





'''simple docstring'''

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

from utils import logger






class    UpperCAmelCase	(       UpperCamelCase__				):
 def __init__(				self      :Tuple						,       lowercase_      :str						,       lowercase_      :List[Any]							)->							List[Any]:
     A__				=			params

     A__				=			np.array(lowercase_							)
     A__				=			np.array([len(lowercase_							) 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      :Tuple						,       lowercase_      :Optional[int]							)->							Optional[int]:
     return (self.token_ids[index], self.lengths[index])
 def __len__(				self      :Optional[Any]							)->							Any:
     return len(self.lengths							)
 def      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     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      :Optional[Any]							)->							Union[str, Any]:
     A__				=			self.params.max_model_input_size
     A__				=			self.lengths > max_len
     logger.info(F"Splitting {sum(lowercase_							)} too long sequences."							)

     def divide_chunks(lowercase_      :int						,       lowercase_      :Tuple							):
         return [l[i : i + n] for i in range(0						,       len(lowercase_							)						,       lowercase_							)]

     A__				=			[]
     A__				=			[]
     if self.params.mlm:
         A__,	A__				=			self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
     else:
         A__,	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(lowercase_						,       0						,       lowercase_							)
                 if sub_s[-1] != sep_id:
                     A__				=			np.insert(lowercase_						,       len(lowercase_							)						,       lowercase_							)
                 assert len(lowercase_							) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(lowercase_							)

             new_tok_ids.extend(lowercase_							)
             new_lengths.extend([len(lowercase_							) for l in sub_seqs]							)

     A__				=			np.array(lowercase_							)
     A__				=			np.array(lowercase_							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							str:
     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      :Dict							)->							Any:
     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      :Union[str, Any]							)->							int:
     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      :List[Any]						,       lowercase_      :Tuple							)->							Any:
     A__				=			[t[0] for t in batch]
     A__				=			[t[1] for t in batch]
     assert len(lowercase_							) == len(lowercase_							)

     # Max for paddings
     A__				=			max(lowercase_							)

     # 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(lowercase_							)							) + [pad_idx] * (max_seq_len_ - len(lowercase_							)) for t in token_ids]
     assert len(tk_							) == len(lowercase_							)
     assert all(len(lowercase_							) == max_seq_len_ for t in tk_							)

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










'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



