





"""simple docstring"""




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


lowerCamelCase_	:		Tuple         =   logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCamelCase_	:		List[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
lowerCamelCase_	:		int         =   {
    '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
}
lowerCamelCase_	:		str         =   sorted(arg_to_scheduler.keys())
lowerCamelCase_	:		Union[str, Any]         =   '{' + ', '.join(arg_to_scheduler_choices) + '}'

class 					_UpperCAmelCase    (  pl.LightningModule						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=None   , snake_case_="base"   , snake_case_=None   , snake_case_=None   , snake_case_=None   , **snake_case_   , ):

        """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_    :			str									=    Path(self.hparams.output_dir    )
        A_    :			Any									=    self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
          A_    :			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:
          A_    :			PretrainedConfig									=    config

        A_    :			Dict									=    ("""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_    :			Union[str, Any]									=    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_    :			Union[str, Any]									=    MODEL_MODES[mode]
        if model is None:
          A_    :			Tuple									=    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_    :			Any									=    model

      def    lowerCamelCase_     (	self   , *snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.model_type.from_pretrained(*_lowerCamelCase   , **_lowerCamelCase    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    arg_to_scheduler[self.hparams.lr_scheduler]
        A_    :			Optional[int]									=    get_schedule_func(
            self.opt   , num_warmup_steps=self.hparams.warmup_steps   , num_training_steps=self.total_steps()    )
        A_    :			Union[str, Any]									=    {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
        return scheduler

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    self.model
        A_    :			List[Any]									=    ["""bias""", """LayerNorm.weight"""]
        A_    :			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:
          A_    :			str									=    Adafactor(
              _lowerCamelCase   , lr=self.hparams.learning_rate   , scale_parameter=_lowerCamelCase   , relative_step=_lowerCamelCase    )

        else:
          A_    :			List[Any]									=    AdamW(
              _lowerCamelCase   , lr=self.hparams.learning_rate   , eps=self.hparams.adam_epsilon    )
        A_    :			List[Any]									=    optimizer

        A_    :			Tuple									=    self.get_lr_scheduler()

        return [optimizer], [scheduler]

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        return self.validation_step(_lowerCamelCase   , _lowerCamelCase    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return self.validation_end(_lowerCamelCase    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    max(1   , self.hparams.gpus    )  # TODO: consider num_tpu_cores
        A_    :			str									=    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if mode == "test":
          A_    :			Optional[int]									=    len(self.test_dataloader().dataset    )
        else:
          A_    :			str									=    self.get_dataloader('train'   , self.hparams.train_batch_size   , shuffle=_lowerCamelCase    )
          A_    :			Tuple									=    len(self.train_dataloader().dataset    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = False    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.train_loader

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.get_dataloader('dev'   , self.hparams.eval_batch_size   , shuffle=_lowerCamelCase    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.get_dataloader('test'   , self.hparams.eval_batch_size   , shuffle=_lowerCamelCase    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """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    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    self.output_dir.joinpath('best_tfmr'    )
        A_    :			int									=    self.step_count
        self.model.save_pretrained(_lowerCamelCase    )
        self.tokenizer.save_pretrained(_lowerCamelCase    )

      @staticmethod
      def    lowerCamelCase_     (	snake_case_   , snake_case_    ):

        """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    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			List[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    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        rank_zero_info('***** Validation results *****'    )
        A_    :			List[str]									=    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    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        rank_zero_info('***** Test results *****'    )
        A_    :			Tuple									=    trainer.callback_metrics
        # Log and save results to file
        A_    :			Optional[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 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  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 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=True					,   _UpperCAmelCase=[]					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   **_UpperCAmelCase					,   ):






  """simple docstring"""





  pl.seed_everything(args.seed   )

  # init model
  A_    :			Tuple									=    Path(model.hparams.output_dir   )
  odir.mkdir(exist_ok=a_   )

  # add custom checkpoints
  if checkpoint_callback is None:
    A_    :			List[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[Any]									=    LoggingCallback()

  A_    :			Any									=    {}

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

  if args.gpus > 1:
    A_    :			Optional[int]									=    """ddp"""

  A_    :			Tuple									=    args.accumulate_grad_batches
  A_    :			Tuple									=    extra_train_kwargs.get('accelerator'					,   a_   )
  A_    :			Optional[int]									=    extra_train_kwargs.get('profiler'					,   a_   )

  A_    :			Optional[Any]									=    pl.Trainer.from_argparse_args(
      a_					,   weights_summary=a_					,   callbacks=[logging_callback] + extra_callbacks					,   logger=a_					,   checkpoint_callback=a_					,   **a_					,   )

  if args.do_train:
    trainer.fit(a_   )

  return trainer




'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
