




'''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


__A						:							Dict							=  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__A						:							Optional[Any]							=  {
    """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
__A						:							List[Any]							=  {
    """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
}
__A						:							List[Any]							=  sorted(arg_to_scheduler.keys())
__A						:							Tuple							=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class 			UpperCAmelCase_      (   pl.LightningModule       ):




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	argparse.Namespace       ,     a       :	Optional[int]=None       ,     a       :	int="base"       ,     a       :	Optional[int]=None       ,     a       :	Any=None       ,     a       :	Dict=None       ,     **a       :	List[str]       ,     )			->					Dict:
										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			)
										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=a       ,     **a       ,     )
										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       ,     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:
													SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,     cache_dir=a       ,     )
										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=a       ,     )
										else:
													SCREAMING_SNAKE_CASE           =					model





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     *a       :	Tuple       ,     **a       :	List[Any]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.model_type.from_pretrained(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[int]:
										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    _UpperCAmelCase     (					self       :	Dict			)			->					List[str]:
										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			)
										        ],  # 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:
													SCREAMING_SNAKE_CASE           =					Adafactor(
													    a       ,     lr=self.hparams.learning_rate       ,     scale_parameter=a       ,     relative_step=a			)

										else:
													SCREAMING_SNAKE_CASE           =					AdamW(
													    a       ,     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    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Tuple       ,     a       :	str			)			->					Optional[Any]:
										return self.validation_step(a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Union[str, Any]			)			->					Union[str, Any]:
										return self.validation_end(a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					int:
										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    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	List[str]			)			->					Any:
										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=a			)
													SCREAMING_SNAKE_CASE           =					len(self.train_dataloader().dataset			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	str       ,     a       :	int       ,     a       :	bool = False			)			->					List[str]:
										raise NotImplementedError("""You must implement this for your task"""			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					str:
										return self.train_loader





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					str:
										return self.get_dataloader("""dev"""       ,     self.hparams.eval_batch_size       ,     shuffle=a			)





							def    _UpperCAmelCase     (					self       :	int			)			->					Any:
										return self.get_dataloader("""test"""       ,     self.hparams.eval_batch_size       ,     shuffle=a			)





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Tuple			)			->					str:
										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    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Dict[str, Any]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.output_dir.joinpath("""best_tfmr"""			)
										SCREAMING_SNAKE_CASE           =					self.step_count
										self.model.save_pretrained(a			)
										self.tokenizer.save_pretrained(a			)







							@staticmethod
							def    _UpperCAmelCase     (					a       :	Tuple       ,     a       :	Union[str, Any]			)			->					Tuple:
										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=str(Path(a			).parent / """test_run""" / """cache"""			)       ,     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=32       ,     type=a			)
										parser.add_argument("""--eval_batch_size"""       ,     default=32       ,     type=a			)
										parser.add_argument("""--adafactor"""       ,     action="""store_true"""			)





class 			UpperCAmelCase_      (   pl.Callback       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Optional[Any]       ,     a       :	Optional[Any]			)			->					Tuple:
										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 			UpperCAmelCase_      (   pl.Callback       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	Optional[Any]       ,     a       :	Dict			)			->					Optional[int]:
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
													if param.grad is None:
																print(a			)





class 			UpperCAmelCase_      (   pl.Callback       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	int       ,     a       :	List[Any]			)			->					int:
										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(a			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	pl.Trainer       ,     a       :	pl.LightningModule			)			->					Optional[Any]:
										rank_zero_info("""***** Validation results *****"""			)
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	int       ,     a       :	pl.Trainer       ,     a       :	pl.LightningModule			)			->					Tuple:
										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(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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			parser.add_argument(
			    """--output_dir"""					,	default=str(Path(SCREAMING_SNAKE_CASE   ).parent / """test_run""" / """model_checkpoints"""   )					,	type=SCREAMING_SNAKE_CASE					,	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=SCREAMING_SNAKE_CASE					,	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=SCREAMING_SNAKE_CASE   )
			parser.add_argument("""--max_grad_norm"""					,	dest="""gradient_clip_val"""					,	default=1.0					,	type=SCREAMING_SNAKE_CASE					,	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=SCREAMING_SNAKE_CASE					,	default=1					,	help="""Number of updates steps to accumulate before performing a backward/update pass."""					,	)
			parser.add_argument("""--seed"""					,	type=SCREAMING_SNAKE_CASE					,	default=42					,	help="""random seed for initialization"""   )
			parser.add_argument(
			    """--data_dir"""					,	default=str(Path(SCREAMING_SNAKE_CASE   ).parent / """test_run""" / """dummy-train-data"""   )					,	type=SCREAMING_SNAKE_CASE					,	help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""					,	)




