


"""simple docstring"""

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

from utils import logger





class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Optional[Any]   ,							A_						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						params

       __lowercase						=						np.array(A_				)
       __lowercase						=						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						:						int   ,							A_						:						Any				):






       '''simple docstring'''


       return (self.token_ids[index], self.lengths[index])
      def __len__(		self						:						Optional[Any]				):






       '''simple docstring'''


       return len(self.lengths				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''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 SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						self.params.max_model_input_size
       __lowercase						=						self.lengths > max_len
       logger.info(F'''Splitting {sum(A_				)} too long sequences.'''				)

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

       __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(A_   ,							0   ,							A_				)
          if sub_s[-1] != sep_id:
           __lowercase						=						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						=						np.array(A_				)
       __lowercase						=						np.array(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						len(self				)
       __lowercase						=						self.lengths > 1_1
       __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 SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						[t[0] for t in batch]
       __lowercase						=						[t[1] for t in batch]
       assert len(A_				) == len(A_				)

       # Max for paddings
       __lowercase						=						max(A_				)

       # 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(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						=						torch.tensor(tk_				)  # (bs, max_seq_len_)
       __lowercase						=						torch.tensor(A_				)  # (bs)
       return tk_t, lg_t




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE					:				List[str]									=		{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Optional[int]									=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





