


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

from utils import logger





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       def __init__(						self				:				Any		,				__a				:				Optional[int]		,				__a				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   Optional[Any]		= params

          lowerCamelCase_						:   str		= np.array(__a							)
          lowerCamelCase_						:   Union[str, Any]		= 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				:				Dict		,				__a				:				List[str]							)					->Optional[Any]:
          return (self.token_ids[index], self.lengths[index])



       def __len__(						self				:				int							)					->Tuple:
          return len(self.lengths							)



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Optional[Any]:
          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 				_lowerCAmelCase							(						self				:				Tuple							)					->Any:
          lowerCamelCase_						:   Tuple		= self.params.max_model_input_size
          lowerCamelCase_						:   Union[str, Any]		= self.lengths > max_len
          logger.info(F'''Splitting {sum(__a							)} too long sequences.'''							)

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

          lowerCamelCase_						:   Optional[int]		= []
          lowerCamelCase_						:   List[str]		= []
          if self.params.mlm:
             lowerCamelCase_,	lowerCamelCase_						:   List[str]		= self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
             lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= 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:
                lowerCamelCase_						:   List[Any]		= []
                for sub_s in divide_chunks(seq_		,				max_len - 2							):
                   if sub_s[0] != cls_id:
                      lowerCamelCase_						:   Tuple		= np.insert(__a		,				0		,				__a							)
                   if sub_s[-1] != sep_id:
                      lowerCamelCase_						:   Dict		= 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]							)

          lowerCamelCase_						:   Dict		= np.array(__a							)
          lowerCamelCase_						:   int		= np.array(__a							)



       def 				_lowerCAmelCase							(						self				:				Any							)					->Optional[int]:
          lowerCamelCase_						:   Any		= len(self							)
          lowerCamelCase_						:   str		= self.lengths > 11
          lowerCamelCase_						:   Any		= self.token_ids[indices]
          lowerCamelCase_						:   int		= self.lengths[indices]
          lowerCamelCase_						:   Union[str, Any]		= len(self							)
          logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''							)



       def 				_lowerCAmelCase							(						self				:				int							)					->int:
          if "unk_token" not in self.params.special_tok_ids:
             return
          else:
             lowerCamelCase_						:   List[Any]		= self.params.special_tok_ids["""unk_token"""]
          lowerCamelCase_						:   Any		= len(self							)
          lowerCamelCase_						:   Optional[Any]		= np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
          lowerCamelCase_						:   Any		= (unk_occs / self.lengths) < 0.5
          lowerCamelCase_						:   Any		= self.token_ids[indices]
          lowerCamelCase_						:   Union[str, Any]		= self.lengths[indices]
          lowerCamelCase_						:   List[Any]		= len(self							)
          logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''							)



       def 				_lowerCAmelCase							(						self				:				str							)					->Optional[Any]:
          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 				_lowerCAmelCase							(						self				:				int		,				__a				:				Union[str, Any]							)					->Optional[int]:
          lowerCamelCase_						:   Union[str, Any]		= [t[0] for t in batch]
          lowerCamelCase_						:   Tuple		= [t[1] for t in batch]
          assert len(__a							) == len(__a							)

          # Max for paddings
          lowerCamelCase_						:   str		= max(__a							)

          # Pad token ids
          if self.params.mlm:
             lowerCamelCase_						:   Dict		= self.params.special_tok_ids["""pad_token"""]
          else:
             lowerCamelCase_						:   Tuple		= self.params.special_tok_ids["""unk_token"""]
          lowerCamelCase_						:   List[str]		= [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_							)

          lowerCamelCase_						:   Optional[int]		= torch.tensor(tk_							)  # (bs, max_seq_len_)
          lowerCamelCase_						:   Optional[Any]		= torch.tensor(__a							)  # (bs)
          return tk_t, lg_t









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





