




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 PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase	:						List[Any]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						Dict			=     "▁"

_UpperCAmelCase	:						Any			=     {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

_UpperCAmelCase	:						Union[str, Any]			=     {
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

_UpperCAmelCase	:						int			=     {
    "facebook/s2t-small-librispeech-asr": 1024,
}

_UpperCAmelCase	:						Optional[Any]			=     ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

_UpperCAmelCase	:						int			=     {"mustc": MUSTC_LANGS}




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						VOCAB_FILES_NAMES
       _a 					=						PRETRAINED_VOCAB_FILES_MAP
       _a 					=						MAX_MODEL_INPUT_SIZES
       _a 					=						['''input_ids''', '''attention_mask''']

       _a 					=						[]






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					List[Any]       ,					_lowerCAmelCase:					str="<s>"       ,					_lowerCAmelCase:					Optional[int]="</s>"       ,					_lowerCAmelCase:					Any="<pad>"       ,					_lowerCAmelCase:					Tuple="<unk>"       ,					_lowerCAmelCase:					List[str]=False       ,					_lowerCAmelCase:					List[str]=False       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[Dict[str, Any]] = None       ,					**_lowerCAmelCase:					List[str]       ,					):
         lowercase    :Union[str, Any]      	=						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase       ,					eos_token=_lowerCAmelCase       ,					unk_token=_lowerCAmelCase       ,					pad_token=_lowerCAmelCase       ,					do_upper_case=_lowerCAmelCase       ,					do_lower_case=_lowerCAmelCase       ,					tgt_lang=_lowerCAmelCase       ,					lang_codes=_lowerCAmelCase       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**_lowerCAmelCase       ,					)
         lowercase    :Tuple      	=						do_upper_case
         lowercase    :int      	=						do_lower_case

         lowercase    :str      	=						load_json(_lowerCAmelCase				)
         lowercase    :int      	=						{v: k for k, v in self.encoder.items()}
         lowercase    :Dict      	=						spm_file
         lowercase    :str      	=						load_spm(_lowerCAmelCase       ,					self.sp_model_kwargs				)

         if lang_codes is not None:
           lowercase    :List[str]      	=						lang_codes
           lowercase    :Union[str, Any]      	=						LANGUAGES[lang_codes]
           lowercase    :Any      	=						[F"<lang:{lang}>" for lang in self.langs]
           lowercase    :List[str]      	=						{lang: self.sp_model.PieceToId(F"<lang:{lang}>"				) for lang in self.langs}

           lowercase    :int      	=						self.lang_tokens
           lowercase    :Dict      	=						tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang				)
         else:
           lowercase    :int      	=						{}






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return len(self.encoder				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         return self._tgt_lang






       @tgt_lang.setter
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					str				):
         lowercase    :Optional[int]      	=						new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					str				):
         lowercase    :Optional[Any]      	=						self.lang_code_to_id[tgt_lang]
         lowercase    :Tuple      	=						[lang_code_id]






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					str				):
         return self.sp_model.encode(_lowerCAmelCase       ,					out_type=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					int				):
         return self.encoder.get(_lowerCAmelCase       ,					self.encoder[self.unk_token]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					int				):
         return self.decoder.get(_lowerCAmelCase       ,					self.unk_token				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					List[str]				):
         lowercase    :int      	=						[]
         lowercase    :Any      	=						""
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             lowercase    :Optional[Any]      	=						self.sp_model.decode(_lowerCAmelCase				)
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             lowercase    :List[Any]      	=						[]
           else:
             current_sub_tokens.append(_lowerCAmelCase				)
         lowercase    :Dict      	=						self.sp_model.decode(_lowerCAmelCase				)
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					Any=None				):
         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # 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.eos_token_id]






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None       ,					_lowerCAmelCase:					bool = False				):

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

         lowercase    :Union[str, Any]      	=						[1] * len(self.prefix_tokens				)
         lowercase    :Optional[int]      	=						[1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(_lowerCAmelCase				)) + suffix_ones
         return prefix_ones + ([0] * len(_lowerCAmelCase				)) + ([0] * len(_lowerCAmelCase				)) + suffix_ones






