

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCamelCase      				=     logging.getLogger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A=-1							):
												# in NER datasets, the last column is usually reserved for NER label
												__lowerCAmelCase					=							label_idx




							def        A__  ( self					,						_A					,						_A							):
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							mode.value
												__lowerCAmelCase					=							os.path.join(_A					,						F"""{mode}.txt"""							)
												__lowerCAmelCase					=							1
												__lowerCAmelCase					=							[]
												with open(_A					,						encoding='utf-8'							) as f:
																	__lowerCAmelCase					=							[]
																	__lowerCAmelCase					=							[]
																	for line in f:
																						if line.startswith('-DOCSTART-'							) or line == "" or line == "\n":
																											if words:
																																examples.append(InputExample(guid=F"""{mode}-{guid_index}"""					,						words=_A					,						labels=_A							)							)
																																guid_index += 1
																																__lowerCAmelCase					=							[]
																																__lowerCAmelCase					=							[]
																						else:
																											__lowerCAmelCase					=							line.split(' '							)
																											words.append(splits[0]							)
																											if len(_A							) > 1:
																																labels.append(splits[self.label_idx].replace('\n'					,						''							)							)
																											else:
																																# Examples could have no label for mode = "test"
																																labels.append('O'							)
																	if words:
																						examples.append(InputExample(guid=F"""{mode}-{guid_index}"""					,						words=_A					,						labels=_A							)							)
												return examples




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							0
												for line in test_input_reader:
																	if line.startswith('-DOCSTART-'							) or line == "" or line == "\n":
																						writer.write(_A							)
																						if not preds_list[example_id]:
																											example_id += 1
																	elif preds_list[example_id]:
																						__lowerCAmelCase					=							line.split()[0] + ' ' + preds_list[example_id].pop(0							) + '\n'
																						writer.write(_A							)
																	else:
																						logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'					,						line.split()[0]							)




							def        A__  ( self					,						_A							):
												if path:
																	with open(_A					,						'r'							) as f:
																						__lowerCAmelCase					=							f.read().splitlines()
																	if "O" not in labels:
																						__lowerCAmelCase					=							['O'] + labels
																	return labels
												else:
																	return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self							):
												# in CONLL2003 dataset chunk column is second-to-last
												super().__init__(label_idx=-2							)




							def        A__  ( self					,						_A							):
												if path:
																	with open(_A					,						'r'							) as f:
																						__lowerCAmelCase					=							f.read().splitlines()
																	if "O" not in labels:
																						__lowerCAmelCase					=							['O'] + labels
																	return labels
												else:
																	return [
																	    "O",
																	    "B-ADVP",
																	    "B-INTJ",
																	    "B-LST",
																	    "B-PRT",
																	    "B-NP",
																	    "B-SBAR",
																	    "B-VP",
																	    "B-ADJP",
																	    "B-CONJP",
																	    "B-PP",
																	    "I-ADVP",
																	    "I-INTJ",
																	    "I-LST",
																	    "I-PRT",
																	    "I-NP",
																	    "I-SBAR",
																	    "I-VP",
																	    "I-ADJP",
																	    "I-CONJP",
																	    "I-PP",
																	]





class     snake_case_			(				_a							):


							"""simple docstring"""





							def        A__  ( self					,						_A					,						_A							):
												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							mode.value
												__lowerCAmelCase					=							os.path.join(_A					,						F"""{mode}.txt"""							)
												__lowerCAmelCase					=							1
												__lowerCAmelCase					=							[]

												with open(_A					,						encoding='utf-8'							) as f:
																	for sentence in parse_incr(_A							):
																						__lowerCAmelCase					=							[]
																						__lowerCAmelCase					=							[]
																						for token in sentence:
																											words.append(token['form']							)
																											labels.append(token['upos']							)
																						assert len(_A							) == len(_A							)
																						if words:
																											examples.append(InputExample(guid=F"""{mode}-{guid_index}"""					,						words=_A					,						labels=_A							)							)
																											guid_index += 1
												return examples




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							0
												for sentence in parse_incr(_A							):
																	__lowerCAmelCase					=							preds_list[example_id]
																	__lowerCAmelCase					=							''
																	for token in sentence:
																						out += F"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
																	out += "\n"
																	writer.write(_A							)
																	example_id += 1




							def        A__  ( self					,						_A							):
												if path:
																	with open(_A					,						'r'							) as f:
																						return f.read().splitlines()
												else:
																	return [
																	    "ADJ",
																	    "ADP",
																	    "ADV",
																	    "AUX",
																	    "CCONJ",
																	    "DET",
																	    "INTJ",
																	    "NOUN",
																	    "NUM",
																	    "PART",
																	    "PRON",
																	    "PROPN",
																	    "PUNCT",
																	    "SCONJ",
																	    "SYM",
																	    "VERB",
																	    "X",
																	]









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							args.log_outputs
					__lowerCAmelCase					=							'_'.join(args.dataset.split('/'      ) + [args.config, args.split]      )

					# load metric
					__lowerCAmelCase					=							load_metric('wer'      )
					__lowerCAmelCase					=							load_metric('cer'      )

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

					# print & log results
					__lowerCAmelCase					=							F"""WER: {wer_result}\nCER: {cer_result}"""
					print(UpperCAmelCase__      )

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

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

										with open(UpperCAmelCase__		,					'w'      ) as p, open(UpperCAmelCase__		,					'w'      ) as t:
															# mapping function to write output
															def write_to_file(UpperCAmelCase__		,					UpperCAmelCase__      ):
																				p.write(F"""{i}""" + '\n'      )
																				p.write(batch['prediction'] + '\n'      )
																				t.write(F"""{i}""" + '\n'      )
																				t.write(batch['target'] + '\n'      )

															result.map(UpperCAmelCase__		,					with_indices=UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							'[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

					__lowerCAmelCase					=							re.sub(UpperCAmelCase__		,					''		,					text.lower()      )

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

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							load_dataset(args.dataset		,					args.config		,					split=args.split		,					use_auth_token=UpperCAmelCase__      )

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

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

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

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

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

										__lowerCAmelCase					=							prediction['text']
										__lowerCAmelCase					=							normalize_text(batch['sentence']      )
										return batch

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

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


if __name__ == "__main__":
		lowerCamelCase      				=     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.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		main(args)






