





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_			(				UpperCamelCase_	)  ->		Dict:
       UpperCamelCase_ 							=	filter(lambda UpperCamelCase_	: p.requires_grad       ,		model.parameters()	)
       UpperCamelCase_ 							=	sum([np.prod(p.size()	) for p in model_parameters]	)
       return params


_UpperCAmelCase										=					logging.getLogger(__name__)



class 				_UpperCamelCase       (						pl.Callback  ):

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	{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 lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			pl.Trainer       , _SCREAMING_SNAKE_CASE:			pl.LightningModule       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Tuple=True		)							->			Optional[Any]:




              """simple docstring"""


              logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''		)
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	Path(pl_module.hparams.output_dir		)
              if type_path == "test":
                     UpperCamelCase_ 							=	od / "test_results.txt"
                     UpperCamelCase_ 							=	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.
                     UpperCamelCase_ 							=	od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
                     UpperCamelCase_ 							=	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
                            UpperCamelCase_ 							=	metrics[key]
                            if isinstance(__UpperCamelCase       , torch.Tensor		):
                                   UpperCamelCase_ 							=	val.item()
                            UpperCamelCase_ 							=	f'''{key}: {val:.6f}\n'''
                            writer.write(__UpperCamelCase		)

              if not save_generations:
                     return

              if "preds" in metrics:
                     UpperCamelCase_ 							=	"\n".join(metrics["preds"]		)
                     generations_file.open("w+"		).write(__UpperCamelCase		)

       @rank_zero_only
       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Tuple		)							->			int:




              """simple docstring"""


              try:
                     UpperCamelCase_ 							=	pl_module.model.model.num_parameters()
              except AttributeError:
                     UpperCamelCase_ 							=	pl_module.model.num_parameters()

              UpperCamelCase_ 							=	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 lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			pl.Trainer       , _SCREAMING_SNAKE_CASE:			pl.LightningModule		)							->			int:




              """simple docstring"""


              save_json(pl_module.metrics       , pl_module.metrics_save_path		)
              return self._write_logs(__UpperCamelCase       , __UpperCamelCase       , "test"		)



       @rank_zero_only
       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			pl.Trainer       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              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_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=1       ,		UpperCamelCase_=False	)  ->		Any:
       if metric == "rouge2":
              UpperCamelCase_ 							=	"{val_avg_rouge2:.4f}-{step_count}"
       elif metric == "bleu":
              UpperCamelCase_ 							=	"{val_avg_bleu:.4f}-{step_count}"
       elif metric == "loss":
              UpperCamelCase_ 							=	"{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."	)

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







def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       return EarlyStopping(
           monitor=F'''val_{metric}'''       ,		mode="min" if "loss" in metric else "max"       ,		patience=UpperCamelCase_       ,		verbose=UpperCamelCase_       ,		)








"""simple docstring"""
from typing import TYPE_CHECKING

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


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    '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

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


