
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 						UpperCAmelCase	(		pl.LightningModule				):



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

          snake_case :     Tuple           =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
                 if getattr(self.hparams					,		snake_case__					,		snake_case__				):
                        assert hasattr(self.config					,		snake_case__				), f"""model config doesn't have a `{p}` attribute"""
                        setattr(self.config					,		snake_case__					,		getattr(self.hparams					,		snake_case__				)				)

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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]					,		*snake_case__	:      Union[str, Any]					,		**snake_case__	:      List[str]				)	->			Dict:


          '''simple docstring'''


          snake_case :     str           =   self.model_type.from_pretrained(*snake_case__					,		**snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			int:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     List[str]           =   self.model
          snake_case :     List[Any]           =   ["bias", "LayerNorm.weight"]
          snake_case :     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:
                 snake_case :     Union[str, Any]           =   Adafactor(
                     snake_case__					,		lr=self.hparams.learning_rate					,		scale_parameter=snake_case__					,		relative_step=snake_case__				)

          else:
                 snake_case :     Optional[int]           =   AdamW(
                     snake_case__					,		lr=self.hparams.learning_rate					,		eps=self.hparams.adam_epsilon				)
          snake_case :     Any           =   optimizer

          snake_case :     List[Any]           =   self.get_lr_scheduler()

          return [optimizer], [scheduler]



   def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      List[Any]					,		snake_case__	:      Optional[int]				)	->			Dict:


          '''simple docstring'''


          return self.validation_step(snake_case__					,		snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      Optional[int]				)	->			Any:


          '''simple docstring'''


          return self.validation_end(snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			int:


          '''simple docstring'''


          snake_case :     int           =   max(1					,		self.hparams.gpus				)  # TODO: consider num_tpu_cores
          snake_case :     Optional[Any]           =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      str				)	->			int:


          '''simple docstring'''


          if mode == "test":
                 snake_case :     Tuple           =   len(self.test_dataloader().dataset				)
          else:
                 snake_case :     Union[str, Any]           =   self.get_dataloader("train"					,		self.hparams.train_batch_size					,		shuffle=snake_case__				)
                 snake_case :     Any           =   len(self.train_dataloader().dataset				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      str					,		snake_case__	:      int					,		snake_case__	:      bool = False				)	->			List[str]:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			List[str]:


          '''simple docstring'''


          return self.train_loader



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          return self.get_dataloader("dev"					,		self.hparams.eval_batch_size					,		shuffle=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Optional[int]:


          '''simple docstring'''


          return self.get_dataloader("test"					,		self.hparams.eval_batch_size					,		shuffle=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      Optional[int]				)	->			Tuple:


          '''simple docstring'''


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



   @pl.utilities.rank_zero_only
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      Dict[str, Any]				)	->			None:


          '''simple docstring'''


          snake_case :     Any           =   self.output_dir.joinpath("best_tfmr"				)
          snake_case :     Union[str, Any]           =   self.step_count
          self.model.save_pretrained(snake_case__				)
          self.tokenizer.save_pretrained(snake_case__				)



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Any					,		snake_case__	:      List[Any]				)	->			int:


          '''simple docstring'''


          parser.add_argument(
              "--model_name_or_path"					,		default=snake_case__					,		type=snake_case__					,		required=snake_case__					,		help="Path to pretrained model or model identifier from huggingface.co/models"					,		)
          parser.add_argument(
              "--config_name"					,		default=""					,		type=snake_case__					,		help="Pretrained config name or path if not the same as model_name"				)
          parser.add_argument(
              "--tokenizer_name"					,		default=snake_case__					,		type=snake_case__					,		help="Pretrained tokenizer name or path if not the same as model_name"					,		)
          parser.add_argument(
              "--cache_dir"					,		default=""					,		type=snake_case__					,		help="Where do you want to store the pre-trained models downloaded from huggingface.co"					,		)
          parser.add_argument(
              "--encoder_layerdrop"					,		type=snake_case__					,		help="Encoder layer dropout probability (Optional). Goes into model.config"					,		)
          parser.add_argument(
              "--decoder_layerdrop"					,		type=snake_case__					,		help="Decoder layer dropout probability (Optional). Goes into model.config"					,		)
          parser.add_argument(
              "--dropout"					,		type=snake_case__					,		help="Dropout probability (Optional). Goes into model.config"					,		)
          parser.add_argument(
              "--attention_dropout"					,		type=snake_case__					,		help="Attention dropout probability (Optional). Goes into model.config"					,		)
          parser.add_argument("--learning_rate"					,		default=5e-5					,		type=snake_case__					,		help="The initial learning rate for Adam."				)
          parser.add_argument(
              "--lr_scheduler"					,		default="linear"					,		choices=snake_case__					,		metavar=snake_case__					,		type=snake_case__					,		help="Learning rate scheduler"					,		)
          parser.add_argument("--weight_decay"					,		default=0.0					,		type=snake_case__					,		help="Weight decay if we apply some."				)
          parser.add_argument("--adam_epsilon"					,		default=1e-8					,		type=snake_case__					,		help="Epsilon for Adam optimizer."				)
          parser.add_argument("--warmup_steps"					,		default=0					,		type=snake_case__					,		help="Linear warmup over warmup_steps."				)
          parser.add_argument("--num_workers"					,		default=4					,		type=snake_case__					,		help="kwarg passed to DataLoader"				)
          parser.add_argument("--num_train_epochs"					,		dest="max_epochs"					,		default=3					,		type=snake_case__				)
          parser.add_argument("--train_batch_size"					,		default=32					,		type=snake_case__				)
          parser.add_argument("--eval_batch_size"					,		default=32					,		type=snake_case__				)
          parser.add_argument("--adafactor"					,		action="store_true"				)






