 ,   required=snake_case_ ,   help="How many sequences to return"      )
     parser.add_argument(
         "--sync_timeout" ,   type=snake_case_ ,   default=6_00 ,   required=snake_case_ ,   help="How long should master process wait for other processes to finish." ,   )
     parser.add_argument("--src_lang" ,   type=snake_case_ ,   default=snake_case_ ,   required=snake_case_      )
     parser.add_argument("--tgt_lang" ,   type=snake_case_ ,   default=snake_case_ ,   required=snake_case_      )
     parser.add_argument(
         "--prefix" ,   type=snake_case_ ,   required=snake_case_ ,   default=snake_case_ ,   help="will be added to the begininng of src examples"      )
     parser.add_argument("--fp16" ,   action="store_true"      )
     parser.add_argument("--debug" ,   action="store_true"      )
     UpperCAmelCase_	   =       time.time()
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       parser.parse_known_args()
     UpperCAmelCase_	   =       parse_numeric_n_bool_cl_kwargs(snake_case_      )
     if generate_kwargs and args.local_rank <= 0:
          print(f"""parsed the following generate kwargs: {generate_kwargs}"""      )
     UpperCAmelCase_	   =       Path(args.save_dir + "_tmp"      )
     Path(snake_case_      ).mkdir(exist_ok=snake_case_      )  # this handles locking.
     UpperCAmelCase_	   =       list(json_save_dir.glob("rank_*.json"      )      )
     if intermediate_files:
          raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""      )
          # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
     UpperCAmelCase_	   =       {}
     if args.src_lang is not None:
          UpperCAmelCase_	   =       args.src_lang
     if args.tgt_lang is not None:
          UpperCAmelCase_	   =       args.tgt_lang

     Path(args.save_dir      ).mkdir(exist_ok=snake_case_      )
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       eval_data_dir(
         args.data_dir ,   snake_case_ ,   args.model_name ,   type_path=args.type_path ,   bs=args.bs ,   fpaa=args.fpaa ,   task=args.task ,   local_rank=args.local_rank ,   n_obs=args.n_obs ,   max_source_length=args.max_source_length ,   num_return_sequences=args.num_return_sequences ,   prefix=args.prefix ,   dataset_kwargs=snake_case_ ,   **snake_case_ ,   )

     if args.local_rank <= 0:
          UpperCAmelCase_	   =       Path(args.save_dir      )
          save_dir.mkdir(exist_ok=snake_case_      )
          UpperCAmelCase_	   =       gather_results_from_each_node(snake_case_ ,   snake_case_ ,   args.sync_timeout      )
          UpperCAmelCase_	   =       combine_partial_results(snake_case_      )
          if args.num_return_sequences > 1:
               UpperCAmelCase_	   =       save_dir.joinpath("pseudolabel_results.json"      )
               print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""      )
               save_json(snake_case_ ,   snake_case_      )
               return
          UpperCAmelCase_	   =       Path(args.data_dir      ).joinpath(args.type_path + ".target"      )
          with open(snake_case_      ) as f:
               UpperCAmelCase_	   =       [x.rstrip() for x in f.readlines()][: len(snake_case_      )]

          # Calculate metrics, save metrics,  and save _generations.txt
          UpperCAmelCase_	   =       "translation" in args.task
          UpperCAmelCase_	   =       calculate_bleu if calc_bleu else calculate_rouge
          UpperCAmelCase_	   =       "bleu" if calc_bleu else "rouge"
          UpperCAmelCase_	   =       score_fn(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       len(snake_case_      )
          UpperCAmelCase_	   =       time.time() - start_time
          UpperCAmelCase_	   =       round(runtime / metrics["n_obs"] ,   4      )
          UpperCAmelCase_	   =       num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          UpperCAmelCase_	   =       save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""      )
          save_json(snake_case_ ,   snake_case_ ,   indent=snake_case_      )
          print(snake_case_      )
          write_txt_file(snake_case_ ,   save_dir.joinpath(f"""{args.type_path}_generations.txt"""      )      )
          if args.debug:
               write_txt_file(snake_case_ ,   save_dir.joinpath(f"""{args.type_path}.target"""      )      )
          else:
               shutil.rmtree(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				List:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     for partial_result in partial_results:
          records.extend(snake_case_      )
     UpperCAmelCase_	   =       sorted(snake_case_ ,   key=lambda snake_case_      : x["id"]      )
     UpperCAmelCase_	   =       [x["pred"] for x in records]
     return preds





def      lowerCAmelCase_    ( snake_case_	:			Optional[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Any      )   ->				List[Dict[str, List]]:


     '''simple docstring'''




     UpperCAmelCase_	   =       time.time()
     logger.info("waiting for all nodes to finish"      )
     UpperCAmelCase_	   =       None
     while (time.time() - start_wait) < timeout:
          UpperCAmelCase_	   =       list(save_dir.glob("rank_*.json"      )      )
          if len(snake_case_      ) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase_	   =       lmap(snake_case_ ,   snake_case_      )
               return json_data
          except JSONDecodeError:
               continue
     else:
          raise TimeoutError("Rank 0 gave up on waiting for other processes"      )
     # Unreachable


if __name__ == "__main__":
    # Usage for MT:
    run_generate()





'''simple docstring'''





from collections import OrderedDict

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


SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					List[Any]  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[Any]  =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'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModel)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						str	       =  FLAX_MODEL_FOR_PRETRAINING_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Tuple  =auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_MASKED_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[int]	       =  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Tuple	       =  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Dict	       =  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




SCREAMING_SNAKE_CASE_:					Any  =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




SCREAMING_SNAKE_CASE_:					int  =auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Any	       =  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Union[str, Any]  =auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)




