


'''simple docstring'''






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

from utils import logger
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Any  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Any			):
									snake_case_		:							Optional[int]			       =						params

									snake_case_		:							int			       =						np.array(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						np.array([len(_UpperCamelCase			) 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		:Dict  ,_UpperCamelCase		:Optional[Any]			):
									return (self.token_ids[index], self.lengths[index])





							def __len__(  self		:Any			):
									return len(self.lengths			)





							def 		a__   (  self		: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 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							str			       =						self.params.max_model_input_size
									snake_case_		:							Union[str, Any]			       =						self.lengths > max_len
									logger.info(F'''Splitting {sum(_UpperCamelCase			)} too long sequences.'''			)

									def divide_chunks(_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[Any]			):
											return [l[i : i + n] for i in range(0  ,len(_UpperCamelCase			)  ,_UpperCamelCase			)]

									snake_case_		:							Tuple			       =						[]
									snake_case_		:							Dict			       =						[]
									if self.params.mlm:
											snake_case_       ,      snake_case_		:							List[str]			       =						self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
									else:
											snake_case_       ,      snake_case_		:							Tuple			       =						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:
													snake_case_		:							List[Any]			       =						[]
													for sub_s in divide_chunks(seq_  ,max_len - 2			):
															if sub_s[0] != cls_id:
																	snake_case_		:							List[Any]			       =						np.insert(_UpperCamelCase  ,0  ,_UpperCamelCase			)
															if sub_s[-1] != sep_id:
																	snake_case_		:							Dict			       =						np.insert(_UpperCamelCase  ,len(_UpperCamelCase			)  ,_UpperCamelCase			)
															assert len(_UpperCamelCase			) <= max_len
															assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
															sub_seqs.append(_UpperCamelCase			)

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

									snake_case_		:							List[Any]			       =						np.array(_UpperCamelCase			)
									snake_case_		:							str			       =						np.array(_UpperCamelCase			)





							def 		a__   (  self		:int			):
									snake_case_		:							Tuple			       =						len(self			)
									snake_case_		:							Any			       =						self.lengths > 1_1
									snake_case_		:							Union[str, Any]			       =						self.token_ids[indices]
									snake_case_		:							Any			       =						self.lengths[indices]
									snake_case_		:							int			       =						len(self			)
									logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''			)





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





							def 		a__   (  self		:Tuple			):
									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 		a__   (  self		:List[str]  ,_UpperCamelCase		:str			):
									snake_case_		:							Optional[int]			       =						[t[0] for t in batch]
									snake_case_		:							Optional[int]			       =						[t[1] for t in batch]
									assert len(_UpperCamelCase			) == len(_UpperCamelCase			)

									# Max for paddings
									snake_case_		:							Tuple			       =						max(_UpperCamelCase			)

									# Pad token ids
									if self.params.mlm:
											snake_case_		:							Union[str, Any]			       =						self.params.special_tok_ids["""pad_token"""]
									else:
											snake_case_		:							Union[str, Any]			       =						self.params.special_tok_ids["""unk_token"""]
									snake_case_		:							Optional[int]			       =						[list(t.astype(_UpperCamelCase			)			) + [pad_idx] * (max_seq_len_ - len(_UpperCamelCase			)) for t in token_ids]
									assert len(tk_			) == len(_UpperCamelCase			)
									assert all(len(_UpperCamelCase			) == max_seq_len_ for t in tk_			)

									snake_case_		:							Any			       =						torch.tensor(tk_			)  # (bs, max_seq_len_)
									snake_case_		:							List[Any]			       =						torch.tensor(_UpperCamelCase			)  # (bs)
									return tk_t, lg_t



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A	:	Tuple     =     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	Tuple     =     [
							    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ASTForAudioClassification',
							    'ASTModel',
							    'ASTPreTrainedModel',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	int     =     ['ASTFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							__A	:	Union[str, Any]     =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)