





from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer







class 				__lowerCAmelCase       :







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



        '''simple docstring'''




        raise NotImplementedError()







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        raise NotImplementedError()







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




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

        # variables used in the streaming process
        a__	:					Dict										=[]
        a__	:					int										=0
        a__	:					str										=True







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, 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:
          a__	:					Any										=value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
          a__	:					Dict										=False
          return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()							)
        a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)

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

        self.on_finalized_text(lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




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

        a__	:					Any										=True
        self.on_finalized_text(lowerCAmelCase__		,      stream_end=lowerCAmelCase__							)







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



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        if (
            (cp >= 0X4E_00 and cp <= 0X9F_FF)
            or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
            or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
            or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
            or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
            or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
            or (cp >= 0XF9_00 and cp <= 0XFA_FF)
            or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
        ):  #
          return True

        return False







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						List[str]:



        '''simple docstring'''




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







      def __iter__(    self							)   ->						Dict:



        '''simple docstring'''




        return self







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					int										=self.text_queue.get(timeout=self.timeout							)
        if value == self.stop_signal:
          raise StopIteration()
        else:
          return value













import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			"""M-CLIP"""







      def __init__(    self		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=7_6_8		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					int										=transformerDimSize
        a__	:					Dict										=imageDimSize
        super().__init__(**lowerCAmelCase__							)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			MCLIPConfig







      def __init__(    self		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Tuple										=XLMRobertaModel(lowerCAmelCase__							)
        a__	:					List[str]										=torch.nn.Linear(
            in_features=config.transformerDimensions		,      out_features=config.numDims							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.transformer(input_ids=lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					int										=(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
        return self.LinearTransformation(lowerCAmelCase__							), embs






