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

from utils import logger


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase) ->			List[str]:
        __UpperCamelCase  :Union[str, Any]    						=	params

        __UpperCamelCase  :str    						=	np.array(__lowercase)
        __UpperCamelCase  :Tuple    						=	np.array([len(__lowercase) 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				,				__lowercase) ->			Any:
        return (self.token_ids[index], self.lengths[index])







    def __len__(			self) ->			List[Any]:
        return len(self.lengths)







    def     UpperCamelCase__     (			self) ->			int:
        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     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :str    						=	self.params.max_model_input_size
        __UpperCamelCase  :Optional[int]    						=	self.lengths > max_len
        logger.info(f"""Splitting {sum(__lowercase)} too long sequences.""")

        def divide_chunks(__lowercase				,				__lowercase):
            return [l[i : i + n] for i in range(0				,				len(__lowercase)				,				__lowercase)]

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

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

        __UpperCamelCase  :Optional[int]    						=	np.array(__lowercase)
        __UpperCamelCase  :List[Any]    						=	np.array(__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :Optional[Any]    						=	len(self)
        __UpperCamelCase  :str    						=	self.lengths > 11
        __UpperCamelCase  :Optional[int]    						=	self.token_ids[indices]
        __UpperCamelCase  :Optional[int]    						=	self.lengths[indices]
        __UpperCamelCase  :Union[str, Any]    						=	len(self)
        logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""")







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







    def     UpperCamelCase__     (			self) ->			Dict:
        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     UpperCamelCase__     (			self				,				__lowercase) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	[t[0] for t in batch]
        __UpperCamelCase  :Union[str, Any]    						=	[t[1] for t in batch]
        assert len(__lowercase) == len(__lowercase)

        # Max for paddings
        __UpperCamelCase  :List[str]    						=	max(__lowercase)

        # Pad token ids
        if self.params.mlm:
            __UpperCamelCase  :str    						=	self.params.special_tok_ids['''pad_token''']
        else:
            __UpperCamelCase  :int    						=	self.params.special_tok_ids['''unk_token''']
        __UpperCamelCase  :Any    						=	[list(t.astype(__lowercase)) + [pad_idx] * (max_seq_len_ - len(__lowercase)) for t in token_ids]
        assert len(tk_) == len(__lowercase)
        assert all(len(__lowercase) == max_seq_len_ for t in tk_)

        __UpperCamelCase  :List[Any]    						=	torch.tensor(tk_)  # (bs, max_seq_len_)
        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(__lowercase)  # (bs)
        return tk_t, lg_t






from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


__lowercase											=      datasets.utils.logging.get_logger(__name__)


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilderConfig				):






    '''simple docstring'''






    a__			:		bool      			=   None
    a__			:		bool      			=   None


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilder				):






    '''simple docstring'''






    a__			:		List[Any]      			=   datasets.Audio()
    a__			:		int      			=   """audio"""
    a__			:		str      			=   AudioFolderConfig
    a__			:		List[str]  # definition at the bottom of the script
    a__			:		int      			=   AudioClassification(audio_column="""audio"""		,			label_column="""label"""				)
__lowercase											=      [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
__lowercase											=      AUDIO_EXTENSIONS





