"""simple docstring"""



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

from utils import logger



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Any	,      A_							:     Union[str, Any]	,      A_							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							params

         __snake_case				=							np.array(A_    )
         __snake_case				=							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							:     Optional[int]	,      A_							:     str    )    ->       Tuple:
         return (self.token_ids[index], self.lengths[index])
    def __len__(		self							:     Tuple    )    ->       Tuple:
         return len(self.lengths    )
    def  lowercase					(		self							:     Tuple    )    ->       int:
         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    )    ->       Tuple:
         __snake_case				=							self.params.max_model_input_size
         __snake_case				=							self.lengths > max_len
         logger.info(f"Splitting {sum(A_    )} too long sequences."    )

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

         __snake_case				=							[]
         __snake_case				=							[]
         if self.params.mlm:
              __snake_case		,	__snake_case				=							self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
         else:
              __snake_case		,	__snake_case				=							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:
                   __snake_case				=							[]
                   for sub_s in divide_chunks(seq_	,      max_len - 2    ):
                        if sub_s[0] != cls_id:
                             __snake_case				=							np.insert(A_	,      0	,      A_    )
                        if sub_s[-1] != sep_id:
                             __snake_case				=							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]    )

         __snake_case				=							np.array(A_    )
         __snake_case				=							np.array(A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         __snake_case				=							len(self    )
         __snake_case				=							self.lengths > 11
         __snake_case				=							self.token_ids[indices]
         __snake_case				=							self.lengths[indices]
         __snake_case				=							len(self    )
         logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."    )
    def  lowercase					(		self							:     Any    )    ->       int:
         if "unk_token" not in self.params.special_tok_ids:
              return
         else:
              __snake_case				=							self.params.special_tok_ids['''unk_token''']
         __snake_case				=							len(self    )
         __snake_case				=							np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
         __snake_case				=							(unk_occs / self.lengths) < 0.5
         __snake_case				=							self.token_ids[indices]
         __snake_case				=							self.lengths[indices]
         __snake_case				=							len(self    )
         logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."    )
    def  lowercase					(		self							:     List[str]    )    ->       Union[str, 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  lowercase					(		self							:     Dict	,      A_							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							[t[0] for t in batch]
         __snake_case				=							[t[1] for t in batch]
         assert len(A_    ) == len(A_    )

         # Max for paddings
         __snake_case				=							max(A_    )

         # Pad token ids
         if self.params.mlm:
              __snake_case				=							self.params.special_tok_ids['''pad_token''']
         else:
              __snake_case				=							self.params.special_tok_ids['''unk_token''']
         __snake_case				=							[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_    )

         __snake_case				=							torch.tensor(tk_    )  # (bs, max_seq_len_)
         __snake_case				=							torch.tensor(A_    )  # (bs)
         return tk_t, lg_t
"""simple docstring"""



import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowercase		:		int														=							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case=False,      ):
     output_path.parent.mkdir(parents=snake_case,      exist_ok=snake_case)
     # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
     # so we check the torch version for backwards compatibility
     if is_torch_less_than_1_11:
          export(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      use_external_data_format=snake_case,      enable_onnx_checker=snake_case,      opset_version=snake_case,      )
     else:
          export(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      opset_version=snake_case,      )





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __snake_case				=							torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          __snake_case				=							'''cuda'''
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
     else:
          __snake_case				=							'''cpu'''
     __snake_case				=							Path(snake_case)

     # VAE DECODER
     __snake_case				=							AutoencoderKL.from_pretrained(model_path + '''/vae''')
     __snake_case				=							vae_decoder.config.latent_channels
     # forward only through the decoder part
     __snake_case				=							vae_decoder.decode
     onnx_export(
         snake_case,      model_args=(
             torch.randn(1,      snake_case,      25,      25).to(device=snake_case,      dtype=snake_case),
             False,
         ),      output_path=output_path / '''vae_decoder''' / '''model.onnx''',      ordered_input_names=['''latent_sample''', '''return_dict'''],      output_names=['''sample'''],      dynamic_axes={
             '''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
         },      opset=snake_case,      )
     del vae_decoder


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							argparse.ArgumentParser()

       parser.add_argument(
           "--model_path",
           type=str,
           required=True,
           help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
       )

       parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
       parser.add_argument(
           "--opset",
           default=14,
           type=int,
           help="The version of the ONNX operator set to use.",
       )
       parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

       __lowercase		:		List[Any]														=							parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")