
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase__						:	Optional[Any]	     = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase__						:	Any	     = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase__						:	Optional[Any]	     = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase__						:	str	     = sorted(arg_to_scheduler.keys())
UpperCAmelCase__						:	List[str]	     = '{' + ', '.join(arg_to_scheduler_choices) + '}'






class UpperCAmelCase  (		pl.LightningModule  ):





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				argparse.Namespace     ,     lowerCAmelCase_	:				Optional[int]=None     ,     lowerCAmelCase_	:				List[Any]="base"     ,     lowerCAmelCase_	:				Union[str, Any]=None     ,     lowerCAmelCase_	:				str=None     ,     lowerCAmelCase_	:				Tuple=None     ,     **lowerCAmelCase_	:				List[str]     ,     ):

      """simple docstring"""
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(lowerCAmelCase_	)
      _A:    Tuple						     =			0
      _A:    List[str]						     =			Path(self.hparams.output_dir	)
      _A:    str						     =			self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
         _A:    int						     =			AutoConfig.from_pretrained(
             self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,     **({'''num_labels''': num_labels} if num_labels is not None else {})     ,     cache_dir=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      else:
         _A:    PretrainedConfig						     =			config

      _A:    Union[str, Any]						     =			('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
         if getattr(self.hparams     ,     lowerCAmelCase_     ,     lowerCAmelCase_	):
            assert hasattr(self.config     ,     lowerCAmelCase_	), F"""model config doesn't have a `{p}` attribute"""
            setattr(self.config     ,     lowerCAmelCase_     ,     getattr(self.hparams     ,     lowerCAmelCase_	)	)

      if tokenizer is None:
         _A:    Dict						     =			AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,     cache_dir=lowerCAmelCase_     ,     )
      else:
         _A:    PreTrainedTokenizer						     =			tokenizer
      _A:    List[Any]						     =			MODEL_MODES[mode]
      if model is None:
         _A:    Union[str, Any]						     =			self.model_type.from_pretrained(
             self.hparams.model_name_or_path     ,     from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path	)     ,     config=self.config     ,     cache_dir=lowerCAmelCase_     ,     )
      else:
         _A:    str						     =			model


   def 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				int     ,     **lowerCAmelCase_	:				Union[str, Any]	):

      """simple docstring"""
      _A:    Tuple						     =			self.model_type.from_pretrained(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[str]	):

      """simple docstring"""
      _A:    Optional[Any]						     =			arg_to_scheduler[self.hparams.lr_scheduler]
      _A:    Dict						     =			get_schedule_func(
          self.opt     ,     num_warmup_steps=self.hparams.warmup_steps     ,     num_training_steps=self.total_steps()	)
      _A:    Optional[Any]						     =			{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler


   def 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      _A:    List[Any]						     =			self.model
      _A:    List[str]						     =			['''bias''', '''LayerNorm.weight''']
      _A:    Dict						     =			[
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': self.hparams.weight_decay,
          },
          {
              '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
              '''weight_decay''': 0.0,
          },
      ]
      if self.hparams.adafactor:
         _A:    int						     =			Adafactor(
             lowerCAmelCase_     ,     lr=self.hparams.learning_rate     ,     scale_parameter=lowerCAmelCase_     ,     relative_step=lowerCAmelCase_	)

      else:
         _A:    str						     =			AdamW(
             lowerCAmelCase_     ,     lr=self.hparams.learning_rate     ,     eps=self.hparams.adam_epsilon	)
      _A:    List[Any]						     =			optimizer

      _A:    int						     =			self.get_lr_scheduler()

      return [optimizer], [scheduler]


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Optional[int]     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      return self.validation_step(lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				Any	):

      """simple docstring"""
      return self.validation_end(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      _A:    List[Any]						     =			max(1     ,     self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _A:    Tuple						     =			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      if stage == "test":
         _A:    Union[str, Any]						     =			len(self.test_dataloader().dataset	)
      else:
         _A:    Any						     =			self.get_dataloader('''train'''     ,     self.hparams.train_batch_size     ,     shuffle=lowerCAmelCase_	)
         _A:    str						     =			len(self.train_dataloader().dataset	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      raise NotImplementedError('''You must implement this for your task'''	)


