"""simple docstring"""






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:int  )				-> str:
						a_							:			int     		=					filter(lambda _SCREAMING_SNAKE_CASE  : p.requires_grad		,			model.parameters()  )
						a_							:			Tuple     		=					sum([np.prod(p.size()  ) for p in model_parameters]  )
						return params


UpperCamelCase     					=     logging.getLogger(__name__)
class   UpperCAmelCase__				(    pl.Callback    ):


	"""simple docstring"""







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	List[Any]:
							a_							:			Union[str, Any]     		=					{f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
							pl_module.logger.log_metrics(UpperCamelCase__ )







	@rank_zero_only
	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE=True )    ->	None:
							logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
							a_							:			Dict     		=					trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
							# Log results
							a_							:			Tuple     		=					Path(pl_module.hparams.output_dir )
							if type_path == "test":
													a_							:			Dict     		=					od / '''test_results.txt'''
													a_							:			Tuple     		=					od / '''test_generations.txt'''
							else:
													# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
													# If people want this it will be easy enough to add back.
													a_							:			str     		=					od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
													a_							:			int     		=					od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
													results_file.parent.mkdir(exist_ok=UpperCamelCase__ )
													generations_file.parent.mkdir(exist_ok=UpperCamelCase__ )
							with open(UpperCamelCase__       ,     "a+" ) as writer:
													for key in sorted(UpperCamelCase__ ):
																			if key in ["log", "progress_bar", "preds"]:
																									continue
																			a_							:			Union[str, Any]     		=					metrics[key]
																			if isinstance(UpperCamelCase__       ,     torch.Tensor ):
																									a_							:			str     		=					val.item()
																			a_							:			Any     		=					f'''{key}: {val:.6f}\n'''
																			writer.write(UpperCamelCase__ )

							if not save_generations:
													return

							if "preds" in metrics:
													a_							:			List[Any]     		=					'''\n'''.join(metrics["preds"] )
													generations_file.open("w+" ).write(UpperCamelCase__ )







	@rank_zero_only
	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	int:







							try:
													a_							:			Optional[Any]     		=					pl_module.model.model.num_parameters()
							except AttributeError:
													a_							:			Dict     		=					pl_module.model.num_parameters()

							a_							:			Optional[int]     		=					count_trainable_parameters(UpperCamelCase__ )
							# mp stands for million parameters
							trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )







	@rank_zero_only
	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:
							save_json(pl_module.metrics       ,     pl_module.metrics_save_path )
							return self._write_logs(UpperCamelCase__       ,     UpperCamelCase__       ,     "test" )







	@rank_zero_only
	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )    ->	str:
							save_json(pl_module.metrics       ,     pl_module.metrics_save_path )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")




def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]		,			_SCREAMING_SNAKE_CASE	:Tuple=1		,			_SCREAMING_SNAKE_CASE	:Tuple=False  )				-> int:


						if metric == "rouge2":
												a_							:			List[str]     		=					'''{val_avg_rouge2:.4f}-{step_count}'''
						elif metric == "bleu":
												a_							:			Union[str, Any]     		=					'''{val_avg_bleu:.4f}-{step_count}'''
						elif metric == "loss":
												a_							:			List[str]     		=					'''{val_avg_loss:.4f}-{step_count}'''
						else:
												raise NotImplementedError(
												    F'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
												    " this function."  )

						a_							:			Dict     		=					ModelCheckpoint(
						    dirpath=SCREAMING_SNAKE_CASE__		,			filename=SCREAMING_SNAKE_CASE__		,			monitor=F'''val_{metric}'''		,			mode="min" if "loss" in metric else "max"		,			save_top_k=SCREAMING_SNAKE_CASE__		,			)
						return checkpoint_callback



def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[Any]		,			_SCREAMING_SNAKE_CASE	:int  )				-> Any:
						return EarlyStopping(
						    monitor=F'''val_{metric}'''		,			mode="min" if "loss" in metric else "max"		,			patience=SCREAMING_SNAKE_CASE__		,			verbose=SCREAMING_SNAKE_CASE__		,			)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase     					=     {
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BarkFineModel',
				    'BarkSemanticModel',
				    'BarkCoarseModel',
				    'BarkModel',
				    'BarkPreTrainedModel',
				    'BarkCausalModel',
				]

if TYPE_CHECKING:
				from .configuration_bark import (
				    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BarkCoarseConfig,
				    BarkConfig,
				    BarkFineConfig,
				    BarkSemanticConfig,
				)
				from .processing_bark import BarkProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bark import (
								    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BarkCausalModel,
								    BarkCoarseModel,
								    BarkFineModel,
								    BarkModel,
								    BarkPreTrainedModel,
								    BarkSemanticModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




