"""simple docstring"""




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

from utils import logger
class 			UpperCamelCase__(	__snake_case       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[Any]:
													A__		   =							params

													A__		   =							np.array(__UpperCAmelCase   )
													A__		   =							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   )    -> int:
													return (self.token_ids[index], self.lengths[index])





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





						def  snake_case__		(				self   )    -> Optional[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  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.params.max_model_input_size
													A__		   =							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   )]

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

													A__		   =							np.array(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   )





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





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





						def  snake_case__		(				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  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							[t[0] for t in batch]
													A__		   =							[t[1] for t in batch]
													assert len(__UpperCAmelCase   ) == len(__UpperCAmelCase   )

													# Max for paddings
													A__		   =							max(__UpperCAmelCase   )

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

													A__		   =							torch.tensor(tk_   )  # (bs, max_seq_len_)
													A__		   =							torch.tensor(__UpperCAmelCase   )  # (bs)
													return tk_t, lg_t








# 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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {}
snake_case_			  = {}
snake_case_			  = {}

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    type	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None	,     ):



     '''simple docstring'''





     lowercase__    :   Union[str, 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__    :   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__    :   int      =       format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Exception	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       aliases if aliases is not None else []
     for alias in set(aliases + [format_type]			):
          lowercase__    :   Any      =       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:
       snake_case_			  = 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:
       snake_case_			  = 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:
       snake_case_			  = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
       _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]	,     **SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   Tuple      =       get_format_type_from_alias(SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE_			)
     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}'"""			)

