


"""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_rag import save_json







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


__SCREAMING_SNAKE_CASE							:    List[Any]		=    logging.getLogger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if metric == "rouge2":
														snake_case_           =		"""{val_avg_rouge2:.4f}-{step_count}"""
							elif metric == "bleu":
														snake_case_           =		"""{val_avg_bleu:.4f}-{step_count}"""
							elif metric == "em":
														snake_case_           =		"""{val_avg_em:.4f}-{step_count}"""
							else:
														raise NotImplementedError(
														    f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
														    """ function."""				)

							snake_case_           =		ModelCheckpoint(
							    dirpath=_SCREAMING_SNAKE_CASE				, filename=_SCREAMING_SNAKE_CASE				, monitor=f"""val_{metric}"""				, mode="""max"""				, save_top_k=3				, every_n_epochs=1				, )
							return checkpoint_callback







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





class      __A     (pl.Callback):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		{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 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      pl.Trainer		,       UpperCAmelCase_							:      pl.LightningModule		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]=True       )					->None:



														"""simple docstring"""



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

														if not save_generations:
																					return

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



							@rank_zero_only
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[int]:



														"""simple docstring"""



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

														snake_case_           =		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 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      pl.Trainer		,       UpperCAmelCase_							:      pl.LightningModule       )					->List[Any]:



														"""simple docstring"""



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



							@rank_zero_only
							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      pl.Trainer		,       UpperCAmelCase_							:      Optional[int]       )					->int:



														"""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")







"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Any							      =    """mctct"""



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=8_065		,       UpperCAmelCase_							:      Tuple=1_536		,       UpperCAmelCase_							:      Optional[Any]=36		,       UpperCAmelCase_							:      int=6_144		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=384		,       UpperCAmelCase_							:      List[str]=920		,       UpperCAmelCase_							:      Any=1E-5		,       UpperCAmelCase_							:      Any=0.3		,       UpperCAmelCase_							:      Tuple="relu"		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Dict=0.3		,       UpperCAmelCase_							:      str=0.3		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=0		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      Tuple=0.3		,       UpperCAmelCase_							:      Optional[Any]=1		,       UpperCAmelCase_							:      Optional[Any]=(7,)		,       UpperCAmelCase_							:      Optional[Any]=(3,)		,       UpperCAmelCase_							:      List[str]=80		,       UpperCAmelCase_							:      Tuple=1		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]="sum"		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Any		,       )					->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		num_attention_heads
														snake_case_           =		attention_head_dim
														snake_case_           =		max_position_embeddings
														snake_case_           =		layer_norm_eps
														snake_case_           =		layerdrop
														snake_case_           =		hidden_act
														snake_case_           =		initializer_range
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		pad_token_id
														snake_case_           =		bos_token_id
														snake_case_           =		eos_token_id
														snake_case_           =		conv_glu_dim
														snake_case_           =		conv_dropout
														snake_case_           =		num_conv_layers
														snake_case_           =		input_feat_per_channel
														snake_case_           =		input_channels
														snake_case_           =		conv_channels
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# prevents config testing fail with exporting to json
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														if len(self.conv_kernel       ) != self.num_conv_layers:
																					raise ValueError(
																					    """Configuration for convolutional module is incorrect. """
																					    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
																					    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel       )}`, """
																					    F"""`config.num_conv_layers = {self.num_conv_layers}`."""       )