   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      return self.train_loader


   def 					__magic_name__							(   self	:				Union[str, Any]	):

      """simple docstring"""
      return self.get_dataloader('''dev'''     ,     self.hparams.eval_batch_size     ,     shuffle=lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      return self.get_dataloader('''test'''     ,     self.hparams.eval_batch_size     ,     shuffle=lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      return os.path.join(
          self.hparams.data_dir     ,     '''cached_{}_{}_{}'''.format(
              lowerCAmelCase_     ,     list(filter(lowerCAmelCase_     ,     self.hparams.model_name_or_path.split('''/'''	)	)	).pop()     ,     str(self.hparams.max_seq_length	)     ,     )     ,     )


   @pl.utilities.rank_zero_only
   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				Dict[str, Any]	):

      """simple docstring"""
      _A:    str						     =			self.output_dir.joinpath('''best_tfmr'''	)
      _A:    Dict						     =			self.step_count
      self.model.save_pretrained(lowerCAmelCase_	)
      self.tokenizer.save_pretrained(lowerCAmelCase_	)


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				int     ,     lowerCAmelCase_	:				Union[str, Any]	):

      """simple docstring"""
      parser.add_argument(
          '''--model_name_or_path'''     ,     default=lowerCAmelCase_     ,     type=lowerCAmelCase_     ,     required=lowerCAmelCase_     ,     help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,     )
      parser.add_argument(
          '''--config_name'''     ,     default=''''''     ,     type=lowerCAmelCase_     ,     help='''Pretrained config name or path if not the same as model_name'''	)
      parser.add_argument(
          '''--tokenizer_name'''     ,     default=lowerCAmelCase_     ,     type=lowerCAmelCase_     ,     help='''Pretrained tokenizer name or path if not the same as model_name'''     ,     )
      parser.add_argument(
          '''--cache_dir'''     ,     default=''''''     ,     type=lowerCAmelCase_     ,     help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,     )
      parser.add_argument(
          '''--encoder_layerdrop'''     ,     type=lowerCAmelCase_     ,     help='''Encoder layer dropout probability (Optional). Goes into model.config'''     ,     )
      parser.add_argument(
          '''--decoder_layerdrop'''     ,     type=lowerCAmelCase_     ,     help='''Decoder layer dropout probability (Optional). Goes into model.config'''     ,     )
      parser.add_argument(
          '''--dropout'''     ,     type=lowerCAmelCase_     ,     help='''Dropout probability (Optional). Goes into model.config'''     ,     )
      parser.add_argument(
          '''--attention_dropout'''     ,     type=lowerCAmelCase_     ,     help='''Attention dropout probability (Optional). Goes into model.config'''     ,     )
      parser.add_argument('''--learning_rate'''     ,     default=5e-5     ,     type=lowerCAmelCase_     ,     help='''The initial learning rate for Adam.'''	)
      parser.add_argument(
          '''--lr_scheduler'''     ,     default='''linear'''     ,     choices=lowerCAmelCase_     ,     metavar=lowerCAmelCase_     ,     type=lowerCAmelCase_     ,     help='''Learning rate scheduler'''     ,     )
      parser.add_argument('''--weight_decay'''     ,     default=0.0     ,     type=lowerCAmelCase_     ,     help='''Weight decay if we apply some.'''	)
      parser.add_argument('''--adam_epsilon'''     ,     default=1e-8     ,     type=lowerCAmelCase_     ,     help='''Epsilon for Adam optimizer.'''	)
      parser.add_argument('''--warmup_steps'''     ,     default=0     ,     type=lowerCAmelCase_     ,     help='''Linear warmup over warmup_steps.'''	)
      parser.add_argument('''--num_workers'''     ,     default=4     ,     type=lowerCAmelCase_     ,     help='''kwarg passed to DataLoader'''	)
      parser.add_argument('''--num_train_epochs'''     ,     dest='''max_epochs'''     ,     default=3     ,     type=lowerCAmelCase_	)
      parser.add_argument('''--train_batch_size'''     ,     default=3_2     ,     type=lowerCAmelCase_	)
      parser.add_argument('''--eval_batch_size'''     ,     default=3_2     ,     type=lowerCAmelCase_	)
      parser.add_argument('''--adafactor'''     ,     action='''store_true'''	)






class UpperCAmelCase  (		pl.Callback  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
         pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class UpperCAmelCase  (		pl.Callback  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				int	):

