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      		=					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase      		=					{
    '''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      		=					{
    '''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      		=					sorted(arg_to_scheduler.keys())
UpperCAmelCase      		=					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class  __lowerCAmelCase	(							pl.LightningModule    ):



 '''simple docstring'''

 def __init__(      self							:		Dict							,      lowerCamelCase							:		argparse.Namespace							,      lowerCamelCase							:		List[Any]=None							,      lowerCamelCase							:		List[str]="base"							,      lowerCamelCase							:		Union[str, Any]=None							,      lowerCamelCase							:		str=None							,      lowerCamelCase							:		Tuple=None							,      **lowerCamelCase							:		int							,      )			->				Any:
        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	)
        _UpperCamelCase  :							str	    =	0
        _UpperCamelCase  :							Optional[Any]	    =	Path(self.hparams.output_dir	)
        _UpperCamelCase  :							List[str]	    =	self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
               _UpperCamelCase  :							Optional[Any]	    =	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:
               _UpperCamelCase  :							PretrainedConfig	    =	config

        _UpperCamelCase  :							Optional[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:
               _UpperCamelCase  :							str	    =	AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,      cache_dir=lowerCamelCase							,      )
        else:
               _UpperCamelCase  :							PreTrainedTokenizer	    =	tokenizer
        _UpperCamelCase  :							Optional[int]	    =	MODEL_MODES[mode]
        if model is None:
               _UpperCamelCase  :							Optional[int]	    =	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:
               _UpperCamelCase  :							int	    =	model

 def        __snake_case       (      self							:		Dict							,      *lowerCamelCase							:		Dict							,      **lowerCamelCase							:		Union[str, Any]	)			->				Tuple:
        _UpperCamelCase  :							Dict	    =	self.model_type.from_pretrained(*lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							str	    =	arg_to_scheduler[self.hparams.lr_scheduler]
        _UpperCamelCase  :							Union[str, Any]	    =	get_schedule_func(
            self.opt							,      num_warmup_steps=self.hparams.warmup_steps							,      num_training_steps=self.total_steps()	)
        _UpperCamelCase  :							str	    =	{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
        return scheduler

 def        __snake_case       (      self							:		int	)			->				Optional[int]:
        _UpperCamelCase  :							Optional[int]	    =	self.model
        _UpperCamelCase  :							Union[str, Any]	    =	['bias', 'LayerNorm.weight']
        _UpperCamelCase  :							str	    =	[
            {
                '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:
               _UpperCamelCase  :							Tuple	    =	Adafactor(
                   lowerCamelCase							,      lr=self.hparams.learning_rate							,      scale_parameter=lowerCamelCase							,      relative_step=lowerCamelCase	)

        else:
               _UpperCamelCase  :							int	    =	AdamW(
                   lowerCamelCase							,      lr=self.hparams.learning_rate							,      eps=self.hparams.adam_epsilon	)
        _UpperCamelCase  :							str	    =	optimizer

        _UpperCamelCase  :							List[str]	    =	self.get_lr_scheduler()

        return [optimizer], [scheduler]

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Any	)			->				List[str]:
        return self.validation_step(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		Optional[Any]	)			->				str:
        return self.validation_end(lowerCamelCase	)

 def        __snake_case       (      self							:		str	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	max(1							,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
        _UpperCamelCase  :							Dict	    =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Tuple	)			->				Union[str, Any]:
        if mode == "test":
               _UpperCamelCase  :							Any	    =	len(self.test_dataloader().dataset	)
        else:
               _UpperCamelCase  :							List[Any]	    =	self.get_dataloader('train'							,      self.hparams.train_batch_size							,      shuffle=lowerCamelCase	)
               _UpperCamelCase  :							Optional[Any]	    =	len(self.train_dataloader().dataset	)

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		bool = False	)			->				List[str]:
        raise NotImplementedError('You must implement this for your task'	)

 def        __snake_case       (      self							:		List[str]	)			->				Tuple:
        return self.train_loader

 def        __snake_case       (      self							:		Optional[int]	)			->				List[Any]:
        return self.get_dataloader('dev'							,      self.hparams.eval_batch_size							,      shuffle=lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        return self.get_dataloader('test'							,      self.hparams.eval_batch_size							,      shuffle=lowerCamelCase	)

