
from jiwer import compute_measures

import datasets


__a         =   '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

__a         =   '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

__a         =   '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,		_KWARGS_DESCRIPTION				)
class 					lowercase__(							datasets.Metric				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION			,				citation=_CITATION			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
					        {
					            '''predictions''': datasets.Value('''string'''			,				id='''sequence'''			),
					            '''references''': datasets.Value('''string'''			,				id='''sequence'''			),
					        }			)			,				codebase_urls=['''https://github.com/jitsi/jiwer/''']			,				reference_urls=[
					        '''https://en.wikipedia.org/wiki/Word_error_rate''',
					    ]			,				)




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     int=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=False			)			->	str:
					if concatenate_texts:
									return compute_measures(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)["wer"]
					else:
									lowercase_	    =   0
									lowercase_	    =   0
									for prediction, reference in zip(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   compute_measures(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
													total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total






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__:   Any   ):


				'''simple docstring'''
				lowercase_	    =   filter(lambda snake_case__   : p.requires_grad		,  model.parameters()   )
				lowercase_	    =   sum([np.prod(p.size()   ) for p in model_parameters]   )
				return params


__a         =   logging.getLogger(__name__)




def 							a		(				snake_case__:   int		,  snake_case__:   Any   ):


				'''simple docstring'''
				if metric == "rouge2":
								lowercase_	    =   '''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								lowercase_	    =   '''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								lowercase_	    =   '''{val_avg_em:.4f}-{step_count}'''
				elif metric == "loss":
								lowercase_	    =   '''{val_avg_loss:.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.'''   )

				lowercase_	    =   ModelCheckpoint(
				    dirpath=snake_case__		,  filename=snake_case__		,  monitor=F'''val_{metric}'''		,  mode='''max'''		,  save_top_k=1		,  every_n_epochs=1		,  )

				return checkpoint_callback




def 							a		(				snake_case__:   str		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return EarlyStopping(
				    monitor=F'''val_{metric}'''		,  mode='''min''' if '''loss''' in metric else '''max'''		,  patience=snake_case__		,  verbose=snake_case__		,  )


class 					lowercase__(							pl.Callback				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     pl.LightningModule			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			)			->	None:
					logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
					lowercase_	    =   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
					lowercase_	    =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
									lowercase_	    =   od / '''test_results.txt'''
									lowercase_	    =   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.
									lowercase_	    =   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
									lowercase_	    =   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
									results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
									generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
					with open(SCREAMING_SNAKE_CASE_			,				'''a+'''			) as writer:
									for key in sorted(SCREAMING_SNAKE_CASE_			):
													if key in ["log", "progress_bar", "preds"]:
																	continue
													lowercase_	    =   metrics[key]
													if isinstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			):
																	lowercase_	    =   val.item()
													lowercase_	    =   f'''{key}: {val:.6f}\n'''
													writer.write(SCREAMING_SNAKE_CASE_			)

					if not save_generations:
									return

					if "preds" in metrics:
									lowercase_	    =   '''\n'''.join(metrics['''preds''']			)
									generations_file.open('''w+'''			).write(SCREAMING_SNAKE_CASE_			)
	@rank_zero_only
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					try:
									lowercase_	    =   pl_module.model.model.num_parameters()
					except AttributeError:
									lowercase_	    =   pl_module.model.num_parameters()

					lowercase_	    =   count_trainable_parameters(SCREAMING_SNAKE_CASE_			)
					# 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			)			->	Any:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					return self._write_logs(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				'''test'''			)




	@rank_zero_only
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     pl.Trainer			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Dict:
					save_json(pl_module.metrics			,				pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")