      """simple docstring"""
      _A:    Optional[Any]						     =			trainer.lr_schedulers[0]['''scheduler''']
      _A:    Any						     =			{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[str]     ,     lowerCAmelCase_	:				pl.Trainer     ,     lowerCAmelCase_	:				pl.LightningModule	):

      """simple docstring"""
      rank_zero_info('''***** Validation results *****'''	)
      _A:    Optional[Any]						     =			trainer.callback_metrics
      # Log results
      for key in sorted(lowerCAmelCase_	):
         if key not in ["log", "progress_bar"]:
            rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase_     ,     str(metrics[key]	)	)	)


   def 					__magic_name__							(   self	:				List[str]     ,     lowerCAmelCase_	:				pl.Trainer     ,     lowerCAmelCase_	:				pl.LightningModule	):

      """simple docstring"""
      rank_zero_info('''***** Test results *****'''	)
      _A:    int						     =			trainer.callback_metrics
      # Log and save results to file
      _A:    Any						     =			os.path.join(pl_module.hparams.output_dir     ,     '''test_results.txt'''	)
      with open(lowerCAmelCase_     ,     '''w'''	) as writer:
         for key in sorted(lowerCAmelCase_	):
            if key not in ["log", "progress_bar"]:
               rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase_     ,     str(metrics[key]	)	)	)
               writer.write('''{} = {}\n'''.format(lowerCAmelCase_     ,     str(metrics[key]	)	)	)






def     lowerCamelCase__				(   a						,						a ) ->  None:
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       '''--output_dir'''						,						default=a						,						type=a						,						required=a						,						help='''The output directory where the model predictions and checkpoints will be written.'''						,						)
   parser.add_argument(
       '''--fp16'''						,						action='''store_true'''						,						help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''						,						)

   parser.add_argument(
       '''--fp16_opt_level'''						,						type=a						,						default='''O2'''						,						help=(
           '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
           '''See details at https://nvidia.github.io/apex/amp.html'''
       )						,						)
   parser.add_argument('''--n_tpu_cores'''						,						dest='''tpu_cores'''						,						type=a )
   parser.add_argument('''--max_grad_norm'''						,						dest='''gradient_clip_val'''						,						default=1.0						,						type=a						,						help='''Max gradient norm''' )
   parser.add_argument('''--do_train'''						,						action='''store_true'''						,						help='''Whether to run training.''' )
   parser.add_argument('''--do_predict'''						,						action='''store_true'''						,						help='''Whether to run predictions on the test set.''' )
   parser.add_argument(
       '''--gradient_accumulation_steps'''						,						dest='''accumulate_grad_batches'''						,						type=a						,						default=1						,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''						,						)
   parser.add_argument('''--seed'''						,						type=a						,						default=42						,						help='''random seed for initialization''' )
   parser.add_argument(
       '''--data_dir'''						,						default=a						,						type=a						,						required=a						,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''						,						)






def     lowerCamelCase__				(   a						,						a						,						a=None						,						a=True						,						a=None						,						a=[]						,						a=None						,						a=None						,						**a						,						) ->  Tuple:
   pl.seed_everything(args.seed )

   # init model
   _A:    Any						     =			Path(model.hparams.output_dir )
   odir.mkdir(exist_ok=a )

   # add custom checkpoints
   if checkpoint_callback is None:
      _A:    Any						     =			pl.callbacks.ModelCheckpoint(
          filepath=args.output_dir						,						prefix='''checkpoint'''						,						monitor='''val_loss'''						,						mode='''min'''						,						save_top_k=1 )
   if early_stopping_callback:
      extra_callbacks.append(a )
   if logging_callback is None:
      _A:    Optional[int]						     =			LoggingCallback()

   _A:    List[Any]						     =			{}

   # TODO: remove with PyTorch 1.6 since pl uses native amp
   if args.fpaa:
      _A:    Any						     =			16
      # train_params["amp_level"] = args.fp16_opt_level

   if args.gpus > 1:
      _A:    int						     =			'''auto'''  # "ddp"
      _A:    List[str]						     =			'''ddp'''