class 						UpperCAmelCase	(		pl.Callback				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      Dict					,		snake_case__	:      Union[str, Any]				)	->			Tuple:


          '''simple docstring'''


          snake_case :     Optional[int]           =   trainer.lr_schedulers[0]["scheduler"]
          snake_case :     Dict           =   {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
          pl_module.logger.log_metrics(snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      pl.Trainer					,		snake_case__	:      pl.LightningModule				)	->			Union[str, Any]:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      pl.Trainer					,		snake_case__	:      pl.LightningModule				)	->			Optional[int]:


          '''simple docstring'''


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



def     UpperCamelCase      (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				Optional[Any]					):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           "--output_dir"       ,      default=__lowerCamelCase       ,      type=__lowerCamelCase       ,      required=__lowerCamelCase       ,      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=__lowerCamelCase       ,      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=__lowerCamelCase					)
       parser.add_argument("--max_grad_norm"       ,      dest="gradient_clip_val"       ,      default=1.0       ,      type=__lowerCamelCase       ,      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=__lowerCamelCase       ,      default=1       ,      help="Number of updates steps to accumulate before performing a backward/update pass."       ,      )
       parser.add_argument("--seed"       ,      type=__lowerCamelCase       ,      default=42       ,      help="random seed for initialization"					)
       parser.add_argument(
           "--data_dir"       ,      default=__lowerCamelCase       ,      type=__lowerCamelCase       ,      required=__lowerCamelCase       ,      help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."       ,      )




def     UpperCamelCase      (					__lowerCamelCase  :				BaseTransformer       ,      __lowerCamelCase  :				argparse.Namespace       ,      __lowerCamelCase  :				Dict=None       ,      __lowerCamelCase  :				Dict=True       ,      __lowerCamelCase  :				Any=[]       ,      __lowerCamelCase  :				Union[str, Any]=None       ,      __lowerCamelCase  :				int=None       ,      **__lowerCamelCase  :				str       ,      ):
       pl.seed_everything(args.seed					)

       # init model
       snake_case :     str           =   Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=__lowerCamelCase					)

       # add custom checkpoints
       if checkpoint_callback is None:
              snake_case :     Tuple           =   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(__lowerCamelCase					)
       if logging_callback is None:
              snake_case :     List[Any]           =   LoggingCallback()

       snake_case :     Dict           =   {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              snake_case :     str           =   16
              snake_case :     Tuple           =   args.fpaa_opt_level

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

       snake_case :     Optional[Any]           =   args.accumulate_grad_batches
       snake_case :     int           =   extra_train_kwargs.get("accelerator"       ,      __lowerCamelCase					)
       snake_case :     str           =   extra_train_kwargs.get("profiler"       ,      __lowerCamelCase					)

       snake_case :     List[str]           =   pl.Trainer.from_argparse_args(
           __lowerCamelCase       ,      weights_summary=__lowerCamelCase       ,      callbacks=[logging_callback] + extra_callbacks       ,      logger=__lowerCamelCase       ,      checkpoint_callback=__lowerCamelCase       ,      **__lowerCamelCase       ,      )

       if args.do_train:
              trainer.fit(__lowerCamelCase					)

       return trainer







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

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

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





