




"""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__ (				snake_case__				)						->					Union[str, Any]:
			lowerCamelCase			      =					filter(lambda snake_case__				: p.requires_grad       , model.parameters()				)
			lowerCamelCase			      =					sum([np.prod(p.size()				) for p in model_parameters]				)
			return params


lowerCAmelCase							:			Union[str, Any]     							=							logging.getLogger(__name__)



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			if metric == "rouge2":
						lowerCamelCase			      =					"""{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						lowerCamelCase			      =					"""{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						lowerCamelCase			      =					"""{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."""				)

			lowerCamelCase			      =					ModelCheckpoint(
			    dirpath=snake_case__       , filename=snake_case__       , monitor=F'val_{metric}'       , mode="""max"""       , save_top_k=3       , every_n_epochs=1       , )
			return checkpoint_callback



def     a__ (				snake_case__       , snake_case__				)						->					List[str]:
			return EarlyStopping(
			    monitor=F'val_{metric}'       , mode="""min""" if """loss""" in metric else """max"""       , patience=snake_case__       , verbose=snake_case__       , )






class     __magic_name__							(       pl.Callback				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					{f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
							pl_module.logger.log_metrics(_a )





				@rank_zero_only
				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a=True ):





							"""simple docstring"""
							logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
							lowerCamelCase			      =					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			      =					Path(pl_module.hparams.output_dir )
							if type_path == "test":
										lowerCamelCase			      =					od / """test_results.txt"""
										lowerCamelCase			      =					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			      =					od / f'{type_path}_results/{trainer.global_step:05d}.txt'
										lowerCamelCase			      =					od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
										results_file.parent.mkdir(exist_ok=_a )
										generations_file.parent.mkdir(exist_ok=_a )
							with open(_a		, """a+""" ) as writer:
										for key in sorted(_a ):
													if key in ["log", "progress_bar", "preds"]:
																continue
													lowerCamelCase			      =					metrics[key]
													if isinstance(_a		, torch.Tensor ):
																lowerCamelCase			      =					val.item()
													lowerCamelCase			      =					f'{key}: {val:.6f}\n'
													writer.write(_a )

							if not save_generations:
										return

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





				@rank_zero_only
				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							try:
										lowerCamelCase			      =					pl_module.model.model.num_parameters()
							except AttributeError:
										lowerCamelCase			      =					pl_module.model.num_parameters()

							lowerCamelCase			      =					count_trainable_parameters(_a )
							# 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		, _a		, _a ):





							"""simple docstring"""
							save_json(pl_module.metrics		, pl_module.metrics_save_path )
							return self._write_logs(_a		, _a		, """test""" )





				@rank_zero_only
				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""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"""




import functools
import operator

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


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[str]     							=							{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						"sew-d"





				def __init__(       self		, _a=32		, _a=768		, _a=12		, _a=12		, _a=3_072		, _a=2		, _a=512		, _a=256		, _a=True		, _a=True		, _a=("p2c", "c2p")		, _a="layer_norm"		, _a="gelu_python"		, _a=0.1		, _a=0.1		, _a=0.1		, _a=0.0		, _a=0.1		, _a=0.02		, _a=1e-7		, _a=1e-5		, _a="group"		, _a="gelu"		, _a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		, _a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		, _a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		, _a=False		, _a=128		, _a=16		, _a=True		, _a=0.05		, _a=10		, _a=2		, _a=0.0		, _a=10		, _a=0		, _a="mean"		, _a=False		, _a=False		, _a=256		, _a=0		, _a=1		, _a=2		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a		, pad_token_id=_a		, bos_token_id=_a		, eos_token_id=_a )
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					feat_extract_norm
							lowerCamelCase			      =					feat_extract_activation
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					conv_bias
							lowerCamelCase			      =					num_conv_pos_embeddings
							lowerCamelCase			      =					num_conv_pos_embedding_groups
							lowerCamelCase			      =					len(self.conv_dim )
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					squeeze_factor
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					position_buckets
							lowerCamelCase			      =					share_att_key
							lowerCamelCase			      =					relative_attention
							lowerCamelCase			      =					norm_rel_ebd
							lowerCamelCase			      =					list(_a )
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					hidden_dropout
							lowerCamelCase			      =					attention_dropout
							lowerCamelCase			      =					activation_dropout
							lowerCamelCase			      =					feat_proj_dropout
							lowerCamelCase			      =					final_dropout
							lowerCamelCase			      =					layer_norm_eps
							lowerCamelCase			      =					feature_layer_norm_eps
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					vocab_size

							if (
							    (len(self.conv_stride ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    """Configuration for convolutional layers is incorrect."""
										    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
										    f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
										    f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCamelCase			      =					apply_spec_augment
							lowerCamelCase			      =					mask_time_prob
							lowerCamelCase			      =					mask_time_length
							lowerCamelCase			      =					mask_time_min_masks
							lowerCamelCase			      =					mask_feature_prob
							lowerCamelCase			      =					mask_feature_length
							lowerCamelCase			      =					mask_feature_min_masks

							# ctc loss
							lowerCamelCase			      =					ctc_loss_reduction
							lowerCamelCase			      =					ctc_zero_infinity

							# sequence classification
							lowerCamelCase			      =					use_weighted_layer_sum
							lowerCamelCase			      =					classifier_proj_size





				@property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return functools.reduce(operator.mul		, self.conv_stride		, 1 )



