'''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    lowerCamelCase       (			UpperCAmelCase__				:  Dataset				,						UpperCAmelCase__				:  Dict[str, str]		)			->	str:


							'''simple docstring'''






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

							# load metric
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = load_metric('wer'		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = load_metric('cer'		)

							# compute metrics
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = wer.compute(references=result['target']				,						predictions=result['prediction']		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = cer.compute(references=result['target']				,						predictions=result['prediction']		)

							# print & log results
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = F'''log_{dataset_id}_predictions.txt'''
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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__				:  List[str]				,						UpperCAmelCase__				:  List[Any]		):
																												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    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	str:


							'''simple docstring'''






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

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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!
							SCREAMING_SNAKE_CASE__     :str						    = ['\n\n', '\n', '   ', '  ']

							for t in token_sequences_to_ignore:
														SCREAMING_SNAKE_CASE__     :List[str]						    = ' '.join(text.split(UpperCAmelCase__		)		)

							return text


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[Any]		)			->	Any:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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
							SCREAMING_SNAKE_CASE__     :str						    = AutoFeatureExtractor.from_pretrained(args.model_id		)
							SCREAMING_SNAKE_CASE__     :Dict						    = feature_extractor.sampling_rate

							# resample audio
							SCREAMING_SNAKE_CASE__     :str						    = dataset.cast_column('audio'				,						Audio(sampling_rate=UpperCAmelCase__		)		)

							# load eval pipeline
							if args.device is None:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0 if torch.cuda.is_available() else -1
							SCREAMING_SNAKE_CASE__     :Dict						    = pipeline('automatic-speech-recognition'				,						model=args.model_id				,						device=args.device		)

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

														SCREAMING_SNAKE_CASE__     :List[Any]						    = prediction['text']
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = normalize_text(batch['sentence']		)
														return batch

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

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


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

		main(args)







'''simple docstring'''







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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCamelCase_      	=	None


UpperCamelCase_      	=	logging.get_logger(__name__)


UpperCamelCase_      	=	'''▁'''

UpperCamelCase_      	=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_      	=	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase_      	=	{
    '''google/pegasus-xsum''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							str     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							int     		= PegasusTokenizer
						A_			:							Dict     		= ['input_ids', 'attention_mask']




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Union[str, Any]="<pad>"							,							UpperCamelCase_ :       List[Any]="</s>"							,							UpperCamelCase_ :       Tuple="<unk>"							,							UpperCamelCase_ :       List[Any]="<mask_2>"							,							UpperCamelCase_ :       int="<mask_1>"							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[Any]=1_03							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = offset

													if additional_special_tokens is not None:
																				if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											raise TypeError(
																											    f'''additional_special_tokens should be of type {type(UpperCamelCase_      )}, but is'''
																											    f''' {type(UpperCamelCase_      )}'''      )

																				SCREAMING_SNAKE_CASE__     :List[str]						    = (
																				    ([mask_token_sent] + additional_special_tokens)
																				    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																				    else additional_special_tokens
																				)
																				# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																				additional_special_tokens_extended += [
																				    f'''<unk_{i}>''' for i in range(len(UpperCamelCase_      )							,							self.offset - 1      )
																				]

																				if len(set(UpperCamelCase_      )      ) != len(UpperCamelCase_      ):
																											raise ValueError(
																											    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = additional_special_tokens_extended
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [mask_token_sent] if mask_token_sent is not None else []
																				additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset      )]

													super().__init__(
													    UpperCamelCase_							,							tokenizer_file=UpperCamelCase_							,							pad_token=UpperCamelCase_							,							eos_token=UpperCamelCase_							,							unk_token=UpperCamelCase_							,							mask_token=UpperCamelCase_							,							mask_token_sent=UpperCamelCase_							,							offset=UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = set(self.all_special_ids      )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
																				raise ValueError(
																				    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
																				    f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

													return [1 if x in all_special_ids else 0 for x in seq]




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List							,							UpperCamelCase_ :       Optional[List] = None							,							UpperCamelCase_ :       bool = False      )     ->				List[int]:
													if already_has_special_tokens:
																				return self._special_token_mask(UpperCamelCase_      )
													elif token_ids_a is None:
																				return self._special_token_mask(UpperCamelCase_      ) + [1]
													else:
																				return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict=None      )     ->				List[int]:
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													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(UpperCamelCase_      ):
																				logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
																				return
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = os.path.join(
													    UpperCamelCase_							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

													return (out_vocab_file,)






