





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   Tuple)       ->Dict:



   '''simple docstring'''



   raise NotImplementedError()







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   raise NotImplementedError()



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Dict       , UpperCAmelCase_				:   "AutoTokenizer"       , UpperCAmelCase_				:   bool = False       , **UpperCAmelCase_				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =tokenizer
   lowerCamelCase__:      List[Any]			  =skip_prompt
   lowerCamelCase__:      Dict			  =decode_kwargs

   # variables used in the streaming process
   lowerCamelCase__:      Optional[int]			  =[]
   lowerCamelCase__:      List[str]			  =0
   lowerCamelCase__:      List[Any]			  =True

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   List[Any])       ->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:
     lowerCamelCase__:      Optional[int]			  =value[0]

   if self.skip_prompt and self.next_tokens_are_prompt:
     lowerCamelCase__:      Any			  =False
     return

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

   # After the symbol for a new line, we flush the cache.
   if text.endswith("\n"):
     lowerCamelCase__:      Any			  =text[self.print_len :]
     lowerCamelCase__:      Union[str, Any]			  =[]
     lowerCamelCase__:      str			  =0
   # If the last token is a CJK character, we print the characters.
   elif len(UpperCAmelCase_) > 0 and self._is_chinese_char(ord(text[-1])):
     lowerCamelCase__:      Any			  =text[self.print_len :]
     self.print_len += len(UpperCAmelCase_)
   # 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:
     lowerCamelCase__:      str			  =text[self.print_len : text.rfind(" ") + 1]
     self.print_len += len(UpperCAmelCase_)

   self.on_finalized_text(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   if len(self.token_cache) > 0:
     lowerCamelCase__:      Tuple			  =self.tokenizer.decode(self.token_cache       , **self.decode_kwargs)
     lowerCamelCase__:      Optional[int]			  =text[self.print_len :]
     lowerCamelCase__:      List[str]			  =[]
     lowerCamelCase__:      Tuple			  =0
   else:
     lowerCamelCase__:      str			  =""

   lowerCamelCase__:      List[str]			  =True
   self.on_finalized_text(UpperCAmelCase_       , stream_end=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   bool = False)       ->int:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Optional[int])       ->int:



   '''simple docstring'''



   if (
       (cp >= 0x4E00 and cp <= 0x9FFF)
       or (cp >= 0x3400 and cp <= 0x4DBF)  #
       or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
       or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
       or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
       or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
       or (cp >= 0xF900 and cp <= 0xFAFF)
       or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
   ):  #
     return True

   return False



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Union[str, Any]       , UpperCAmelCase_				:   "AutoTokenizer"       , UpperCAmelCase_				:   bool = False       , UpperCAmelCase_				:   Optional[float] = None       , **UpperCAmelCase_				:   Optional[int])       ->Optional[Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_       , UpperCAmelCase_       , **UpperCAmelCase_)
   lowerCamelCase__:      Any			  =Queue()
   lowerCamelCase__:      Any			  =None
   lowerCamelCase__:      int			  =timeout

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   bool = False)       ->Union[str, Any]:



   '''simple docstring'''



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

 def __iter__(self				:   List[str])       ->Dict:



   '''simple docstring'''



   return self







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



   '''simple docstring'''



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








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

