

"""simple docstring"""


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer

class    __a							:






     '''simple docstring'''

     def     _a	(  self		,			_a     )     ->					List[Any]:


           """simple docstring"""


           raise NotImplementedError()





     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           raise NotImplementedError()





class    __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(  self		,			_a		,			_a = False		,			**_a     )     ->					Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		tokenizer
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		skip_prompt
           SCREAMING_SNAKE_CASE__      :					Any											=		decode_kwargs

           # variables used in the streaming process
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[]
           SCREAMING_SNAKE_CASE__      :					Tuple											=		0
           SCREAMING_SNAKE_CASE__      :					Tuple											=		True

     def     _a	(  self		,			_a     )     ->					Any:


           """simple docstring"""


           if len(value.shape     ) > 1 and value.shape[0] > 1:
                 raise ValueError("""TextStreamer only supports batch size 1"""     )
           elif len(value.shape     ) > 1:
                 SCREAMING_SNAKE_CASE__      :					Optional[int]											=		value[0]

           if self.skip_prompt and self.next_tokens_are_prompt:
                 SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		False
                 return

           # Add the new token to the cache and decodes the entire thing.
           self.token_cache.extend(value.tolist()     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.tokenizer.decode(self.token_cache		,			**self.decode_kwargs     )

           # After the symbol for a new line, we flush the cache.
           if text.endswith("""\n"""     ):
                 SCREAMING_SNAKE_CASE__      :					Any											=		text[self.print_len :]
                 SCREAMING_SNAKE_CASE__      :					int											=		[]
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		0
           # If the last token is a CJK character, we print the characters.
           elif len(lowercase_     ) > 0 and self._is_chinese_char(ord(text[-1]     )     ):
                 SCREAMING_SNAKE_CASE__      :					Dict											=		text[self.print_len :]
                 self.print_len += len(lowercase_     )
           # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
           # which may change with the subsequent token -- there are probably smarter ways to do this!)
           else:
                 SCREAMING_SNAKE_CASE__      :					int											=		text[self.print_len : text.rfind(""" """     ) + 1]
                 self.print_len += len(lowercase_     )

           self.on_finalized_text(lowercase_     )

     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           if len(self.token_cache     ) > 0:
                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		self.tokenizer.decode(self.token_cache		,			**self.decode_kwargs     )
                 SCREAMING_SNAKE_CASE__      :					Tuple											=		text[self.print_len :]
                 SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		[]
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		0
           else:
                 SCREAMING_SNAKE_CASE__      :					List[Any]											=		''

           SCREAMING_SNAKE_CASE__      :					Dict											=		True
           self.on_finalized_text(lowercase_		,			stream_end=lowercase_     )

     def     _a	(  self		,			_a		,			_a = False     )     ->					int:


           """simple docstring"""


           print(lowercase_		,			flush=lowercase_		,			end="""""" if not stream_end else None     )





     def     _a	(  self		,			_a     )     ->					Optional[Any]:


           """simple docstring"""


           if (
               (cp >= 0X4e00 and cp <= 0X9fff)
               or (cp >= 0X3400 and cp <= 0X4dbf)  #
               or (cp >= 0X20000 and cp <= 0X2a6df)  #
               or (cp >= 0X2a700 and cp <= 0X2b73f)  #
               or (cp >= 0X2b740 and cp <= 0X2b81f)  #
               or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
               or (cp >= 0Xf900 and cp <= 0Xfaff)
               or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
           ):  #
                 return True

           return False





class    __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(  self		,			_a		,			_a = False		,			_a = None		,			**_a     )     ->					str:


           """simple docstring"""


           super().__init__(lowercase_		,			lowercase_		,			**lowercase_     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		Queue()
           SCREAMING_SNAKE_CASE__      :					str											=		None
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		timeout

     def     _a	(  self		,			_a		,			_a = False     )     ->					Optional[Any]:


           """simple docstring"""


           self.text_queue.put(lowercase_		,			timeout=self.timeout     )
           if stream_end:
                 self.text_queue.put(self.stop_signal		,			timeout=self.timeout     )

     def __iter__(  self     )     ->					str:


           """simple docstring"""


           return self





     def     _a	(  self     )     ->					Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.text_queue.get(timeout=self.timeout     )
           if value == self.stop_signal:
                 raise StopIteration()
           else:
                 return value








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,
 )


