import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ="▁"

lowerCAmelCase			:							List[str]     ={
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase			:							Union[str, Any]     ={
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowerCAmelCase			:							Union[str, Any]     ={
    "facebook/m2m100_418M": 1024,
}

# fmt: off
lowerCAmelCase			:							int     ={
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       ['input_ids', 'attention_mask']

      _snake_case    		=       []
      _snake_case    		=       []



      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str=None    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				Optional[int]="<s>"    ,  _UpperCamelCase     :				List[Any]="</s>"    ,  _UpperCamelCase     :				Tuple="</s>"    ,  _UpperCamelCase     :				str="<pad>"    ,  _UpperCamelCase     :				Optional[Any]="<unk>"    ,  _UpperCamelCase     :				Optional[Any]="m2m100"    ,  _UpperCamelCase     :				Any = None    ,  _UpperCamelCase     :				Any=8    ,  **_UpperCamelCase     :				int    ,  )				->None:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						{} if sp_model_kwargs is None else sp_model_kwargs

          _lowerCamelCase     :		Any 						=						language_codes
          _lowerCamelCase     :		Optional[int] 						=						FAIRSEQ_LANGUAGE_CODES[language_codes]
          _lowerCamelCase     :		int 						=						{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

          _lowerCamelCase     :		str 						=						kwargs.get("""additional_special_tokens"""    ,  [])
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(__a)
              for lang_code in fairseq_language_code
              if self.get_lang_token(__a) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=__a    ,  tgt_lang=__a    ,  bos_token=__a    ,  eos_token=__a    ,  sep_token=__a    ,  unk_token=__a    ,  pad_token=__a    ,  language_codes=__a    ,  sp_model_kwargs=self.sp_model_kwargs    ,  num_madeup_words=__a    ,  **__a    ,  )

          _lowerCamelCase     :		Optional[Any] 						=						vocab_file
          _lowerCamelCase     :		List[str] 						=						load_json(__a)
          _lowerCamelCase     :		Any 						=						{v: k for k, v in self.encoder.items()}
          _lowerCamelCase     :		int 						=						spm_file
          _lowerCamelCase     :		Any 						=						load_spm(__a    ,  self.sp_model_kwargs)

          _lowerCamelCase     :		Dict 						=						len(self.encoder)

          _lowerCamelCase     :		str 						=						{
              self.get_lang_token(__a): self.encoder_size + i for i, lang_code in enumerate(__a)
          }
          _lowerCamelCase     :		List[Any] 						=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(__a)}
          _lowerCamelCase     :		Union[str, Any] 						=						{v: k for k, v in self.lang_token_to_id.items()}

          _lowerCamelCase     :		Union[str, Any] 						=						src_lang if src_lang is not None else """en"""
          _lowerCamelCase     :		Optional[Any] 						=						tgt_lang
          _lowerCamelCase     :		Tuple 						=						self.get_lang_id(self._src_lang)
          self.set_src_lang_special_tokens(self._src_lang)

          _lowerCamelCase     :		Any 						=						num_madeup_words



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          return len(self.encoder) + len(self.lang_token_to_id)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->str:


          """simple docstring"""


          return self._src_lang



      @src_lang.setter
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Tuple)				->None:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						new_src_lang
          self.set_src_lang_special_tokens(self._src_lang)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Any)				->List[str]:


          """simple docstring"""


          return self.sp_model.encode(__a    ,  out_type=__a)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Tuple:


          """simple docstring"""


          if token in self.lang_token_to_id:
              return self.lang_token_to_id[token]
          return self.encoder.get(__a    ,  self.encoder[self.unk_token])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				List[str])				->str:


          """simple docstring"""


          if index in self.id_to_lang_token:
              return self.id_to_lang_token[index]
          return self.decoder.get(__a    ,  self.unk_token)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						[]
          _lowerCamelCase     :		Tuple 						=						""""""
          for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                  out_string += self.sp_model.decode(__a) + token
                  _lowerCamelCase     :		Tuple 						=						[]
              else:
                  current_sub_tokens.append(__a)
          out_string += self.sp_model.decode(__a)
          return out_string.strip()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				Dict = False)				->List[int]:


          """simple docstring"""


