



"""simple docstring"""




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,
 )





"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:   List[str]				=				{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase							:   List[str]				=				{
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  Dict						=					GPTaTokenizer



      def __init__(	self	:      Optional[Any]	,      A	:      Optional[int]=None	,      A	:      List[Any]=None	,      A	:      Optional[int]=None	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Union[str, Any]="<|endoftext|>"	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Optional[Any]=False	,      **A	:      str	,      ):
           super().__init__(
               A	,      A	,      tokenizer_file=A	,      unk_token=A	,      bos_token=A	,      eos_token=A	,      add_prefix_space=A	,      **A	,      )

           _UpperCAmelCase     :      List[Any]														=       kwargs.pop("add_bos_token"	,      A		)

           _UpperCAmelCase     :      List[str]														=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
           if pre_tok_state.get("add_prefix_space"	,      A		) != add_prefix_space:
                _UpperCAmelCase     :      Dict														=       getattr(A	,      pre_tok_state.pop("type"		)		)
                _UpperCAmelCase     :      Optional[Any]														=       add_prefix_space
                _UpperCAmelCase     :      Optional[Any]														=       pre_tok_class(**A		)

           _UpperCAmelCase     :      List[Any]														=       add_prefix_space



      def 		snake_case_   (	self	:      List[Any]	,      *A	:      List[Any]	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)
           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      List[Any]	,      **A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)

           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      List[Any]														=       self._tokenizer.model.save(A	,      name=A		)
           return tuple(A		)



      def 		snake_case_   (	self	:      int	,      A	:      "Conversation"		):
           _UpperCAmelCase     :      List[str]														=       []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(A	,      add_special_tokens=A		) + [self.eos_token_id]		)

           if len(A		) > self.model_max_length:
                _UpperCAmelCase     :      Optional[int]														=       input_ids[-self.model_max_length :]
           return input_ids
