
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 							A__	(			pl.LightningModule):
      def __init__(   self					,						__magic_name__					,						__magic_name__=None					,						__magic_name__="base"					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__					,						):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(__magic_name__   )
       lowerCamelCase	:			List[Any]           =   0
       lowerCamelCase	:			Optional[Any]           =   Path(self.hparams.output_dir   )
       lowerCamelCase	:			Optional[int]           =   self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
        lowerCamelCase	:			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=__magic_name__					,						**__magic_name__					,						)
       else:
        lowerCamelCase	:			PretrainedConfig           =   config

       lowerCamelCase	:			int           =   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
       for p in extra_model_params:
        if getattr(self.hparams					,						__magic_name__					,						__magic_name__   ):
         assert hasattr(self.config					,						__magic_name__   ), F'''model config doesn\'t have a `{p}` attribute'''
         setattr(self.config					,						__magic_name__					,						getattr(self.hparams					,						__magic_name__   )   )

       if tokenizer is None:
        lowerCamelCase	:			List[Any]           =   AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,						cache_dir=__magic_name__					,						)
       else:
        lowerCamelCase	:			PreTrainedTokenizer           =   tokenizer
       lowerCamelCase	:			Any           =   MODEL_MODES[mode]
       if model is None:
        lowerCamelCase	:			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=__magic_name__					,						)
       else:
        lowerCamelCase	:			int           =   model
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       lowerCamelCase	:			Dict           =   self.model_type.from_pretrained(*__magic_name__					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   arg_to_scheduler[self.hparams.lr_scheduler]
       lowerCamelCase	:			List[Any]           =   get_schedule_func(
           self.opt					,						num_warmup_steps=self.hparams.warmup_steps					,						num_training_steps=self.total_steps()   )
       lowerCamelCase	:			int           =   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
       return scheduler
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   self.model
       lowerCamelCase	:			Tuple           =   ["""bias""", """LayerNorm.weight"""]
       lowerCamelCase	:			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:
        lowerCamelCase	:			List[str]           =   Adafactor(
            __magic_name__					,						lr=self.hparams.learning_rate					,						scale_parameter=__magic_name__					,						relative_step=__magic_name__   )

       else:
        lowerCamelCase	:			List[str]           =   AdamW(
            __magic_name__					,						lr=self.hparams.learning_rate					,						eps=self.hparams.adam_epsilon   )
       lowerCamelCase	:			Tuple           =   optimizer

       lowerCamelCase	:			int           =   self.get_lr_scheduler()

       return [optimizer], [scheduler]
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       return self.validation_step(__magic_name__					,						__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       return self.validation_end(__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			str           =   max(1					,						self.hparams.gpus   )  # TODO: consider num_tpu_cores
       lowerCamelCase	:			List[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 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if mode == "test":
        lowerCamelCase	:			Dict           =   len(self.test_dataloader().dataset   )
       else:
        lowerCamelCase	:			str           =   self.get_dataloader("""train"""					,						self.hparams.train_batch_size					,						shuffle=__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   len(self.train_dataloader().dataset   )
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__ = False   ):
       raise NotImplementedError("""You must implement this for your task"""   )
      def 		UpperCamelCase__							(   self   ):
       return self.train_loader
      def 		UpperCamelCase__							(   self   ):
       return self.get_dataloader("""dev"""					,						self.hparams.eval_batch_size					,						shuffle=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       return self.get_dataloader("""test"""					,						self.hparams.eval_batch_size					,						shuffle=__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       return os.path.join(
           self.hparams.data_dir					,						"""cached_{}_{}_{}""".format(
               __magic_name__					,						list(filter(__magic_name__					,						self.hparams.model_name_or_path.split("""/"""   )   )   ).pop()					,						str(self.hparams.max_seq_length   )					,						)					,						)
      @pl.utilities.rank_zero_only
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			Tuple           =   self.output_dir.joinpath("""best_tfmr"""   )
       lowerCamelCase	:			Any           =   self.step_count
       self.model.save_pretrained(__magic_name__   )
       self.tokenizer.save_pretrained(__magic_name__   )






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





class 							A__	(			pl.Callback):
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			Optional[int]           =   trainer.lr_schedulers[0]["""scheduler"""]
       lowerCamelCase	:			List[str]           =   {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
       pl_module.logger.log_metrics(__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       rank_zero_info("""***** Validation results *****"""   )
       lowerCamelCase	:			str           =   trainer.callback_metrics
       # Log results
       for key in sorted(__magic_name__   ):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("""{} = {}\n""".format(__magic_name__					,						str(metrics[key]   )   )   )






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





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 #  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 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=True,			lowerCamelCase=[],			lowerCamelCase=None,			lowerCamelCase=None,			**lowerCamelCase,			):
 pl.seed_everything(args.seed							)

 # init model
 lowerCamelCase	:			List[str]           =   Path(model.hparams.output_dir							)
 odir.mkdir(exist_ok=lowerCamelCase							)

 # add custom checkpoints
 if checkpoint_callback is None:
  lowerCamelCase	:			int           =   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:
  lowerCamelCase	:			Tuple           =   LoggingCallback()

 lowerCamelCase	:			Dict           =   {}

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

 if args.gpus > 1:
  lowerCamelCase	:			int           =   """ddp"""

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

 lowerCamelCase	:			Optional[int]           =   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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase           ={
    """configuration_musicgen""": [
        """MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MusicgenConfig""",
        """MusicgenDecoderConfig""",
    ],
    """processing_musicgen""": ["""MusicgenProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MusicgenForConditionalGeneration""",
          """MusicgenForCausalLM""",
          """MusicgenModel""",
          """MusicgenPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_musicgen import (
          MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MusicgenConfig,
          MusicgenDecoderConfig,
      )
      from .processing_musicgen import MusicgenProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_musicgen import (
                MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MusicgenForCausalLM,
                MusicgenForConditionalGeneration,
                MusicgenModel,
                MusicgenPreTrainedModel,
            )

else:
      import sys

      _lowerCamelCase           =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




