

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:int						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict						=     {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}






class 						__magic_name__					(							SCREAMING_SNAKE_CASE__				):
     UpperCamelCase_	:Any										=      """xlnet"""
     UpperCamelCase_	:List[Any]										=      ["""mems"""]
     UpperCamelCase_	:Optional[Any]										=      {
         """n_token""": """vocab_size""",  # Backward compatibility
         """hidden_size""": """d_model""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }






     def __init__(				self		,						_lowercase=32_000		,						_lowercase=1_024		,						_lowercase=24		,						_lowercase=16		,						_lowercase=4_096		,						_lowercase="gelu"		,						_lowercase=True		,						_lowercase="bi"		,						_lowercase=0.02		,						_lowercase=1e-1_2		,						_lowercase=0.1		,						_lowercase=512		,						_lowercase=None		,						_lowercase=True		,						_lowercase=False		,						_lowercase=False		,						_lowercase=-1		,						_lowercase=False		,						_lowercase="last"		,						_lowercase=True		,						_lowercase="tanh"		,						_lowercase=0.1		,						_lowercase=5		,						_lowercase=5		,						_lowercase=5		,						_lowercase=1		,						_lowercase=2		,						**_lowercase		,						)->     Tuple:
         UpperCamelCase_			  =						vocab_size
         UpperCamelCase_			  =						d_model
         UpperCamelCase_			  =						n_layer
         UpperCamelCase_			  =						n_head
         if d_model % n_head != 0:
             raise ValueError(F"\'d_model % n_head\' ({d_model % n_head}) should be equal to 0"	)
         if "d_head" in kwargs:
             if kwargs["d_head"] != d_model // n_head:
                 raise ValueError(
                     F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"	)
         UpperCamelCase_			  =						d_model // n_head
         UpperCamelCase_			  =						ff_activation
         UpperCamelCase_			  =						d_inner
         UpperCamelCase_			  =						untie_r
         UpperCamelCase_			  =						attn_type

         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						layer_norm_eps

         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						mem_len
         UpperCamelCase_			  =						reuse_len
         UpperCamelCase_			  =						bi_data
         UpperCamelCase_			  =						clamp_len
         UpperCamelCase_			  =						same_length

         UpperCamelCase_			  =						summary_type
         UpperCamelCase_			  =						summary_use_proj
         UpperCamelCase_			  =						summary_activation
         UpperCamelCase_			  =						summary_last_dropout
         UpperCamelCase_			  =						start_n_top
         UpperCamelCase_			  =						end_n_top

         UpperCamelCase_			  =						bos_token_id
         UpperCamelCase_			  =						pad_token_id
         UpperCamelCase_			  =						eos_token_id

         if "use_cache" in kwargs:
             warnings.warn(
                 "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
                 " instead."		,						A_		,						)
             UpperCamelCase_			  =						kwargs['''use_cache''']

         UpperCamelCase_			  =						use_mems_eval
         UpperCamelCase_			  =						use_mems_train
         super().__init__(pad_token_id=A_		,						bos_token_id=A_		,						eos_token_id=A_		,						**A_	)






     @property
     def 	UpperCAmelCase_				(				self	)->     int:
         logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."	)
         return -1






     @max_position_embeddings.setter
     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         raise NotImplementedError(
             F"The model {self.model_type} is one of the few models that has no sequence length limit."	)


"""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