   _A:    str						     =			args.accumulate_grad_batches
   _A:    List[Any]						     =			None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
   _A:    List[str]						     =			'''auto'''

   _A:    Tuple						     =			pl.Trainer.from_argparse_args(
       a						,						weights_summary=a						,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,						logger=a						,						**a						,						)

   if args.do_train:
      trainer.fit(a )

   return trainer





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)

UpperCAmelCase__						:	Optional[Any]	     = {
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Optional[Any]												=       '''blenderbot'''
   __UpperCamelCase						:       Optional[int]												=       ['''past_key_values''']
   __UpperCamelCase						:       Union[str, Any]												=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


   def __init__(   self	:				Any     ,     lowerCAmelCase_	:				List[str]=8_0_0_8     ,     lowerCAmelCase_	:				Any=1_2_8     ,     lowerCAmelCase_	:				List[str]=2     ,     lowerCAmelCase_	:				Dict=1_0_2_4_0     ,     lowerCAmelCase_	:				Dict=3_2     ,     lowerCAmelCase_	:				Union[str, Any]=2_4     ,     lowerCAmelCase_	:				Union[str, Any]=1_0_2_4_0     ,     lowerCAmelCase_	:				Union[str, Any]=3_2     ,     lowerCAmelCase_	:				Optional[Any]=0.0     ,     lowerCAmelCase_	:				Optional[int]=0.0     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				Optional[Any]=True     ,     lowerCAmelCase_	:				Union[str, Any]="gelu"     ,     lowerCAmelCase_	:				Optional[int]=2_5_6_0     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				Any=0.0     ,     lowerCAmelCase_	:				Union[str, Any]=0.0     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				List[Any]=1     ,     lowerCAmelCase_	:				List[str]=False     ,     lowerCAmelCase_	:				str=0     ,     lowerCAmelCase_	:				str=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				List[str]=3     ,     lowerCAmelCase_	:				int=2     ,     **lowerCAmelCase_	:				Tuple     ,     ):

      """simple docstring"""
      _A:    Any						     =			vocab_size
      _A:    Optional[int]						     =			max_position_embeddings
      _A:    int						     =			d_model
      _A:    Optional[int]						     =			encoder_ffn_dim
      _A:    Optional[int]						     =			encoder_layers
      _A:    int						     =			encoder_attention_heads
      _A:    Optional[Any]						     =			decoder_ffn_dim
      _A:    Dict						     =			decoder_layers
      _A:    int						     =			decoder_attention_heads
      _A:    List[str]						     =			dropout
      _A:    Union[str, Any]						     =			attention_dropout
      _A:    Tuple						     =			activation_dropout
      _A:    Dict						     =			activation_function
      _A:    Optional[Any]						     =			init_std
      _A:    Optional[int]						     =			encoder_layerdrop
      _A:    Any						     =			decoder_layerdrop
      _A:    List[str]						     =			use_cache
      _A:    Dict						     =			encoder_layers
      _A:    int						     =			scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     eos_token_id=lowerCAmelCase_     ,     is_encoder_decoder=lowerCAmelCase_     ,     decoder_start_token_id=lowerCAmelCase_     ,     encoder_no_repeat_ngram_size=lowerCAmelCase_     ,     forced_eos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Any	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A:    int						     =			{0: '''batch'''}
            _A:    Optional[int]						     =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            _A:    List[Any]						     =			{0: '''batch''', 1: '''decoder_sequence'''}
            _A:    Tuple						     =			{0: '''batch''', 1: '''decoder_sequence'''}
         if self.use_past:
            self.fill_with_past_key_values_(lowerCAmelCase_     ,     direction='''inputs'''	)
      elif self.task == "causal-lm":
         _A:    str						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]	)
         if self.use_past:
            _A     ,    _A:    Tuple						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    str						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    List[str]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
         _A:    List[Any]						     =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
             ]	)

