

'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
						from ..models.auto import AutoTokenizer



class 					lowercase						:


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





							def 						_snake_case							(			self)							->				Any:
								raise NotImplementedError()



class 					lowercase						(  a_ ):


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

								# variables used in the streaming process
								UpperCAmelCase_		:      Any       	=					[]
								UpperCAmelCase_		:      Optional[Any]       	=					0
								UpperCAmelCase_		:      List[Any]       	=					True


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

								if self.skip_prompt and self.next_tokens_are_prompt:
									UpperCAmelCase_		:      List[Any]       	=					False
									return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist())
								UpperCAmelCase_		:      Dict       	=					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_		:      List[Any]       	=					text[self.print_len :]
									UpperCAmelCase_		:      Optional[Any]       	=					[]
									UpperCAmelCase_		:      Dict       	=					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_		:      Dict       	=					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_		:      List[str]       	=					text[self.print_len : text.rfind(' ') + 1]
									self.print_len += len(_snake_case)

								self.on_finalized_text(_snake_case)


							def 						_snake_case							(			self)							->				Dict:
								# Flush the cache, if it exists
								if len(self.token_cache) > 0:
									UpperCAmelCase_		:      Any       	=					self.tokenizer.decode(self.token_cache    ,			**self.decode_kwargs)
									UpperCAmelCase_		:      Optional[Any]       	=					text[self.print_len :]
									UpperCAmelCase_		:      Tuple       	=					[]
									UpperCAmelCase_		:      Any       	=					0
								else:
									UpperCAmelCase_		:      Optional[int]       	=					''

								UpperCAmelCase_		:      str       	=					True
								self.on_finalized_text(_snake_case    ,			stream_end=_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False)							->				Tuple:
								print(_snake_case    ,			flush=_snake_case    ,			end='' if not stream_end else None)





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								# 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 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case    ,			_snake_case = False    ,			_snake_case = None    ,			**_snake_case)							->				Tuple:
								super().__init__(_snake_case    ,			_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					Queue()
								UpperCAmelCase_		:      List[str]       	=					None
								UpperCAmelCase_		:      Union[str, Any]       	=					timeout


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False)							->				Optional[Any]:
								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)							->				int:
								return self





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









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