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         lowercase    :int      	=						self.encoder.copy()
         vocab.update(self.added_tokens_encoder				)
         return vocab






       def __getstate__(   self:					Tuple				):
         lowercase    :Tuple      	=						self.__dict__.copy()
         lowercase    :Any      	=						None
         return state






       def __setstate__(   self:					Optional[int]       ,					_lowerCAmelCase:					Dict				):
         lowercase    :List[Any]      	=						d

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

         lowercase    :List[str]      	=						load_spm(self.spm_file       ,					self.sp_model_kwargs				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[str] = None				):
         lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         assert save_dir.is_dir(), F"{save_directory} should be a directory"
         lowercase    :Union[str, Any]      	=						save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         lowercase    :List[Any]      	=						save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
         )

         save_json(self.encoder       ,					_lowerCAmelCase				)

         if os.path.abspath(self.spm_file				) != os.path.abspath(_lowerCAmelCase				) and os.path.isfile(self.spm_file				):
           copyfile(self.spm_file       ,					_lowerCAmelCase				)
         elif not os.path.isfile(self.spm_file				):
           with open(_lowerCAmelCase       ,					"wb"				) as fi:
             lowercase    :Optional[int]      	=						self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase				)

         return (str(_lowerCAmelCase				), str(_lowerCAmelCase				))







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :Tuple      	=						sentencepiece.SentencePieceProcessor(**lowerCamelCase   )
  spm.Load(str(lowerCamelCase   )   )
  return spm



def       UpperCAmelCase__  (     lowerCamelCase   ):
  with open(lowerCamelCase,			"r"   ) as f:
    return json.load(lowerCamelCase   )







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











import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
     import onnxruntime as ort


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}




class    __lowerCAmelCase       :






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					**_lowerCAmelCase:					Any				):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
         lowercase    :str      	=						model
         lowercase    :Any      	=						kwargs.get("model_save_dir"       ,					_lowerCAmelCase				)
         lowercase    :Dict      	=						kwargs.get("latest_model_name"       ,					_lowerCAmelCase				)






       def __call__(   self:					Optional[Any]       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						{k: np.array(_lowerCAmelCase				) for k, v in kwargs.items()}
         return self.model.run(_lowerCAmelCase       ,					_lowerCAmelCase				)






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					List[Any]=None				):
         if provider is None:
           logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
           lowercase    :int      	=						"CPUExecutionProvider"

         return ort.InferenceSession(_lowerCAmelCase       ,					providers=[provider]       ,					sess_options=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

         lowercase    :Tuple      	=						self.model_save_dir.joinpath(self.latest_model_name				)
         lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
         try:
           shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
         except shutil.SameFileError:
           pass

         # copy external weights (for models >2GB)
         lowercase    :Tuple      	=						self.model_save_dir.joinpath(_lowerCAmelCase				)
         if src_path.exists():
           lowercase    :Dict      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
           try:
             shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
           except shutil.SameFileError:
             pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					Union[str, os.PathLike]       ,					**_lowerCAmelCase:					int       ,					):
         if os.path.isfile(_lowerCAmelCase				):
           logger.error(F"Provided path ({save_directory}) should be a directory, not a file"				)
           return

         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)

         # saving model weights/files
         self._save_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					Dict       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[Union[bool, str, None]] = None       ,					_lowerCAmelCase:					Optional[Union[str, None]] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional["ort.SessionOptions"] = None       ,					**_lowerCAmelCase:					Optional[int]       ,					):
         lowercase    :List[str]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						OnnxRuntimeModel.load_model(
               os.path.join(_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
           lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         # load model from hub
         else:
           # download model
           lowercase    :str      	=						hf_hub_download(
               repo_id=_lowerCAmelCase       ,					filename=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					)
           lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).parent
           lowercase    :Tuple      	=						Path(_lowerCAmelCase				).name
           lowercase    :Tuple      	=						OnnxRuntimeModel.load_model(_lowerCAmelCase       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
         return cls(model=_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					str       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any       ,					):
         lowercase    :List[str]      	=						None
         if len(str(_lowerCAmelCase				).split("@"				)				) == 2:
           lowercase	,    lowercase    :Tuple      	=						model_id.split("@"				)

         return cls._from_pretrained(
             model_id=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)



