


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      lowerCAmelCase__       (				pl.LightningModule):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase="base" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				int:
            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      :       Union[str, Any]				    = Path(self.hparams.output_dir)
            _A      :       str				    = self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _A      :       List[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      :       Optional[int]				    = ("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      :       Dict				    = 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      :       List[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=__lowerCamelCase ,						)
            else:
                   _A      :       Any				    = model


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       List[str]				    = self.model_type.from_pretrained(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Tuple				    = 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      :       Union[str, Any]				    = {"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = self.model
            _A      :       Optional[int]				    = ["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)
                    ],  # check this named paramters
                    "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      :       Optional[int]				    = 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      :       Union[str, Any]				    = optimizer

            _A      :       Optional[Any]				    = self.get_lr_scheduler()

            return [optimizer], [scheduler]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Dict:
            return self.validation_step(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[Any]:
            return self.validation_end(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       int				    = max(1 ,						self.hparams.gpus)  # TODO: consider num_tpu_cores
            _A      :       Union[str, 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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            if stage == "test":
                   _A      :       Tuple				    = len(self.test_dataloader().dataset)
            else:
                   _A      :       List[str]				    = self.get_dataloader("train" ,						self.hparams.train_batch_size ,						shuffle=__lowerCamelCase)
                   _A      :       List[str]				    = len(self.train_dataloader().dataset)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = False)			->				Any:
            raise NotImplementedError("You must implement this for your task")


     def  _lowerCamelCase (						self)			->				Any:
            return self.train_loader


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.get_dataloader("dev" ,						self.hparams.eval_batch_size ,						shuffle=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.get_dataloader("test" ,						self.hparams.eval_batch_size ,						shuffle=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            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 ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.output_dir.joinpath("best_tfmr")
            _A      :       Optional[int]				    = self.step_count
            self.model.save_pretrained(__lowerCamelCase)
            self.tokenizer.save_pretrained(__lowerCamelCase)


     @staticmethod
     def  _lowerCamelCase (						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            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=str(Path(__lowerCamelCase).parent / "test_run" / "cache") ,						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      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            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      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Union[str, Any]:
            # print(pl_module.model.rag)
            for name, param in pl_module.model.rag.named_parameters():
                   if param.grad is None:
                          print(__lowerCamelCase)







class      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       int				    = trainer.lr_schedulers[0]["scheduler"]
            _A      :       Union[str, Any]				    = {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
            pl_module.logger.log_metrics(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            rank_zero_info("***** Validation results *****")
            _A      :       Union[str, Any]				    = 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 ,						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            rank_zero_info("***** Test results *****")
            _A      :       int				    = 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(__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__     :  List[str]				,							UpperCamelCase__     :  int						):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           "--output_dir"				,							default=str(Path(UpperCamelCase__						).parent / "test_run" / "model_checkpoints"						)				,							type=UpperCamelCase__				,							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=UpperCamelCase__				,							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=UpperCamelCase__						)
       parser.add_argument("--max_grad_norm"				,							dest="gradient_clip_val"				,							default=1.0				,							type=UpperCamelCase__				,							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=UpperCamelCase__				,							default=1				,							help="Number of updates steps to accumulate before performing a backward/update pass."				,							)
       parser.add_argument("--seed"				,							type=UpperCamelCase__				,							default=42				,							help="random seed for initialization"						)
       parser.add_argument(
           "--data_dir"				,							default=str(Path(UpperCamelCase__						).parent / "test_run" / "dummy-train-data"						)				,							type=UpperCamelCase__				,							help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,							)






def 							_UpperCAmelCase				(UpperCamelCase__     :  BaseTransformer				,							UpperCamelCase__     :  argparse.Namespace				,							UpperCamelCase__     :  str=None				,							UpperCamelCase__     :  Dict=True				,							UpperCamelCase__     :  List[Any]=[]				,							UpperCamelCase__     :  Any=None				,							UpperCamelCase__     :  Optional[Any]=None				,							**UpperCamelCase__     :  Union[str, Any]				,							):
       pl.seed_everything(args.seed						)

       # init model
       _A      :       str				    = Path(model.hparams.output_dir						)
       odir.mkdir(exist_ok=UpperCamelCase__						)

       # add custom checkpoints
       if checkpoint_callback is None:
              _A      :       Optional[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(UpperCamelCase__						)
       if logging_callback is None:
              _A      :       List[Any]				    = LoggingCallback()

       _A      :       int				    = {}

       if args.fpaa:
              _A      :       int				    = 16

       if args.gpus > 1:
              _A      :       str				    = "auto"
              _A      :       Optional[int]				    = "ddp"

       _A      :       List[str]				    = args.accumulate_grad_batches
       _A      :       str				    = None
       _A      :       Union[str, Any]				    = "auto"

       _A      :       List[str]				    = pl.Trainer.from_argparse_args(
           UpperCamelCase__				,							weights_summary=UpperCamelCase__				,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,							logger=UpperCamelCase__				,							val_check_interval=1				,							num_sanity_val_steps=2				,							**UpperCamelCase__				,							)

       if args.do_train:
              trainer.fit(UpperCamelCase__						)

       else:
              print("RAG modeling tests with new set functions successfuly executed!"						)
       return trainer


'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger("transformers.models.speecht5")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

	_snake_case   						=		checkpoint['input_conv.weight_g']
	_snake_case   						=		checkpoint['input_conv.weight_v']
	_snake_case   						=		checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates	)	):
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_g']
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_v']
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.bias']

	for i in range(len(config.upsample_rates	) * len(config.resblock_kernel_sizes	)	):
		for j in range(len(config.resblock_dilation_sizes	)	):
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

	_snake_case   						=		checkpoint['output_conv.1.weight_g']
	_snake_case   						=		checkpoint['output_conv.1.weight_v']
	_snake_case   						=		checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=None			,				__A=None			,				)       ->	List[Any]:
	if config_path is not None:
		_snake_case   						=		SpeechTaHifiGanConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		SpeechTaHifiGanConfig()

	_snake_case   						=		SpeechTaHifiGan(__A	)

	_snake_case   						=		torch.load(__A	)
	load_weights(orig_checkpoint['model']['generator']			,				__A			,				__A	)

	_snake_case   						=		np.load(__A	)
	_snake_case   						=		stats[0].reshape(-1	)
	_snake_case   						=		stats[1].reshape(-1	)
	_snake_case   						=		torch.from_numpy(__A	).float()
	_snake_case   						=		torch.from_numpy(__A	).float()

	model.save_pretrained(__A	)

	if repo_id:
		print('Pushing to the hub...'	)
		model.push_to_hub(__A	)


if __name__ == "__main__":
					lowercase    :							Dict      =					argparse.ArgumentParser()
					parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
					parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
					)

					lowercase    :							List[Any]      =					parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)

