



"""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 	snake_case__  (						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Any						      =filter(lambda __lowerCamelCase      : p.requires_grad				,						model.parameters()      )
			lowerCamelCase__						:						List[Any]						      =sum([np.prod(p.size()      ) for p in model_parameters]      )
			return params


_lowercase					:					List[str] 		=		logging.getLogger(__name__)

class 			__SCREAMING_SNAKE_CASE      ( pl.Callback  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Dict	)->   Union[str, Any]:
								lowerCamelCase__						:						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(lowerCamelCase	)



					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    pl.LightningModule,    lowerCamelCase				:    str,    lowerCamelCase				:    int=True	)->   None:
								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''	)
								lowerCamelCase__						:						Optional[int]						      =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
								lowerCamelCase__						:						str						      =Path(pl_module.hparams.output_dir	)
								if type_path == "test":
											lowerCamelCase__						:						Optional[Any]						      =od / '''test_results.txt'''
											lowerCamelCase__						:						Dict						      =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.
											lowerCamelCase__						:						int						      =od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											lowerCamelCase__						:						Any						      =od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=lowerCamelCase	)
											generations_file.parent.mkdir(exist_ok=lowerCamelCase	)
								with open(lowerCamelCase,    '''a+'''	) as writer:
											for key in sorted(lowerCamelCase	):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														lowerCamelCase__						:						List[Any]						      =metrics[key]
														if isinstance(lowerCamelCase,    torch.Tensor	):
																	lowerCamelCase__						:						List[str]						      =val.item()
														lowerCamelCase__						:						Any						      =F'''{key}: {val:.6f}\n'''
														writer.write(lowerCamelCase	)

								if not save_generations:
											return

								if "preds" in metrics:
											lowerCamelCase__						:						Optional[Any]						      ='''\n'''.join(metrics['''preds''']	)
											generations_file.open('''w+'''	).write(lowerCamelCase	)



					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    Optional[Any]	)->   List[str]:
								try:
											lowerCamelCase__						:						Union[str, Any]						      =pl_module.model.model.num_parameters()
								except AttributeError:
											lowerCamelCase__						:						Optional[Any]						      =pl_module.model.num_parameters()

								lowerCamelCase__						:						List[str]						      =count_trainable_parameters(lowerCamelCase	)
								# 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 						snake_case			(			self				:    Any,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    pl.LightningModule	)->   int:
								save_json(pl_module.metrics,    pl_module.metrics_save_path	)
								return self._write_logs(lowerCamelCase,    lowerCamelCase,    '''test'''	)




					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    Dict	)->   Union[str, Any]:
								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 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Any=1				,						__lowerCamelCase							:       Optional[int]=False      ):
			"""simple docstring"""
			if metric == "rouge2":
						lowerCamelCase__						:						str						      ='''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						lowerCamelCase__						:						Dict						      ='''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "loss":
						lowerCamelCase__						:						Dict						      ='''{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.'''      )

			lowerCamelCase__						:						Optional[Any]						      =ModelCheckpoint(
			    dirpath=__lowerCamelCase				,						filename=__lowerCamelCase				,						monitor=f'''val_{metric}'''				,						mode='''min''' if '''loss''' in metric else '''max'''				,						save_top_k=__lowerCamelCase				,						)
			return checkpoint_callback






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			return EarlyStopping(
			    monitor=f'''val_{metric}'''				,						mode='''min''' if '''loss''' in metric else '''max'''				,						patience=__lowerCamelCase				,						verbose=__lowerCamelCase				,						)









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					Any 		=		{
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Any 		=		[
		    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "UniSpeechSatForAudioFrameClassification",
		    "UniSpeechSatForCTC",
		    "UniSpeechSatForPreTraining",
		    "UniSpeechSatForSequenceClassification",
		    "UniSpeechSatForXVector",
		    "UniSpeechSatModel",
		    "UniSpeechSatPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					int 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




