
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)


__lowerCamelCase							=       {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase							=       {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase							=       {"""facebook/blenderbot_small-90M""": 5_12}


def 			UpperCamelCase					(    __lowerCamelCase     : Any    ):
       snake_case       :   Optional[Any]							=      set()
       snake_case       :   Tuple							=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              snake_case       :   Tuple							=      char

       snake_case       :   int							=      set(__lowerCamelCase    )
       return pairs






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Any						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		Optional[int]						     =     ["input_ids", "attention_mask"]



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict="__start__"    ,						snake_case__		:					Any="__end__"    ,						snake_case__		:					Tuple="__unk__"    ,						snake_case__		:					Dict="__null__"    ,						**snake_case__		:					List[Any]    ,						)		->	List[str]:


          '''simple docstring'''


          super().__init__(unk_token=snake_case__    ,						bos_token=snake_case__    ,						eos_token=snake_case__    ,						pad_token=snake_case__    ,						**snake_case__ )

          with open(snake_case__    ,						encoding="utf-8" ) as vocab_handle:
                 snake_case       :   Optional[int]							=      json.load(snake_case__ )
          snake_case       :   Any							=      {v: k for k, v in self.encoder.items()}
          with open(snake_case__    ,						encoding="utf-8" ) as merges_handle:
                 snake_case       :   List[Any]							=      merges_handle.read().split("\n" )[1:-1]
          snake_case       :   int							=      [tuple(merge.split() ) for merge in merges]
          snake_case       :   Any							=      dict(zip(snake_case__    ,						range(len(snake_case__ ) ) ) )
          snake_case       :   Optional[Any]							=      {}



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          return len(self.encoder )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Dict:


          '''simple docstring'''


          return dict(self.encoder    ,						**self.added_tokens_encoder )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					str )		->	str:


          '''simple docstring'''


          if token in self.cache:
                 return self.cache[token]
          snake_case       :   Union[str, Any]							=      re.sub("([.,!?()])"    ,						R" \1"    ,						snake_case__ )
          snake_case       :   int							=      re.sub("(')"    ,						R" \1 "    ,						snake_case__ )
          snake_case       :   str							=      re.sub(R"\s{2,}"    ,						" "    ,						snake_case__ )
          if "\n" in token:
                 snake_case       :   List[Any]							=      token.replace("\n"    ,						" __newln__" )

          snake_case       :   Optional[Any]							=      token.split(" " )
          snake_case       :   str							=      []
          for token in tokens:
                 if not len(snake_case__ ):
                        continue

                 snake_case       :   Any							=      token.lower()
                 snake_case       :   Any							=      tuple(snake_case__ )
                 snake_case       :   str							=      tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
                 snake_case       :   Optional[int]							=      get_pairs(snake_case__ )

                 if not pairs:
                        words.append(snake_case__ )
                        continue

                 while True:
                        snake_case       :   Optional[int]							=      min(snake_case__    ,						key=lambda snake_case__ : self.bpe_ranks.get(snake_case__    ,						float("inf" ) ) )
                        if bigram not in self.bpe_ranks:
                               break
                        snake_case				,					snake_case       :   Tuple							=      bigram
                        snake_case       :   Tuple							=      []
                        snake_case       :   str							=      0

                        while i < len(snake_case__ ):
                               try:
                                      snake_case       :   str							=      word.index(snake_case__    ,						snake_case__ )
                                      new_word.extend(word[i:j] )
                                      snake_case       :   Dict							=      j
                               except ValueError:
                                      new_word.extend(word[i:] )
                                      break

                               if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
                                      new_word.append(first + second )
                                      i += 2
                               else:
                                      new_word.append(word[i] )
                                      i += 1
                        snake_case       :   Optional[Any]							=      tuple(snake_case__ )
                        snake_case       :   List[Any]							=      new_word
                        if len(snake_case__ ) == 1:
                               break
                        else:
                               snake_case       :   Any							=      get_pairs(snake_case__ )
                 snake_case       :   List[str]							=      "@@ ".join(snake_case__ )
                 snake_case       :   Union[str, Any]							=      word[:-4]

                 snake_case       :   int							=      word
                 words.append(snake_case__ )
          return " ".join(snake_case__ )



