



'''simple docstring'''





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

from utils import logger


class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				List[str]:
           lowercase__									=    params

           lowercase__									=    np.array(UpperCAmelCase_   )
           lowercase__									=    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  ,UpperCAmelCase_   )	->				Optional[Any]:
           return (self.token_ids[index], self.lengths[index])



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



       def 				_a   (					self   )	->				List[str]:
           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   )	->				Optional[Any]:
           lowercase__									=    self.params.max_model_input_size
           lowercase__									=    self.lengths > max_len
           logger.info(F'''Splitting {sum(UpperCAmelCase_   )} too long sequences.'''   )

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

           lowercase__									=    []
           lowercase__									=    []
           if self.params.mlm:
               lowercase__							,							lowercase__									=    self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
           else:
               lowercase__							,							lowercase__									=    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:
                   lowercase__									=    []
                   for sub_s in divide_chunks(seq_  ,max_len - 2   ):
                       if sub_s[0] != cls_id:
                           lowercase__									=    np.insert(UpperCAmelCase_  ,0  ,UpperCAmelCase_   )
                       if sub_s[-1] != sep_id:
                           lowercase__									=    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]   )

           lowercase__									=    np.array(UpperCAmelCase_   )
           lowercase__									=    np.array(UpperCAmelCase_   )



       def 				_a   (					self   )	->				Optional[int]:
           lowercase__									=    len(self   )
           lowercase__									=    self.lengths > 11
           lowercase__									=    self.token_ids[indices]
           lowercase__									=    self.lengths[indices]
           lowercase__									=    len(self   )
           logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''   )



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



       def 				_a   (					self   )	->				int:
           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  ,UpperCAmelCase_   )	->				Union[str, Any]:
           lowercase__									=    [t[0] for t in batch]
           lowercase__									=    [t[1] for t in batch]
           assert len(UpperCAmelCase_   ) == len(UpperCAmelCase_   )

           # Max for paddings
           lowercase__									=    max(UpperCAmelCase_   )

           # Pad token ids
           if self.params.mlm:
               lowercase__									=    self.params.special_tok_ids["pad_token"]
           else:
               lowercase__									=    self.params.special_tok_ids["unk_token"]
           lowercase__									=    [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_   )

           lowercase__									=    torch.tensor(tk_   )  # (bs, max_seq_len_)
           lowercase__									=    torch.tensor(UpperCAmelCase_   )  # (bs)
           return tk_t, lg_t









'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__          =  {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ClapModel",
     "ClapPreTrainedModel",
     "ClapTextModel",
     "ClapTextModelWithProjection",
     "ClapAudioModel",
     "ClapAudioModelWithProjection",
 ]
 SCREAMING_SNAKE_CASE__          =  ["ClapFeatureExtractor"]

if TYPE_CHECKING:
 from .configuration_clap import (
     CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
     ClapAudioConfig,
     ClapConfig,
     ClapTextConfig,
 )
 from .processing_clap import ClapProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clap import ClapFeatureExtractor
  from .modeling_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioModel,
      ClapAudioModelWithProjection,
      ClapModel,
      ClapPreTrainedModel,
      ClapTextModel,
      ClapTextModelWithProjection,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__          =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




