





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case								=							{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Dict  			=	'''albert'''







       def __init__(     self						:						Optional[int]    ,__A						:						Tuple=3_0000    ,__A						:						int=128    ,__A						:						List[Any]=4096    ,__A						:						List[str]=12    ,__A						:						str=1    ,__A						:						Dict=64    ,__A						:						Any=1_6384    ,__A						:						Union[str, Any]=1    ,__A						:						List[Any]="gelu_new"    ,__A						:						List[Any]=0    ,__A						:						Any=0    ,__A						:						Tuple=512    ,__A						:						List[str]=2    ,__A						:						Tuple=0.02    ,__A						:						Dict=1e-12    ,__A						:						Tuple=0.1    ,__A						:						str="absolute"    ,__A						:						List[Any]=0    ,__A						:						str=2    ,__A						:						Optional[Any]=3    ,**__A						:						List[str]    ,)    ->       Dict:
         super().__init__(pad_token_id=__A    ,bos_token_id=__A    ,eos_token_id=__A    ,**__A	)

         _lowercase						    =			vocab_size
         _lowercase						    =			embedding_size
         _lowercase						    =			hidden_size
         _lowercase						    =			num_hidden_layers
         _lowercase						    =			num_hidden_groups
         _lowercase						    =			num_attention_heads
         _lowercase						    =			inner_group_num
         _lowercase						    =			hidden_act
         _lowercase						    =			intermediate_size
         _lowercase						    =			hidden_dropout_prob
         _lowercase						    =			attention_probs_dropout_prob
         _lowercase						    =			max_position_embeddings
         _lowercase						    =			type_vocab_size
         _lowercase						    =			initializer_range
         _lowercase						    =			layer_norm_eps
         _lowercase						    =			classifier_dropout_prob
         _lowercase						    =			position_embedding_type





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       @property
       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
           _lowercase						    =			{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           _lowercase						    =			{0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]	)

"""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