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		int	)			->				Optional[Any]:
        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        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Dict[str, Any]	)			->				None:
        _UpperCamelCase  :							List[str]	    =	self.output_dir.joinpath('best_tfmr'	)
        _UpperCamelCase  :							Optional[int]	    =	self.step_count
        self.model.save_pretrained(lowerCamelCase	)
        self.tokenizer.save_pretrained(lowerCamelCase	)

 @staticmethod
 def        __snake_case       (      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Optional[Any]	)			->				int:
        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  __lowerCAmelCase	(							pl.Callback    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Tuple	)			->				List[Any]:
        _UpperCamelCase  :							int	    =	trainer.lr_schedulers[0]['scheduler']
        _UpperCamelCase  :							str	    =	{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
        pl_module.logger.log_metrics(lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		pl.Trainer							,      lowerCamelCase							:		pl.LightningModule	)			->				List[str]:
        rank_zero_info('***** Validation results *****'	)
        _UpperCamelCase  :							Tuple	    =	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        __snake_case       (      self							:		int							,      lowerCamelCase							:		pl.Trainer							,      lowerCamelCase							:		pl.LightningModule	)			->				Tuple:
        rank_zero_info('***** Test results *****'	)
        _UpperCamelCase  :							Optional[int]	    =	trainer.callback_metrics
        # Log and save results to file
        _UpperCamelCase  :							int	    =	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 SCREAMING_SNAKE_CASE					(  lowercase_:						int		,    lowercase_:						List[Any]					):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '--output_dir'		,    default=lowercase_		,    type=lowercase_		,    required=lowercase_		,    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=lowercase_		,    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=lowercase_					)
       parser.add_argument('--max_grad_norm'		,    dest='gradient_clip_val'		,    default=1.0		,    type=lowercase_		,    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=lowercase_		,    default=1		,    help='Number of updates steps to accumulate before performing a backward/update pass.'		,    )
       parser.add_argument('--seed'		,    type=lowercase_		,    default=42		,    help='random seed for initialization'					)
       parser.add_argument(
           '--data_dir'		,    default=lowercase_		,    type=lowercase_		,    required=lowercase_		,    help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,    )





def SCREAMING_SNAKE_CASE					(  lowercase_:						BaseTransformer		,    lowercase_:						argparse.Namespace		,    lowercase_:						int=None		,    lowercase_:						Tuple=True		,    lowercase_:						int=[]		,    lowercase_:						Optional[Any]=None		,    lowercase_:						Dict=None		,    **lowercase_:						Any		,    ):
       pl.seed_everything(args.seed					)

       # init model
       _UpperCamelCase  :							Tuple	    =	Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=lowercase_					)

       # add custom checkpoints
       if checkpoint_callback is None:
              _UpperCamelCase  :							str	    =	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(lowercase_					)
       if logging_callback is None:
              _UpperCamelCase  :							Dict	    =	LoggingCallback()

       _UpperCamelCase  :							List[str]	    =	{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _UpperCamelCase  :							List[Any]	    =	16
              _UpperCamelCase  :							Dict	    =	args.fpaa_opt_level

       if args.gpus > 1:
              _UpperCamelCase  :							Dict	    =	'ddp'

       _UpperCamelCase  :							Union[str, Any]	    =	args.accumulate_grad_batches
       _UpperCamelCase  :							Tuple	    =	extra_train_kwargs.get('accelerator'		,    lowercase_					)
       _UpperCamelCase  :							str	    =	extra_train_kwargs.get('profiler'		,    lowercase_					)