      return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    List[str]						     =			super().outputs
      else:
         _A:    Tuple						     =			super(lowerCAmelCase_     ,     self	).outputs
         if self.use_past:
            _A     ,    _A:    Optional[int]						     =			self.num_layers
            for i in range(lowerCAmelCase_	):
               _A:    Union[str, Any]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
               _A:    Optional[int]						     =			{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      # Generate decoder inputs
      _A:    int						     =			seq_length if not self.use_past else 1
      _A:    Any						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Optional[int]						     =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      _A:    Union[str, Any]						     =			dict(**lowerCAmelCase_     ,     **lowerCAmelCase_	)

      if self.use_past:
         if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
         else:
            import torch
         _A     ,    _A:    str						     =			common_inputs['''input_ids'''].shape
         _A:    Optional[Any]						     =			common_inputs['''decoder_input_ids'''].shape[1]
         _A     ,    _A:    Optional[Any]						     =			self.num_attention_heads
         _A:    List[str]						     =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			decoder_seq_length
         _A:    Tuple						     =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         _A:    Union[str, Any]						     =			torch.cat(
             [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_	)]     ,     dim=1	)
         _A:    List[Any]						     =			[]
         _A     ,    _A:    Union[str, Any]						     =			self.num_layers

         for _ in range(lowerCAmelCase_	):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                    torch.zeros(lowerCAmelCase_	),
                )	)
      return common_inputs


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)

      if self.use_past:
         if not is_torch_available():
            raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
         else:
            import torch
         _A     ,    _A:    Optional[int]						     =			common_inputs['''input_ids'''].shape
         _A:    Union[str, Any]						     =			seqlen
         _A     ,    _A:    Tuple						     =			self.num_layers
         _A     ,    _A:    Dict						     =			self.num_attention_heads
         _A:    Tuple						     =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         _A:    List[Any]						     =			common_inputs['''attention_mask'''].dtype
         _A:    Optional[int]						     =			torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase_     ,     lowerCAmelCase_     ,     dtype=lowerCAmelCase_	)]     ,     dim=1	)
         _A:    Tuple						     =			[
             (torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	)) for _ in range(lowerCAmelCase_	)
         ]
      return common_inputs


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _A:    Optional[int]						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_batch     ,     num_token_to_add=0	)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      _A:    int						     =			tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
      _A:    Any						     =			compute_effective_axis_dimension(
          lowerCAmelCase_     ,     fixed_dimension=OnnxConfig.default_fixed_sequence     ,     num_token_to_add=lowerCAmelCase_	)

      # Generate dummy inputs according to compute batch and sequence
      _A:    Tuple						     =			[''' '''.join([tokenizer.unk_token]	) * seq_length] * batch_size
      _A:    Any						     =			dict(tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_	)	)
      return common_inputs


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				PreTrainedTokenizer     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				int = -1     ,     lowerCAmelCase_	:				bool = False     ,     lowerCAmelCase_	:				Optional[TensorType] = None     ,     ):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    Union[str, Any]						     =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      elif self.task == "causal-lm":
         _A:    Optional[Any]						     =			self._generate_dummy_inputs_for_causal_lm(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)
      else:
         _A:    Tuple						     =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCAmelCase_     ,     batch_size=lowerCAmelCase_     ,     seq_length=lowerCAmelCase_     ,     is_pair=lowerCAmelCase_     ,     framework=lowerCAmelCase_	)

      return common_inputs


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tuple     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				List[str]	):

      """simple docstring"""
      if self.task in ["default", "seq2seq-lm"]:
         _A:    int						     =			super()._flatten_past_key_values_(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      else:
         _A:    Optional[int]						     =			super(lowerCAmelCase_     ,     self	)._flatten_past_key_values_(
             lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Mapping[str, Mapping[int, str]]     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      if direction not in ["inputs", "outputs"]:
         raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""	)

      _A:    Tuple						     =			'''past_key_values''' if direction == '''inputs''' else '''present'''
      _A     ,    _A:    int						     =			self.num_layers

      _A:    int						     =			'''past_encoder_sequence'''
      _A:    int						     =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

      for i in range(lowerCAmelCase_	):
         _A:    Optional[int]						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    Dict						     =			{0: '''batch''', 2: decoder_sequence}
         _A:    int						     =			{0: '''batch''', 2: encoder_sequence}
         _A:    Optional[int]						     =			{0: '''batch''', 2: encoder_sequence}



