




import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]       ,     a       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					params

										SCREAMING_SNAKE_CASE           =					np.array(a			)
										SCREAMING_SNAKE_CASE           =					np.array([len(a			) for t in data]			)

										self.check()
										self.remove_long_sequences()
										self.remove_empty_sequences()
										self.remove_unknown_sequences()
										self.check()
										self.print_statistics()





							def __getitem__(					self       :	str       ,     a       :	Optional[Any]			)			->					List[Any]:
										return (self.token_ids[index], self.lengths[index])





							def __len__(					self       :	Dict			)			->					Any:
										return len(self.lengths			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										assert len(self.token_ids			) == len(self.lengths			)
										assert all(self.lengths[i] == len(self.token_ids[i]			) for i in range(len(self.lengths			)			)			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.params.max_model_input_size
										SCREAMING_SNAKE_CASE           =					self.lengths > max_len
										logger.info(f"""Splitting {sum(a			)} too long sequences."""			)

										def divide_chunks(a       :	Optional[Any]       ,     a       :	Union[str, Any]			):
													return [l[i : i + n] for i in range(0       ,     len(a			)       ,     a			)]

										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[]
										if self.params.mlm:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
										else:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

										for seq_, len_ in zip(self.token_ids       ,     self.lengths			):
													assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
													if len_ <= max_len:
																new_tok_ids.append(seq_			)
																new_lengths.append(len_			)
													else:
																SCREAMING_SNAKE_CASE           =					[]
																for sub_s in divide_chunks(seq_       ,     max_len - 2			):
																			if sub_s[0] != cls_id:
																						SCREAMING_SNAKE_CASE           =					np.insert(a       ,     0       ,     a			)
																			if sub_s[-1] != sep_id:
																						SCREAMING_SNAKE_CASE           =					np.insert(a       ,     len(a			)       ,     a			)
																			assert len(a			) <= max_len
																			assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																			sub_seqs.append(a			)

																new_tok_ids.extend(a			)
																new_lengths.extend([len(a			) for l in sub_seqs]			)

										SCREAMING_SNAKE_CASE           =					np.array(a			)
										SCREAMING_SNAKE_CASE           =					np.array(a			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					len(self			)
										SCREAMING_SNAKE_CASE           =					self.lengths > 11
										SCREAMING_SNAKE_CASE           =					self.token_ids[indices]
										SCREAMING_SNAKE_CASE           =					self.lengths[indices]
										SCREAMING_SNAKE_CASE           =					len(self			)
										logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										if "unk_token" not in self.params.special_tok_ids:
													return
										else:
													SCREAMING_SNAKE_CASE           =					self.params.special_tok_ids["""unk_token"""]
										SCREAMING_SNAKE_CASE           =					len(self			)
										SCREAMING_SNAKE_CASE           =					np.array([np.count_nonzero(a == unk_token_id			) for a in self.token_ids]			)
										SCREAMING_SNAKE_CASE           =					(unk_occs / self.lengths) < 0.5
										SCREAMING_SNAKE_CASE           =					self.token_ids[indices]
										SCREAMING_SNAKE_CASE           =					self.lengths[indices]
										SCREAMING_SNAKE_CASE           =					len(self			)
										logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Dict:
										if not self.params.is_master:
													return
										logger.info(f"""{len(self			)} sequences"""			)
										# data_len = sum(self.lengths)
										# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
										# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

										# unk_idx = self.params.special_tok_ids['unk_token']
										# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
										# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					[t[0] for t in batch]
										SCREAMING_SNAKE_CASE           =					[t[1] for t in batch]
										assert len(a			) == len(a			)

										# Max for paddings
										SCREAMING_SNAKE_CASE           =					max(a			)

										# Pad token ids
										if self.params.mlm:
													SCREAMING_SNAKE_CASE           =					self.params.special_tok_ids["""pad_token"""]
										else:
													SCREAMING_SNAKE_CASE           =					self.params.special_tok_ids["""unk_token"""]
										SCREAMING_SNAKE_CASE           =					[list(t.astype(a			)			) + [pad_idx] * (max_seq_len_ - len(a			)) for t in token_ids]
										assert len(tk_			) == len(a			)
										assert all(len(a			) == max_seq_len_ for t in tk_			)

										SCREAMING_SNAKE_CASE           =					torch.tensor(tk_			)  # (bs, max_seq_len_)
										SCREAMING_SNAKE_CASE           =					torch.tensor(a			)  # (bs)
										return tk_t, lg_t









import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

			# compute metrics
			SCREAMING_SNAKE_CASE           =					wer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )
			SCREAMING_SNAKE_CASE           =					cer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )

			# print & log results
			SCREAMING_SNAKE_CASE           =					f"""WER: {wer_result}\nCER: {cer_result}"""
			print(SCREAMING_SNAKE_CASE   )

			with open(f"""{dataset_id}_eval_results.txt"""					,	"""w"""   ) as f:
						f.write(SCREAMING_SNAKE_CASE   )

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_predictions.txt"""
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_targets.txt"""

						with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as p, open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as t:
									# mapping function to write output
									def write_to_file(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
												p.write(f"""{i}""" + """\n"""   )
												p.write(batch["""prediction"""] + """\n"""   )
												t.write(f"""{i}""" + """\n"""   )
												t.write(batch["""target"""] + """\n"""   )

									result.map(SCREAMING_SNAKE_CASE					,	with_indices=SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			SCREAMING_SNAKE_CASE           =					re.sub(SCREAMING_SNAKE_CASE					,	""""""					,	text.lower()   )

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					load_dataset(args.dataset					,	args.config					,	split=args.split					,	use_auth_token=SCREAMING_SNAKE_CASE   )

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

			# load eval pipeline
			if args.device is None:
						SCREAMING_SNAKE_CASE           =					0 if torch.cuda.is_available() else -1
			SCREAMING_SNAKE_CASE           =					pipeline("""automatic-speech-recognition"""					,	model=args.model_id					,	device=args.device   )

			# map function to decode audio
			def map_to_pred(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					asr(
						    batch["""audio"""]["""array"""]					,	chunk_length_s=args.chunk_length_s					,	stride_length_s=args.stride_length_s   )

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					dataset.map(SCREAMING_SNAKE_CASE					,	remove_columns=dataset.column_names   )

			# compute and log_results
			# do not change function below
			log_results(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[Any]							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
		)
		parser.add_argument(
		    """--dataset""",
		    type=str,
		    required=True,
		    help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
		)
		parser.add_argument(
		    """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
		)
		parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
		parser.add_argument(
		    """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
		)
		parser.add_argument(
		    """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
		)
		parser.add_argument(
		    """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
		)
		parser.add_argument(
		    """--device""",
		    type=int,
		    default=None,
		    help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
		)
		__A						:							Tuple							=  parser.parse_args()

		main(args)