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

       if args.do_train:
              trainer.fit(lowercase_					)

       return trainer











"""simple docstring"""


from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'Constraint',
           'ConstraintListState',
           'DisjunctiveConstraint',
           'PhrasalConstraint',
       ]
       lowercase_								=	[
           'BeamHypotheses',
           'BeamScorer',
           'BeamSearchScorer',
           'ConstrainedBeamSearchScorer',
       ]
       lowercase_								=	[
           'EpsilonLogitsWarper',
           'EtaLogitsWarper',
           'ForcedBOSTokenLogitsProcessor',
           'ForcedEOSTokenLogitsProcessor',
           'HammingDiversityLogitsProcessor',
           'InfNanRemoveLogitsProcessor',
           'LogitsProcessor',
           'LogitsProcessorList',
           'LogitsWarper',
           'MinLengthLogitsProcessor',
           'MinNewTokensLengthLogitsProcessor',
           'NoBadWordsLogitsProcessor',
           'NoRepeatNGramLogitsProcessor',
           'PrefixConstrainedLogitsProcessor',
           'RepetitionPenaltyLogitsProcessor',
           'SequenceBiasLogitsProcessor',
           'EncoderRepetitionPenaltyLogitsProcessor',
           'TemperatureLogitsWarper',
           'TopKLogitsWarper',
           'TopPLogitsWarper',
           'TypicalLogitsWarper',
           'EncoderNoRepeatNGramLogitsProcessor',
           'ExponentialDecayLengthPenalty',
           'LogitNormalization',
       ]
       lowercase_								=	[
           'MaxNewTokensCriteria',
           'MaxLengthCriteria',
           'MaxTimeCriteria',
           'StoppingCriteria',
           'StoppingCriteriaList',
           'validate_stopping_criteria',
       ]
       lowercase_								=	[
           'GenerationMixin',
           'top_k_top_p_filtering',
           'GreedySearchEncoderDecoderOutput',
           'GreedySearchDecoderOnlyOutput',
           'SampleEncoderDecoderOutput',
           'SampleDecoderOnlyOutput',
           'BeamSearchEncoderDecoderOutput',
           'BeamSearchDecoderOnlyOutput',
           'BeamSampleEncoderDecoderOutput',
           'BeamSampleDecoderOnlyOutput',
           'ContrastiveSearchEncoderDecoderOutput',
           'ContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TFForcedBOSTokenLogitsProcessor',
           'TFForcedEOSTokenLogitsProcessor',
           'TFLogitsProcessor',
           'TFLogitsProcessorList',
           'TFLogitsWarper',
           'TFMinLengthLogitsProcessor',
           'TFNoBadWordsLogitsProcessor',
           'TFNoRepeatNGramLogitsProcessor',
           'TFRepetitionPenaltyLogitsProcessor',
           'TFTemperatureLogitsWarper',
           'TFTopKLogitsWarper',
           'TFTopPLogitsWarper',
           'TFForceTokensLogitsProcessor',
           'TFSuppressTokensAtBeginLogitsProcessor',
           'TFSuppressTokensLogitsProcessor',
       ]
       lowercase_								=	[
           'TFGenerationMixin',
           'tf_top_k_top_p_filtering',
           'TFGreedySearchDecoderOnlyOutput',
           'TFGreedySearchEncoderDecoderOutput',
           'TFSampleEncoderDecoderOutput',
           'TFSampleDecoderOnlyOutput',
           'TFBeamSearchEncoderDecoderOutput',
           'TFBeamSearchDecoderOnlyOutput',
           'TFBeamSampleEncoderDecoderOutput',
           'TFBeamSampleDecoderOnlyOutput',
           'TFContrastiveSearchEncoderDecoderOutput',
           'TFContrastiveSearchDecoderOnlyOutput',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'FlaxForcedBOSTokenLogitsProcessor',
           'FlaxForcedEOSTokenLogitsProcessor',
           'FlaxLogitsProcessor',
           'FlaxLogitsProcessorList',
           'FlaxLogitsWarper',
           'FlaxMinLengthLogitsProcessor',
           'FlaxTemperatureLogitsWarper',
           'FlaxTopKLogitsWarper',
           'FlaxTopPLogitsWarper',
       ]
       lowercase_								=	[
           'FlaxGenerationMixin',
           'FlaxGreedySearchOutput',
           'FlaxSampleOutput',
           'FlaxBeamSearchOutput',
       ]

