



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
    from .camera import create_pan_cameras
    from .pipeline_shap_e import ShapEPipeline
    from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
    from .renderer import (
        BoundingBoxVolume,
        ImportanceRaySampler,
        MLPNeRFModelOutput,
        MLPNeRSTFModel,
        ShapEParamsProjModel,
        ShapERenderer,
        StratifiedRaySampler,
        VoidNeRFModel,
    )








import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	       =	logging.get_logger(__name__)
__UpperCAmelCase	       =	{'''vocab_file''': '''spiece.model'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

__UpperCAmelCase	       =	{
    '''AI-Sweden/gpt-sw3-126m''': 2_048,
    '''AI-Sweden/gpt-sw3-350m''': 2_048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_048,
    '''AI-Sweden/gpt-sw3-20b''': 2_048,
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Union[str, Any]       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : Any       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : int       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : Optional[int]       					=  ["input_ids", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		None:
          UpperCamelCase   :  Optional[Any]						     =     {} if sp_model_kwargs is None else sp_model_kwargs

          UpperCamelCase   :  Dict						     =     kwargs.get('name_or_path'	)
          if name_or_path is None:
             logger.warning(
                 'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                 ' you are testing the model, this can safely be ignored'	)
             UpperCamelCase   :  Tuple						     =     'None'

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          UpperCamelCase   :  str						     =     '<|endoftext|>' if eos_token is None else eos_token
          UpperCamelCase   :  Tuple						     =     '<unk>' if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
             UpperCamelCase   :  str						     =     unk_token if pad_token is None else pad_token
             UpperCamelCase   :  List[str]						     =     eos_token if bos_token is None else bos_token
          else:
             UpperCamelCase   :  List[Any]						     =     '<pad>' if pad_token is None else pad_token
             UpperCamelCase   :  Dict						     =     '<s>' if bos_token is None else bos_token

          super().__init__(
              do_lower_case=SCREAMING_SNAKE_CASE_,      remove_space=SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      sp_model_kwargs=self.sp_model_kwargs,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  List[str]						     =     do_lower_case
          UpperCamelCase   :  List[str]						     =     remove_space
          UpperCamelCase   :  List[Any]						     =     keep_accents
          UpperCamelCase   :  List[str]						     =     vocab_file

          UpperCamelCase   :  Union[str, Any]						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(SCREAMING_SNAKE_CASE_	)

          # Used for whitespace normalization in input texts
          # fmt : off
          UpperCamelCase   :  Dict						     =     {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          UpperCamelCase   :  List[Any]						     =     re.compile(
              F"""[{"".join(map(SCREAMING_SNAKE_CASE_,      list(range(0,      9	)	) + list(range(11,      32	)	) + list(range(127,      160	)	) + [160, 173, 8203]	)	)}]"""	)
       def __getstate__(      self	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     self.__dict__.copy()
          UpperCamelCase   :  Optional[int]						     =     None
          return state
       def __setstate__(      self,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  Any						     =     d

          # for backward compatibility
          if not hasattr(self,      'sp_model_kwargs'	):
             UpperCamelCase   :  Optional[int]						     =     {}

          UpperCamelCase   :  int						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(self.vocab_file	)
       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def  snake_case_	(      self	)					->		int:
          return len(self.sp_model	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Dict						     =     self.non_printing_characters_re.sub('',      SCREAMING_SNAKE_CASE_	)

          # Normalize whitespaces
          UpperCamelCase   :  Any						     =     ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

          # NFC Unicode normalization
          UpperCamelCase   :  Dict						     =     unicodedata.normalize('NFC',      SCREAMING_SNAKE_CASE_	)
          return text
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  Any						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,      out_type=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		int:
          return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
       @staticmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		str:
          return out_string
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     []
          UpperCamelCase   :  List[Any]						     =     ''
          UpperCamelCase   :  str						     =     False
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                if not prev_is_special:
                   out_string += " "

                out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                UpperCamelCase   :  Dict						     =     True
                UpperCamelCase   :  Optional[Any]						     =     []
             else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
                UpperCamelCase   :  str						     =     False
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)

          return out_string
       def  snake_case_	(      self	)					->		Dict[str, int]:
          UpperCamelCase   :  Tuple						     =     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
             return
          UpperCamelCase   :  List[str]						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,      SCREAMING_SNAKE_CASE_	)
          elif not os.path.isfile(self.vocab_file	):
             with open(SCREAMING_SNAKE_CASE_,      'wb'	) as fi:
                UpperCamelCase   :  Any						     =     self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_	)

          return (out_vocab_file,)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False	)					->		Union[List[int], List[List[int]], "torch.Tensor"]:

          if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  List[str]						     =     self.preprocess_text(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Optional[Any]						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  Union[str, Any]						     =     [self.preprocess_text(SCREAMING_SNAKE_CASE_	) for t in text]
             UpperCamelCase   :  Any						     =     self.sp_model.encode(SCREAMING_SNAKE_CASE_	)

          if return_tensors is True or return_tensors == "pt":
             UpperCamelCase   :  List[Any]						     =     torch.tensor(SCREAMING_SNAKE_CASE_	)

          return token_ids
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          return self.sp_model.decode(SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[int]:
          UpperCamelCase   :  List[Any]						     =     [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
          UpperCamelCase   :  Optional[Any]						     =     (
              F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE_	) + F"""{self.bos_token}Bot:"""
          )
          return self.encode(text=SCREAMING_SNAKE_CASE_	)



