





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

from utils import logger




class     SCREAMING_SNAKE_CASE__ (						__snake_case      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						params

        A__											=						np.array(A_ )
        A__											=						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,__lowerCamelCase ):
        return (self.token_ids[index], self.lengths[index])



 def __len__(							self ):
        return len(self.lengths )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        A__											=						self.params.max_model_input_size
        A__											=						self.lengths > max_len
        logger.info(f"Splitting {sum(A_ )} too long sequences." )

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

        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(A_,0,A_ )
                             if sub_s[-1] != sep_id:
                                    A__											=						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] )

        A__											=						np.array(A_ )
        A__											=						np.array(A_ )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):




        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 				UpperCamelCase			(							self ):




        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 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[t[0] for t in batch]
        A__											=						[t[1] for t in batch]
        assert len(A_ ) == len(A_ )

        # Max for paddings
        A__											=						max(A_ )

        # 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(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_ )

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












import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline



@pytest.mark.integration
def 		A_				(     )						-> List[Any]:
 with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT     ):
  with pytest.raises(_lowerCAmelCase     ):
   requests.request("GET"							,   "https://huggingface.co"     )
  with pytest.raises(requests.exceptions.ConnectTimeout     ):
   requests.request("GET"							,   "https://huggingface.co"							,   timeout=1.0     )



@pytest.mark.integration
def 		A_				(     )						-> Tuple:
 with offline(OfflineSimulationMode.CONNECTION_FAILS     ):
  with pytest.raises(requests.exceptions.ConnectionError     ):
   requests.request("GET"							,   "https://huggingface.co"     )



def 		A_				(     )						-> Optional[int]:
 with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1     ):
  with pytest.raises(_lowerCAmelCase     ):
   http_head("https://huggingface.co"     )
