






"""simple docstring"""





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class 			lowercase__			(      lowercase__    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'EncodecFeatureExtractor'
							_UpperCAmelCase      				=					('T5Tokenizer', 'T5TokenizerFast')






							def __init__(  self	,       snake_case	,       snake_case						)			->							Optional[Any]:
									super().__init__(snake_case	,       snake_case						)
									_UpperCAmelCase							 =		self.feature_extractor
									_UpperCAmelCase							 =		False






							def        lowerCamelCase_   (  self	,       snake_case=None	,       snake_case=None	,       snake_case=True						)			->							Any:
									return self.tokenizer.get_decoder_prompt_ids(task=snake_case	,       language=snake_case	,       no_timestamps=snake_case						)






							def __call__(  self	,       *snake_case	,       **snake_case						)			->							List[str]:
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor(*snake_case	,       **snake_case						)

									_UpperCAmelCase							 =		kwargs.pop('audio'	,       snake_case						)
									_UpperCAmelCase							 =		kwargs.pop('sampling_rate'	,       snake_case						)
									_UpperCAmelCase							 =		kwargs.pop('text'	,       snake_case						)
									if len(snake_case						) > 0:
											_UpperCAmelCase							 =		args[0]
											_UpperCAmelCase							 =		args[1:]

									if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'						)

									if text is not None:
											_UpperCAmelCase							 =		self.tokenizer(snake_case	,       **snake_case						)

									if audio is not None:
											_UpperCAmelCase							 =		self.feature_extractor(snake_case	,       *snake_case	,       sampling_rate=snake_case	,       **snake_case						)

									if audio is None:
											return inputs

									elif text is None:
											return audio_inputs

									else:
											_UpperCAmelCase							 =		audio_inputs['input_values']
											if "padding_mask" in audio_inputs:
													_UpperCAmelCase							 =		audio_inputs['padding_mask']
											return inputs






							def        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		kwargs.pop('audio'	,       snake_case						)
									_UpperCAmelCase							 =		kwargs.pop('padding_mask'	,       snake_case						)

									if len(snake_case						) > 0:
											_UpperCAmelCase							 =		args[0]
											_UpperCAmelCase							 =		args[1:]

									if audio_values is not None:
											return self._decode_audio(snake_case	,       padding_mask=snake_case						)
									else:
											return self.tokenizer.batch_decode(*snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							Dict:
									return self.tokenizer.decode(*snake_case	,       **snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = None						)			->							int:
									_UpperCAmelCase							 =		to_numpy(snake_case						)
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		audio_values.shape

									if padding_mask is None:
											return list(snake_case						)

									_UpperCAmelCase							 =		to_numpy(snake_case						)

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									_UpperCAmelCase							 =		seq_len - padding_mask.shape[-1]
									_UpperCAmelCase							 =		1 - self.feature_extractor.padding_value
									_UpperCAmelCase							 =		np.pad(snake_case	,       ((0, 0), (0, difference))	,       'constant'	,       constant_values=snake_case						)

									_UpperCAmelCase							 =		audio_values.tolist()
									for i in range(snake_case						):
											_UpperCAmelCase							 =		np.asarray(audio_values[i]						)[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											_UpperCAmelCase							 =		sliced_audio.reshape(snake_case	,       -1						)

									return audio_values







"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase  							=					logging.getLogger(__name__)

if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser(
					    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
					)
					parser.add_argument(
					    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
					)
					parser.add_argument(
					    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
					)
					parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
					lowercase  							=					parser.parse_args()

					logger.info(F'''Loading data from {args.data_file}''')
					with open(args.data_file, '''rb''') as fp:
										lowercase  							=					pickle.load(fp)

					logger.info('''Counting occurrences for MLM.''')
					lowercase  							=					Counter()
					for tk_ids in data:
										counter.update(tk_ids)
					lowercase  							=					[0] * args.vocab_size
					for k, v in counter.items():
										lowercase  							=					v

					logger.info(F'''Dump to {args.token_counts_dump}''')
					with open(args.token_counts_dump, '''wb''') as handle:
										pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


