




"""simple docstring"""

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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					args.log_outputs
					snake_case_					:		Tuple 					=					"_".join(args.dataset.split("""/"""			) + [args.config, args.split]			)

					# load metric
					snake_case_					:		int 					=					load_metric("""wer"""			)
					snake_case_					:		List[Any] 					=					load_metric("""cer"""			)

					# compute metrics
					snake_case_					:		Tuple 					=					wer.compute(references=result["""target"""]				,    predictions=result["""prediction"""]			)
					snake_case_					:		Dict 					=					cer.compute(references=result["""target"""]				,    predictions=result["""prediction"""]			)

					# print & log results
					snake_case_					:		List[Any] 					=					f'WER: {wer_result}\nCER: {cer_result}'
					print(_lowerCamelCase			)

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

					# log all results in text file. Possibly interesting for analysis
					if log_outputs is not None:
										snake_case_					:		Dict 					=					f'log_{dataset_id}_predictions.txt'
										snake_case_					:		List[str] 					=					f'log_{dataset_id}_targets.txt'

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

															result.map(_lowerCamelCase				,    with_indices=_lowerCamelCase			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple			):



					"""simple docstring"""





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

					snake_case_					:		Tuple 					=					re.sub(_lowerCamelCase				,    """"""				,    text.lower()			)

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

					for t in token_sequences_to_ignore:
										snake_case_					:		Optional[Any] 					=					" ".join(text.split(_lowerCamelCase			)			)

					return text







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					load_dataset(args.dataset				,    args.config				,    split=args.split				,    use_auth_token=_lowerCamelCase			)

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

					# load processor
					snake_case_					:		Union[str, Any] 					=					AutoFeatureExtractor.from_pretrained(args.model_id			)
					snake_case_					:		List[str] 					=					feature_extractor.sampling_rate

					# resample audio
					snake_case_					:		Tuple 					=					dataset.cast_column("""audio"""				,    Audio(sampling_rate=_lowerCamelCase			)			)

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

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

										snake_case_					:		Union[str, Any] 					=					prediction["text"]
										snake_case_					:		Dict 					=					normalize_text(batch["""sentence"""]			)
										return batch

					# run inference on all examples
					snake_case_					:		int 					=					dataset.map(_lowerCamelCase				,    remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
			a_			   =					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_			   =					parser.parse_args()

			main(args)









"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			a_			   =					None


a_			   =					logging.get_logger(__name__)


a_			   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_			   =					{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

a_			   =					{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
a_			   =					['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		VOCAB_FILES_NAMES
						_A		:							Union[str, Any]							       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A		:							Optional[int]							       =		PRETRAINED_VOCAB_FILES_MAP
						_A		:							str							       =		["""input_ids""", """attention_mask"""]
						_A		:							Tuple							       =		MBartTokenizer

						_A		:							List[int]							       =		[]
						_A		:							List[int]							       =		[]







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__="<s>"   ,		lowercase__="</s>"   ,		lowercase__="</s>"   ,		lowercase__="<s>"   ,		lowercase__="<unk>"   ,		lowercase__="<pad>"   ,		lowercase__="<mask>"   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__   ,		):
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_					:		int 					=					AddedToken(lowercase__   ,		lstrip=lowercase__   ,		rstrip=lowercase__       ) if isinstance(lowercase__   ,		lowercase__       ) else mask_token

											super().__init__(
											    vocab_file=lowercase__   ,		tokenizer_file=lowercase__   ,		bos_token=lowercase__   ,		eos_token=lowercase__   ,		sep_token=lowercase__   ,		cls_token=lowercase__   ,		unk_token=lowercase__   ,		pad_token=lowercase__   ,		mask_token=lowercase__   ,		src_lang=lowercase__   ,		tgt_lang=lowercase__   ,		additional_special_tokens=lowercase__   ,		**lowercase__   ,		)

											snake_case_					:		Dict 					=					vocab_file
											snake_case_					:		Optional[int] 					=					False if not self.vocab_file else True

											snake_case_					:		Optional[Any] 					=					FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
																# Only add those special tokens if they are not already there.
																_additional_special_tokens.extend(
																    [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

											self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}       )
											snake_case_					:		Any 					=					{
											    lang_code: self.convert_tokens_to_ids(lowercase__       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											snake_case_					:		Tuple 					=					src_lang if src_lang is not None else """en_XX"""
											snake_case_					:		Tuple 					=					self.convert_tokens_to_ids(self._src_lang       )
											snake_case_					:		Tuple 					=					tgt_lang
											self.set_src_lang_special_tokens(self._src_lang       )







						@property
						def 						__UpperCamelCase							(self       ):
											return self._src_lang







						@src_lang.setter
						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					new_src_lang
											self.set_src_lang_special_tokens(self._src_lang       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											if token_ids_a is None:
																return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											snake_case_					:		List[Any] 					=					[self.sep_token_id]
											snake_case_					:		Optional[int] 					=					[self.cls_token_id]

											if token_ids_a is None:
																return len(cls + token_ids_a + sep       ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											if src_lang is None or tgt_lang is None:
																raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
											snake_case_					:		int 					=					src_lang
											snake_case_					:		List[str] 					=					self(lowercase__   ,		add_special_tokens=lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )
											snake_case_					:		List[str] 					=					self.convert_tokens_to_ids(lowercase__       )
											snake_case_					:		Union[str, Any] 					=					tgt_lang_id
											return inputs







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = "en_XX"   ,		lowercase__ = None   ,		lowercase__ = "ro_RO"   ,		**lowercase__   ,		):
											snake_case_					:		List[str] 					=					src_lang
											snake_case_					:		int 					=					tgt_lang
											return super().prepare_seqaseq_batch(lowercase__   ,		lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self       ):
											return self.set_src_lang_special_tokens(self.src_lang       )







						def 						__UpperCamelCase							(self       ):
											return self.set_tgt_lang_special_tokens(self.tgt_lang       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					self.convert_tokens_to_ids(lowercase__       )
											snake_case_					:		Tuple 					=					[]
											snake_case_					:		List[Any] 					=					[self.eos_token_id, self.cur_lang_code]

											snake_case_					:		List[Any] 					=					self.convert_ids_to_tokens(self.prefix_tokens       )
											snake_case_					:		Union[str, Any] 					=					self.convert_ids_to_tokens(self.suffix_tokens       )

											snake_case_					:		Optional[int] 					=					processors.TemplateProcessing(
											    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str   ,		pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str   ,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,		self.prefix_tokens + self.suffix_tokens       )       )   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					self.convert_tokens_to_ids(lowercase__       )
											snake_case_					:		Optional[int] 					=					[]
											snake_case_					:		Optional[Any] 					=					[self.eos_token_id, self.cur_lang_code]

											snake_case_					:		Union[str, Any] 					=					self.convert_ids_to_tokens(self.prefix_tokens       )
											snake_case_					:		int 					=					self.convert_ids_to_tokens(self.suffix_tokens       )

											snake_case_					:		List[Any] 					=					processors.TemplateProcessing(
											    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str   ,		pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str   ,		special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,		self.prefix_tokens + self.suffix_tokens       )       )   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None       ):
											if not self.can_save_slow_tokenizer:
																raise ValueError(
																    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																    """tokenizer."""       )

											if not os.path.isdir(lowercase__       ):
																logger.error(f'Vocabulary path ({save_directory}) should be a directory.'       )
																return
											snake_case_					:		List[str] 					=					os.path.join(
											    lowercase__   ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ):
																copyfile(self.vocab_file   ,		lowercase__       )

											return (out_vocab_file,)




