




'''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_	 =		logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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







class 	lowercase_					(				pl.LightningModule     ):


   """simple docstring"""

   def __init__(					self      :			Tuple						,   __lowerCamelCase      :			argparse.Namespace						,   __lowerCamelCase      :			Optional[int]=None						,   __lowerCamelCase      :			Any="base"						,   __lowerCamelCase      :			Dict=None						,   __lowerCamelCase      :			int=None						,   __lowerCamelCase      :			Any=None						,   **__lowerCamelCase      :			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(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   0
      _SCREAMING_SNAKE_CASE =   Path(self.hparams.output_dir					)
      _SCREAMING_SNAKE_CASE =   self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
         _SCREAMING_SNAKE_CASE =   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:
         _SCREAMING_SNAKE_CASE =   config

      _SCREAMING_SNAKE_CASE =   ("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:
         _SCREAMING_SNAKE_CASE =   AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,   cache_dir=__lowerCamelCase						,   )
      else:
         _SCREAMING_SNAKE_CASE =   tokenizer
      _SCREAMING_SNAKE_CASE =   MODEL_MODES[mode]
      if model is None:
         _SCREAMING_SNAKE_CASE =   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:
         _SCREAMING_SNAKE_CASE =   model



   def 							lowerCAmelCase_    (					self      :			Tuple						,   *__lowerCamelCase      :			Dict						,   **__lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_type.from_pretrained(*__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model
      _SCREAMING_SNAKE_CASE =   ["bias", "LayerNorm.weight"]
      _SCREAMING_SNAKE_CASE =   [
          {
              "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:
         _SCREAMING_SNAKE_CASE =   Adafactor(
             __lowerCamelCase						,   lr=self.hparams.learning_rate						,   scale_parameter=__lowerCamelCase						,   relative_step=__lowerCamelCase					)

      else:
         _SCREAMING_SNAKE_CASE =   AdamW(
             __lowerCamelCase						,   lr=self.hparams.learning_rate						,   eps=self.hparams.adam_epsilon					)
      _SCREAMING_SNAKE_CASE =   optimizer

      _SCREAMING_SNAKE_CASE =   self.get_lr_scheduler()

      return [optimizer], [scheduler]



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

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



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]					):

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



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   max(1						,   self.hparams.gpus					)  # TODO: consider num_tpu_cores
      _SCREAMING_SNAKE_CASE =   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      :			Tuple						,   __lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      if stage == "test":
         _SCREAMING_SNAKE_CASE =   len(self.test_dataloader().dataset					)
      else:
         _SCREAMING_SNAKE_CASE =   self.get_dataloader("train"						,   self.hparams.train_batch_size						,   shuffle=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   len(self.train_dataloader().dataset					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			bool = False					):

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



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

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



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

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



   def 							lowerCAmelCase_    (					self      :			int					):

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



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			str					):

      """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      :			List[Any]						,   __lowerCamelCase      :			Dict[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.output_dir.joinpath("best_tfmr"					)
      _SCREAMING_SNAKE_CASE =   self.step_count
      self.model.save_pretrained(__lowerCamelCase					)
      self.tokenizer.save_pretrained(__lowerCamelCase					)



   @staticmethod
   def 							lowerCAmelCase_    (					__lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Tuple					):

      """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 	lowercase_					(				pl.Callback     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			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 	lowercase_					(				pl.Callback     ):


   """simple docstring"""

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

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



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			pl.Trainer						,   __lowerCamelCase      :			pl.LightningModule					):

      """simple docstring"""
      rank_zero_info("***** Validation results *****"					)
      _SCREAMING_SNAKE_CASE =   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      :			str						,   __lowerCamelCase      :			pl.Trainer						,   __lowerCamelCase      :			pl.LightningModule					):

      """simple docstring"""
      rank_zero_info("***** Test results *****"					)
      _SCREAMING_SNAKE_CASE =   trainer.callback_metrics
      # Log and save results to file
      _SCREAMING_SNAKE_CASE =   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_				(							__A       :				List[Any] ,  __A       :				Any						)				->				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 				SCREAMING_SNAKE_CASE_				(							__A       :				BaseTransformer ,  __A       :				argparse.Namespace ,  __A       :				List[str]=None ,  __A       :				int=True ,  __A       :				List[Any]=None ,  __A       :				List[Any]=[] ,  __A       :				str=None ,  __A       :				Optional[int]=None ,  **__A       :				Optional[int] ,  )				->				Optional[Any]:
   pl.seed_everything(args.seed						)

   # init model
   _SCREAMING_SNAKE_CASE =   Path(model.hparams.output_dir						)
   odir.mkdir(exist_ok=__A						)

   # add custom checkpoints
   if checkpoint_callback is None:
      _SCREAMING_SNAKE_CASE =   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:
      _SCREAMING_SNAKE_CASE =   LoggingCallback()

   _SCREAMING_SNAKE_CASE =   {}

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

   if args.gpus > 1:
      _SCREAMING_SNAKE_CASE =   "auto"  # "ddp"
      _SCREAMING_SNAKE_CASE =   "ddp"

   _SCREAMING_SNAKE_CASE =   args.accumulate_grad_batches
   _SCREAMING_SNAKE_CASE =   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
   _SCREAMING_SNAKE_CASE =   "auto"

   _SCREAMING_SNAKE_CASE =   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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @patch("diffusers.utils.hub_utils.get_full_repo_name"					)
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Mock					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "full_repo_name"
      with TemporaryDirectory() as tmpdir:
         # Dummy args values
         _SCREAMING_SNAKE_CASE =   Mock()
         _SCREAMING_SNAKE_CASE =   tmpdir
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   "hub_token"
         _SCREAMING_SNAKE_CASE =   "dataset_name"
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0_0
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   0.0_2
         _SCREAMING_SNAKE_CASE =   0.0_3
         _SCREAMING_SNAKE_CASE =   0.0_0_0_5
         _SCREAMING_SNAKE_CASE =   0.0_0_0_0_0_1
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   1_0
         _SCREAMING_SNAKE_CASE =   0.0_0_1
         _SCREAMING_SNAKE_CASE =   0.1
         _SCREAMING_SNAKE_CASE =   0.2
         _SCREAMING_SNAKE_CASE =   True

         # Model card mush be rendered and saved
         diffusers.utils.hub_utils.create_model_card(__lowerCamelCase						,   model_name="model_name"					)
         self.assertTrue((Path(__lowerCamelCase					) / "README.md").is_file()					)