if TYPE_CHECKING:
       from .configuration_utils import GenerationConfig
       from .streamers import TextIteratorStreamer, TextStreamer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
              from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
              from .logits_process import (
                  EncoderNoRepeatNGramLogitsProcessor,
                  EncoderRepetitionPenaltyLogitsProcessor,
                  EpsilonLogitsWarper,
                  EtaLogitsWarper,
                  ExponentialDecayLengthPenalty,
                  ForcedBOSTokenLogitsProcessor,
                  ForcedEOSTokenLogitsProcessor,
                  HammingDiversityLogitsProcessor,
                  InfNanRemoveLogitsProcessor,
                  LogitNormalization,
                  LogitsProcessor,
                  LogitsProcessorList,
                  LogitsWarper,
                  MinLengthLogitsProcessor,
                  MinNewTokensLengthLogitsProcessor,
                  NoBadWordsLogitsProcessor,
                  NoRepeatNGramLogitsProcessor,
                  PrefixConstrainedLogitsProcessor,
                  RepetitionPenaltyLogitsProcessor,
                  SequenceBiasLogitsProcessor,
                  TemperatureLogitsWarper,
                  TopKLogitsWarper,
                  TopPLogitsWarper,
                  TypicalLogitsWarper,
              )
              from .stopping_criteria import (
                  MaxLengthCriteria,
                  MaxNewTokensCriteria,
                  MaxTimeCriteria,
                  StoppingCriteria,
                  StoppingCriteriaList,
                  validate_stopping_criteria,
              )
              from .utils import (
                  BeamSampleDecoderOnlyOutput,
                  BeamSampleEncoderDecoderOutput,
                  BeamSearchDecoderOnlyOutput,
                  BeamSearchEncoderDecoderOutput,
                  ContrastiveSearchDecoderOnlyOutput,
                  ContrastiveSearchEncoderDecoderOutput,
                  GenerationMixin,
                  GreedySearchDecoderOnlyOutput,
                  GreedySearchEncoderDecoderOutput,
                  SampleDecoderOnlyOutput,
                  SampleEncoderDecoderOutput,
                  top_k_top_p_filtering,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tf_logits_process import (
                  TFForcedBOSTokenLogitsProcessor,
                  TFForcedEOSTokenLogitsProcessor,
                  TFForceTokensLogitsProcessor,
                  TFLogitsProcessor,
                  TFLogitsProcessorList,
                  TFLogitsWarper,
                  TFMinLengthLogitsProcessor,
                  TFNoBadWordsLogitsProcessor,
                  TFNoRepeatNGramLogitsProcessor,
                  TFRepetitionPenaltyLogitsProcessor,
                  TFSuppressTokensAtBeginLogitsProcessor,
                  TFSuppressTokensLogitsProcessor,
                  TFTemperatureLogitsWarper,
                  TFTopKLogitsWarper,
                  TFTopPLogitsWarper,
              )
              from .tf_utils import (
                  TFBeamSampleDecoderOnlyOutput,
                  TFBeamSampleEncoderDecoderOutput,
                  TFBeamSearchDecoderOnlyOutput,
                  TFBeamSearchEncoderDecoderOutput,
                  TFContrastiveSearchDecoderOnlyOutput,
                  TFContrastiveSearchEncoderDecoderOutput,
                  TFGenerationMixin,
                  TFGreedySearchDecoderOnlyOutput,
                  TFGreedySearchEncoderDecoderOutput,
                  TFSampleDecoderOnlyOutput,
                  TFSampleEncoderDecoderOutput,
                  tf_top_k_top_p_filtering,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .flax_logits_process import (
                  FlaxForcedBOSTokenLogitsProcessor,
                  FlaxForcedEOSTokenLogitsProcessor,
                  FlaxLogitsProcessor,
                  FlaxLogitsProcessorList,
                  FlaxLogitsWarper,
                  FlaxMinLengthLogitsProcessor,
                  FlaxTemperatureLogitsWarper,
                  FlaxTopKLogitsWarper,
                  FlaxTopPLogitsWarper,
              )
              from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




