


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer







class       lowerCamelCase__   :





     def      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      raise NotImplementedError()





     def      __A					(self       )     ->   Optional[int]:
      raise NotImplementedError()







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase = False		,     **UpperCAmelCase       )     ->   Any:
      _lowercase							      =tokenizer
      _lowercase							      =skip_prompt
      _lowercase							      =decode_kwargs

      # variables used in the streaming process
      _lowercase							      =[]
      _lowercase							      =0
      _lowercase							      =True





     def      __A					(self		,     UpperCAmelCase       )     ->   int:
      if len(value.shape       ) > 1 and value.shape[0] > 1:
       raise ValueError('''TextStreamer only supports batch size 1'''       )
      elif len(value.shape       ) > 1:
       _lowercase							      =value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
       _lowercase							      =False
       return

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

      # After the symbol for a new line, we flush the cache.
      if text.endswith('''\n'''       ):
       _lowercase							      =text[self.print_len :]
       _lowercase							      =[]
       _lowercase							      =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]       )       ):
       _lowercase							      =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:
       _lowercase							      =text[self.print_len : text.rfind(''' '''       ) + 1]
       self.print_len += len(UpperCAmelCase       )

      self.on_finalized_text(UpperCAmelCase       )





     def      __A					(self       )     ->   Any:
      # Flush the cache, if it exists
      if len(self.token_cache       ) > 0:
       _lowercase							      =self.tokenizer.decode(self.token_cache		,     **self.decode_kwargs       )
       _lowercase							      =text[self.print_len :]
       _lowercase							      =[]
       _lowercase							      =0
      else:
       _lowercase							      =''''''

      _lowercase							      =True
      self.on_finalized_text(UpperCAmelCase		,     stream_end=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = False       )     ->   Union[str, Any]:
      print(UpperCAmelCase		,     flush=UpperCAmelCase		,     end='''''' if not stream_end else None       )





     def      __A					(self		,     UpperCAmelCase       )     ->   Optional[int]:
      # This defines a "chinese character" as anything in the CJK Unicode block:
      #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
      #
      # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
      # despite its name. The modern Korean Hangul alphabet is a different block,
      # as is Japanese Hiragana and Katakana. Those alphabets are used to write
      # space-separated words, so they are not treated specially and handled
      # like the all of the other languages.
      if (
          (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
          or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
          or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
          or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
          or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
          or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
      ):  #
       return True

      return False







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     **UpperCAmelCase       )     ->   List[str]:
      super().__init__(UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )
      _lowercase							      =Queue()
      _lowercase							      =None
      _lowercase							      =timeout





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = False       )     ->   Any:
      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]:
      return self





     def      __A					(self       )     ->   Dict:
      _lowercase							      =self.text_queue.get(timeout=self.timeout       )
      if value == self.stop_signal:
       raise StopIteration()
      else:
       return value





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