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


          '''simple docstring'''


          snake_case       :   Tuple							=      []

          snake_case       :   Tuple							=      re.findall(R"\S+\n?"    ,						snake_case__ )

          for token in words:
                 split_tokens.extend(list(self.bpe(snake_case__ ).split(" " ) ) )
          return split_tokens



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str )		->	int:


          '''simple docstring'''


          snake_case       :   Tuple							=      token.lower()
          return self.encoder.get(snake_case__    ,						self.encoder.get(self.unk_token ) )



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


          '''simple docstring'''


          return self.decoder.get(snake_case__    ,						self.unk_token )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					List[str] )		->	str:


          '''simple docstring'''


          snake_case       :   str							=      " ".join(snake_case__ ).replace("@@ "    ,						"" ).strip()
          return out_string



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


          '''simple docstring'''


          if not os.path.isdir(snake_case__ ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                 return
          snake_case       :   int							=      os.path.join(
              snake_case__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
          snake_case       :   List[Any]							=      os.path.join(
              snake_case__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

          with open(snake_case__    ,						"w"    ,						encoding="utf-8" ) as f:
                 f.write(json.dumps(self.encoder    ,						indent=2    ,						sort_keys=snake_case__    ,						ensure_ascii=snake_case__ ) + "\n" )

          snake_case       :   Tuple							=      0
          with open(snake_case__    ,						"w"    ,						encoding="utf-8" ) as writer:
                 writer.write("#version: 0.2\n" )
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,						key=lambda snake_case__ : kv[1] ):
                        if index != token_index:
                               logger.warning(
                                   f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                   " Please check that the tokenizer is not corrupted!" )
                               snake_case       :   List[Any]							=      token_index
                        writer.write(" ".join(snake_case__ ) + "\n" )
                        index += 1

          return vocab_file, merge_file







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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """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   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
          snake_case       :   Optional[Any]							=      model
          snake_case       :   Dict							=      kwargs.get("model_save_dir"    ,						snake_case__ )
          snake_case       :   int							=      kwargs.get("latest_model_name"    ,						snake_case__ )



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {k: np.array(snake_case__ ) for k, v in kwargs.items()}
          return self.model.run(snake_case__    ,						snake_case__ )



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Optional[int]=None )		->	Any:


          '''simple docstring'''


          if provider is None:
                 logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
                 snake_case       :   Optional[int]							=      "CPUExecutionProvider"

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

          snake_case       :   Any							=      self.model_save_dir.joinpath(self.latest_model_name )
          snake_case       :   str							=      Path(snake_case__ ).joinpath(snake_case__ )
          try:
                 shutil.copyfile(snake_case__    ,						snake_case__ )
          except shutil.SameFileError:
                 pass

          # copy external weights (for models >2GB)
          snake_case       :   List[str]							=      self.model_save_dir.joinpath(snake_case__ )
          if src_path.exists():
                 snake_case       :   Tuple							=      Path(snake_case__ ).joinpath(snake_case__ )
                 try:
                        shutil.copyfile(snake_case__    ,						snake_case__ )
                 except shutil.SameFileError:
                        pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''simple docstring'''


          if os.path.isfile(snake_case__ ):
                 logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
                 return

          os.makedirs(snake_case__    ,						exist_ok=snake_case__ )

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



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Tuple    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[Union[bool, str, None]] = None    ,						snake_case__		:					Optional[Union[str, None]] = None    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional["ort.SessionOptions"] = None    ,						**snake_case__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(snake_case__ ):
                 snake_case       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ )
          # load model from hub
          else:
                 # download model
                 snake_case       :   Dict							=      hf_hub_download(
                     repo_id=snake_case__    ,						filename=snake_case__    ,						use_auth_token=snake_case__    ,						revision=snake_case__    ,						cache_dir=snake_case__    ,						force_download=snake_case__    ,						)
                 snake_case       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      OnnxRuntimeModel.load_model(snake_case__    ,						provider=snake_case__    ,						sess_options=snake_case__ )
          return cls(model=snake_case__    ,						**snake_case__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      model_id.split("@" )

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