          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__a    ,  token_ids_a=__a    ,  already_has_special_tokens=__a)

          _lowerCamelCase     :		Union[str, Any] 						=						[1] * len(self.prefix_tokens)
          _lowerCamelCase     :		Dict 						=						[1] * len(self.suffix_tokens)
          if token_ids_a is None:
              return prefix_ones + ([0] * len(__a)) + suffix_ones
          return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[Any] = None)				->List[int]:


          """simple docstring"""


          if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						{self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab



      def __getstate__(					self     :				str)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.__dict__.copy()
          _lowerCamelCase     :		List[Any] 						=						None
          return state



      def __setstate__(					self     :				List[str]    ,  _UpperCamelCase     :				Union[str, Any])				->None:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						d

          # for backward compatibility
          if not hasattr(self    ,  """sp_model_kwargs"""):
              _lowerCamelCase     :		int 						=						{}

          _lowerCamelCase     :		List[Any] 						=						load_spm(self.spm_file    ,  self.sp_model_kwargs)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[Any] = None)				->Tuple[str]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						Path(__a)
          if not save_dir.is_dir():
              raise OSError(F"""{save_directory} should be a directory""")
          _lowerCamelCase     :		Optional[Any] 						=						save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
          )
          _lowerCamelCase     :		Dict 						=						save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
          )

          save_json(self.encoder    ,  __a)

          if os.path.abspath(self.spm_file) != os.path.abspath(__a) and os.path.isfile(self.spm_file):
              copyfile(self.spm_file    ,  __a)
          elif not os.path.isfile(self.spm_file):
              with open(__a    ,  """wb""") as fi:
                  _lowerCamelCase     :		str 						=						self.sp_model.serialized_model_proto()
                  fi.write(__a)

          return (str(__a), str(__a))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Any = "en"    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				str = "ro"    ,  **_UpperCamelCase     :				Dict    ,  )				->BatchEncoding:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						src_lang
          _lowerCamelCase     :		List[str] 						=						tgt_lang
          self.set_src_lang_special_tokens(self.src_lang)
          return super().prepare_seqaseq_batch(__a    ,  __a    ,  **__a)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Union[str, Any])				->int:


          """simple docstring"""


          if src_lang is None or tgt_lang is None:
              raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""")
          _lowerCamelCase     :		Union[str, Any] 						=						src_lang
          _lowerCamelCase     :		Tuple 						=						self(__a    ,  add_special_tokens=__a    ,  **__a)
          _lowerCamelCase     :		str 						=						self.get_lang_id(__a)
          _lowerCamelCase     :		Union[str, Any] 						=						tgt_lang_id
          return inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->int:


          """simple docstring"""


          self.set_src_lang_special_tokens(self.src_lang)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->str:


          """simple docstring"""


          self.set_tgt_lang_special_tokens(self.tgt_lang)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any])				->None:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.get_lang_token(__a)
          _lowerCamelCase     :		List[Any] 						=						self.lang_token_to_id[lang_token]
          _lowerCamelCase     :		Tuple 						=						[self.cur_lang_id]
          _lowerCamelCase     :		Optional[Any] 						=						[self.eos_token_id]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Dict)				->None:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.get_lang_token(__a)
          _lowerCamelCase     :		List[Any] 						=						self.lang_token_to_id[lang_token]
          _lowerCamelCase     :		int 						=						[self.cur_lang_id]
          _lowerCamelCase     :		Optional[int] 						=						[self.eos_token_id]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Union[str, Any])				->str:


          """simple docstring"""


          return self.lang_code_to_token[lang]




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Dict)				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_lang_token(__a)
          return self.lang_token_to_id[lang_token]







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						sentencepiece.SentencePieceProcessor(**__snake_case	)
    spm.Load(str(__snake_case	)	)
    return spm







def 						A__  (					__A	):







    '''simple docstring'''





    with open(__snake_case   , """r"""	) as f:
        return json.load(__snake_case	)







def 						A__  (					__A   , __A	):







    '''simple docstring'''





    with open(__snake_case   , """w"""	) as f:
        json.dump(__snake_case   , __snake_case   , indent=2	)





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # 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(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            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=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =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")

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