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=True					,	SCREAMING_SNAKE_CASE=[]					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None					,	**SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''
			pl.seed_everything(args.seed   )

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

			# 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(SCREAMING_SNAKE_CASE   )
			if logging_callback is None:
						SCREAMING_SNAKE_CASE           =					LoggingCallback()

			SCREAMING_SNAKE_CASE           =					{}

			if args.fpaa:
						SCREAMING_SNAKE_CASE           =					16

			if args.gpus > 1:
						SCREAMING_SNAKE_CASE           =					"""auto"""
						SCREAMING_SNAKE_CASE           =					"""ddp"""

			SCREAMING_SNAKE_CASE           =					args.accumulate_grad_batches
			SCREAMING_SNAKE_CASE           =					None
			SCREAMING_SNAKE_CASE           =					"""auto"""

			SCREAMING_SNAKE_CASE           =					pl.Trainer.from_argparse_args(
			    SCREAMING_SNAKE_CASE					,	weights_summary=SCREAMING_SNAKE_CASE					,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,	logger=SCREAMING_SNAKE_CASE					,	val_check_interval=1					,	num_sanity_val_steps=2					,	**SCREAMING_SNAKE_CASE					,	)

			if args.do_train:
						trainer.fit(SCREAMING_SNAKE_CASE   )

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












import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							List[Any]							=  logging.get_logger(__name__)

__A						:							Tuple							=  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''encodec'''





							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,     a       :	Union[str, Any]=24_000       ,     a       :	List[Any]=1       ,     a       :	str=False       ,     a       :	List[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=128       ,     a       :	int=32       ,     a       :	int=1       ,     a       :	Dict=[8, 5, 4, 2]       ,     a       :	List[str]="weight_norm"       ,     a       :	str=7       ,     a       :	int=7       ,     a       :	Optional[int]=3       ,     a       :	Optional[int]=2       ,     a       :	Optional[int]=True       ,     a       :	Union[str, Any]="reflect"       ,     a       :	Dict=2       ,     a       :	Union[str, Any]=2       ,     a       :	Optional[Any]=1.0       ,     a       :	List[Any]=1_024       ,     a       :	int=None       ,     a       :	Dict=True       ,     **a       :	Tuple       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					target_bandwidths
										SCREAMING_SNAKE_CASE           =					sampling_rate
										SCREAMING_SNAKE_CASE           =					audio_channels
										SCREAMING_SNAKE_CASE           =					normalize
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_filters
										SCREAMING_SNAKE_CASE           =					num_residual_layers
										SCREAMING_SNAKE_CASE           =					upsampling_ratios
										SCREAMING_SNAKE_CASE           =					norm_type
										SCREAMING_SNAKE_CASE           =					kernel_size
										SCREAMING_SNAKE_CASE           =					last_kernel_size
										SCREAMING_SNAKE_CASE           =					residual_kernel_size
										SCREAMING_SNAKE_CASE           =					dilation_growth_rate
										SCREAMING_SNAKE_CASE           =					use_causal_conv
										SCREAMING_SNAKE_CASE           =					pad_mode
										SCREAMING_SNAKE_CASE           =					compress
										SCREAMING_SNAKE_CASE           =					num_lstm_layers
										SCREAMING_SNAKE_CASE           =					trim_right_ratio
										SCREAMING_SNAKE_CASE           =					codebook_size
										SCREAMING_SNAKE_CASE           =					codebook_dim if codebook_dim is not None else hidden_size
										SCREAMING_SNAKE_CASE           =					use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""			)

										super().__init__(**a			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1       ,     int((1.0 - self.overlap) * self.chunk_length			)			)





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					np.prod(self.upsampling_ratios			)
										return math.ceil(self.sampling_rate / hop_length			)







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)






