






"""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


_UpperCamelCase				:    Tuple     =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase				:    Dict     =						{
    '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				:    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
}
_UpperCamelCase				:    Tuple     =						sorted(arg_to_scheduler.keys())
_UpperCamelCase				:    Optional[Any]     =						'{' + ', '.join(arg_to_scheduler_choices) + '}'






class 						snake_case  (  pl.LightningModule       ):





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


      '''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(A			)
      a     :			List[Any]				=   0
      a     :			Dict				=   Path(self.hparams.output_dir			)
      a     :			Tuple				=   self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        a     :			str				=   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=A				,		**A				,		)
      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				,		A				,		A			):
          assert hasattr(self.config				,		A			), F'''model config doesn\'t have a `{p}` attribute'''
          setattr(self.config				,		A				,		getattr(self.hparams				,		A			)			)

      if tokenizer is None:
        a     :			str				=   AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,		cache_dir=A				,		)
      else:
        a     :			PreTrainedTokenizer				=   tokenizer
      a     :			Union[str, Any]				=   MODEL_MODES[mode]
      if model is None:
        a     :			Optional[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=A				,		)
      else:
        a     :			List[Any]				=   model





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		*A	:					List[Any]				,		**A	:					Optional[int]			):


      '''simple docstring'''




      a     :			Optional[int]				=   self.model_type.from_pretrained(*A				,		**A			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




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





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


      '''simple docstring'''




      a     :			List[str]				=   self.model
      a     :			List[str]				=   ['bias', 'LayerNorm.weight']
      a     :			int				=   [
          {
              '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     :			Union[str, Any]				=   Adafactor(
            A				,		lr=self.hparams.learning_rate				,		scale_parameter=A				,		relative_step=A			)

      else:
        a     :			Tuple				=   AdamW(
            A				,		lr=self.hparams.learning_rate				,		eps=self.hparams.adam_epsilon			)
      a     :			Optional[int]				=   optimizer

      a     :			str				=   self.get_lr_scheduler()

      return [optimizer], [scheduler]





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					Union[str, Any]				,		A	:					Optional[Any]			):


      '''simple docstring'''




      return self.validation_step(A				,		A			)





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					str			):


      '''simple docstring'''




      return self.validation_end(A			)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[str]				=   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	:					Any				,		A	:					Tuple			):


      '''simple docstring'''




      if stage == "test":
        a     :			Any				=   len(self.test_dataloader().dataset			)
      else:
        a     :			List[str]				=   self.get_dataloader('train'				,		self.hparams.train_batch_size				,		shuffle=A			)
        a     :			Dict				=   len(self.train_dataloader().dataset			)





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					str				,		A	:					int				,		A	:					bool = False			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self.train_loader





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					int				,		A	:					Dict			):


      '''simple docstring'''




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





    @pl.utilities.rank_zero_only
    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					Dict[str, Any]			):


      '''simple docstring'''




      a     :			Any				=   self.output_dir.joinpath('best_tfmr'			)
      a     :			Tuple				=   self.step_count
      self.model.save_pretrained(A			)
      self.tokenizer.save_pretrained(A			)





    @staticmethod
    def 				lowerCamelCase__      (							A	:					Optional[Any]				,		A	:					List[Any]			):


      '''simple docstring'''




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






class 						snake_case  (  pl.Callback       ):





    def 				lowerCamelCase__      (							self	:					Tuple				,		A	:					Optional[int]				,		A	:					str			):


      '''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 						snake_case  (  pl.Callback       ):





    def 				lowerCamelCase__      (							self	:					Optional[Any]				,		A	:					Optional[Any]				,		A	:					Optional[int]			):


      '''simple docstring'''




      a     :			Dict				=   trainer.lr_schedulers[0]['scheduler']
      a     :			Optional[int]				=   {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
      pl_module.logger.log_metrics(A			)





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					pl.Trainer				,		A	:					pl.LightningModule			):


      '''simple docstring'''




      rank_zero_info('***** Validation results *****'			)
      a     :			int				=   trainer.callback_metrics
      # Log results
      for key in sorted(A			):
        if key not in ["log", "progress_bar"]:
          rank_zero_info('{} = {}\n'.format(A				,		str(metrics[key]			)			)			)





    def 				lowerCamelCase__      (							self	:					int				,		A	:					pl.Trainer				,		A	:					pl.LightningModule			):


      '''simple docstring'''




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




def 			snake_case  (A_      :List[Any]	,			A_      :int      ):







  '''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=4_2	,			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 			snake_case  (A_      :BaseTransformer	,			A_      :argparse.Namespace	,			A_      :Union[str, Any]=None	,			A_      :Dict=True	,			A_      :Any=None	,			A_      :Union[str, Any]=[]	,			A_      :Dict=None	,			A_      :str=None	,			**A_      :Optional[int]	,			):







  '''simple docstring'''

  pl.seed_everything(args.seed      )

  # init model
  a     :			Union[str, Any]				=   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     :			List[Any]				=   LoggingCallback()

  a     :			Tuple				=   {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    a     :			str				=   1_6
    # train_params["amp_level"] = args.fp16_opt_level

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

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

  a     :			int				=   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












"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=A			)





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3




