


"""simple docstring"""




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							, _a      ):
				raise NotImplementedError()





		def 			__UpperCAmelCase       (  self      ):
				raise NotImplementedError()







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a = False							, **_a      ):
				__a           =			tokenizer
				__a           =			skip_prompt
				__a           =			decode_kwargs

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

		def 			__UpperCAmelCase       (  self							, _a      ):
				if len(value.shape      ) > 1 and value.shape[0] > 1:
						raise ValueError('''TextStreamer only supports batch size 1'''      )
				elif len(value.shape      ) > 1:
						__a           =			value[0]

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

				# Add the new token to the cache and decodes the entire thing.
				self.token_cache.extend(value.tolist()      )
				__a           =			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           =			text[self.print_len :]
						__a           =			[]
						__a           =			0
				# If the last token is a CJK character, we print the characters.
				elif len(SCREAMING_SNAKE_CASE_      ) > 0 and self._is_chinese_char(ord(text[-1]      )      ):
						__a           =			text[self.print_len :]
						self.print_len += len(SCREAMING_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:
						__a           =			text[self.print_len : text.rfind(''' '''      ) + 1]
						self.print_len += len(SCREAMING_SNAKE_CASE_      )

				self.on_finalized_text(SCREAMING_SNAKE_CASE_      )

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

				__a           =			True
				self.on_finalized_text(SCREAMING_SNAKE_CASE_							, stream_end=SCREAMING_SNAKE_CASE_      )

		def 			__UpperCAmelCase       (  self							, _a							, _a = False      ):
				print(SCREAMING_SNAKE_CASE_							, flush=SCREAMING_SNAKE_CASE_							, end='''''' if not stream_end else None      )





		def 			__UpperCAmelCase       (  self							, _a      ):
				# 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 >= 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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a = False							, _a = None							, **_a      ):
				super().__init__(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_      )
				__a           =			Queue()
				__a           =			None
				__a           =			timeout

		def 			__UpperCAmelCase       (  self							, _a							, _a = False      ):
				self.text_queue.put(SCREAMING_SNAKE_CASE_							, timeout=self.timeout      )
				if stream_end:
						self.text_queue.put(self.stop_signal							, timeout=self.timeout      )

		def __iter__(  self      ):
				return self





		def 			__UpperCAmelCase       (  self      ):
				__a           =			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,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




