




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
			from ..models.auto import AutoTokenizer


class    lowerCamelCase    :






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Any:
								raise NotImplementedError()


			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Dict:
								raise NotImplementedError()






class    lowerCamelCase    (		__A						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Union[str, Any]       ,						lowerCamelCase_			:   Tuple = False       ,						**lowerCamelCase_			:   Optional[Any]  )						->   str:
								__magic_name__							:	List[Any]    							=							tokenizer
								__magic_name__							:	Optional[Any]    							=							skip_prompt
								__magic_name__							:	Optional[int]    							=							decode_kwargs

								# variables used in the streaming process
								__magic_name__							:	Union[str, Any]    							=							[]
								__magic_name__							:	str    							=							0
								__magic_name__							:	Tuple    							=							True

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   List[Any]  )						->   Union[str, Any]:





								if len(value.shape  ) > 1 and value.shape[0] > 1:
													raise ValueError('''TextStreamer only supports batch size 1'''  )
								elif len(value.shape  ) > 1:
													__magic_name__							:	Union[str, Any]    							=							value[0]

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

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()  )
								__magic_name__							:	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'''  ):
													__magic_name__							:	Tuple    							=							text[self.print_len :]
													__magic_name__							:	List[Any]    							=							[]
													__magic_name__							:	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]  )  ):
													__magic_name__							:	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:
													__magic_name__							:	Optional[int]    							=							text[self.print_len : text.rfind(''' '''  ) + 1]
													self.print_len += len(lowerCamelCase_  )

								self.on_finalized_text(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:





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

								__magic_name__							:	Tuple    							=							True
								self.on_finalized_text(lowerCamelCase_       ,						stream_end=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Any = False  )						->   Tuple:
								print(lowerCamelCase_       ,						flush=lowerCamelCase_       ,						end='''''' if not stream_end else None  )


			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   List[str]:





								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    lowerCamelCase    (		__A						):






			'''simple docstring'''





			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Union[str, Any] = False       ,						lowerCamelCase_			:   List[str] = None       ,						**lowerCamelCase_			:   str  )						->   Dict:
								super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Union[str, Any]    							=							Queue()
								__magic_name__							:	str    							=							None
								__magic_name__							:	Optional[int]    							=							timeout

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Tuple = False  )						->   Union[str, Any]:
								self.text_queue.put(lowerCamelCase_       ,						timeout=self.timeout  )
								if stream_end:
													self.text_queue.put(self.stop_signal       ,						timeout=self.timeout  )

			def __iter__( self			:   str  )						->   Optional[Any]:
								return self


			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Optional[int]:
								__magic_name__							:	str    							=							self.text_queue.get(timeout=self.timeout  )
								if value == self.stop_signal:
													raise StopIteration()
								else:
													return value









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



