
"""simple docstring"""




# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A__ :       Union[str, Any]				= logging.get_logger(__name__)

A__ :       Dict[Optional[str], Type[Formatter]]				= {}
A__ :       Dict[Optional[str], str]				= {}
A__ :       Dict[Optional[str], Exception]				= {}




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = None			,					):






  """simple docstring"""



  _lowercase:      List[Any]   			=  aliases if aliases is not None else []
  if format_type in _FORMAT_TYPES:
    logger.warning(
        f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''    )
  _lowercase:      List[Any]   			=  formatter_cls
  for alias in set(aliases + [format_type]    ):
    if alias in _FORMAT_TYPES_ALIASES:
      logger.warning(
          f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''    )
    _lowercase:      Optional[int]   			=  format_type




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = None    ):






  """simple docstring"""



  _lowercase:      str   			=  aliases if aliases is not None else []
  for alias in set(aliases + [format_type]    ):
    _lowercase:      Dict   			=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
  from .torch_formatter import TorchFormatter

  _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
  A__ :       Union[str, Any]				= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
  _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
  from .tf_formatter import TFFormatter

  _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
  A__ :       str				= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
  _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
  from .jax_formatter import JaxFormatter

  _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
  A__ :       Any				= ValueError('JAX needs to be installed to be able to return JAX arrays.')
  _register_unavailable_formatter(_jax_error, 'jax', aliases=[])




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  if format_type in _FORMAT_TYPES_ALIASES:
    return _FORMAT_TYPES_ALIASES[format_type]
  else:
    return format_type







def 			_lowerCAmelCase		(		_UpperCamelCase			,					**_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  get_format_type_from_alias(_UpperCamelCase    )
  if format_type in _FORMAT_TYPES:
    return _FORMAT_TYPES[format_type](**_UpperCamelCase    )
  if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
  else:
    raise ValueError(
        f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None    )}, but got \'{format_type}\''''    )


"""simple docstring"""




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

from utils import logger


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      List[str]   			=  params

         _lowercase:      str   			=  np.array(A_							)
         _lowercase:      Optional[int]   			=  np.array([len(A_							) 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			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return (self.token_ids[index], self.lengths[index])
       def __len__(  self							)  ->						List[str]:





         """simple docstring"""




         return len(self.lengths							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         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 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.params.max_model_input_size
         _lowercase:      Tuple   			=  self.lengths > max_len
         logger.info(f'''Splitting {sum(A_							)} too long sequences.'''							)

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

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

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

         _lowercase:      Optional[Any]   			=  np.array(A_							)
         _lowercase:      List[str]   			=  np.array(A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  len(self							)
         _lowercase:      Optional[Any]   			=  self.lengths > 11
         _lowercase:      int   			=  self.token_ids[indices]
         _lowercase:      List[str]   			=  self.lengths[indices]
         _lowercase:      Optional[int]   			=  len(self							)
         logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




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





         """simple docstring"""




         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 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  [t[0] for t in batch]
         _lowercase:      Dict   			=  [t[1] for t in batch]
         assert len(A_							) == len(A_							)

         # Max for paddings
         _lowercase:      Tuple   			=  max(A_							)

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

         _lowercase:      str   			=  torch.tensor(tk_							)  # (bs, max_seq_len_)
         _lowercase:      int   			=  torch.tensor(A_							)  # (bs)
         return tk_t, lg_t
