





'''simple docstring'''




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer







class   A_	:




 def      lowercase	(					self       :    str	,			snake_case_       :    int    ):
      raise NotImplementedError()




 def      lowercase	(					self       :    Any    ):
      raise NotImplementedError()







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    str	,			snake_case_       :    "AutoTokenizer"	,			snake_case_       :    bool = False	,			**snake_case_       :    Tuple    ):
      _UpperCAmelCase     							=   tokenizer
      _UpperCAmelCase     							=   skip_prompt
      _UpperCAmelCase     							=   decode_kwargs

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




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[str]    ):
      if len(value.shape    ) > 1 and value.shape[0] > 1:
           raise ValueError("TextStreamer only supports batch size 1"    )
      elif len(value.shape    ) > 1:
           _UpperCAmelCase     							=   value[0]

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

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

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

      self.on_finalized_text(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      # Flush the cache, if it exists
      if len(self.token_cache    ) > 0:
           _UpperCAmelCase     							=   self.tokenizer.decode(self.token_cache	,			**self.decode_kwargs    )
           _UpperCAmelCase     							=   text[self.print_len :]
           _UpperCAmelCase     							=   []
           _UpperCAmelCase     							=   0
      else:
           _UpperCAmelCase     							=   ""

      _UpperCAmelCase     							=   True
      self.on_finalized_text(snake_case_	,			stream_end=snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str	,			snake_case_       :    bool = False    ):
      print(snake_case_	,			flush=snake_case_	,			end="" if not stream_end else None    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Tuple    ):
      # 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 >= 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   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    "AutoTokenizer"	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[float] = None	,			**snake_case_       :    Union[str, Any]    ):
      super().__init__(snake_case_	,			snake_case_	,			**snake_case_    )
      _UpperCAmelCase     							=   Queue()
      _UpperCAmelCase     							=   None
      _UpperCAmelCase     							=   timeout




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    bool = False    ):
      self.text_queue.put(snake_case_	,			timeout=self.timeout    )
      if stream_end:
           self.text_queue.put(self.stop_signal	,			timeout=self.timeout    )




 def __iter__(					self       :    str    ):
      return self




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.text_queue.get(timeout=self.timeout    )
      if value == self.stop_signal:
           raise StopIteration()
      else:
           return value












from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




