
from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
						from ..models.auto import AutoTokenizer
class  lowercase__:



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					raise NotImplementedError()




	def 						_lowercase  (       self  :			Dict       )       ->     str:
					raise NotImplementedError()

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			"AutoTokenizer"       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     int:
					lowercase_       =					tokenizer
					lowercase_       =					skip_prompt
					lowercase_       =					decode_kwargs

					# variables used in the streaming process
					lowercase_       =					[]
					lowercase_       =					0
					lowercase_       =					True
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[Any]:
					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(SCREAMING_SNAKE_CASE_       ) > 0 and self._is_chinese_char(ord(text[-1]       )       ):
									lowercase_       =					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:
									lowercase_       =					text[self.print_len : text.rfind(''' '''       ) + 1]
									self.print_len += len(SCREAMING_SNAKE_CASE_       )

					self.on_finalized_text(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Tuple:
					# 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(SCREAMING_SNAKE_CASE_       ,							stream_end=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			bool = False       )       ->     int:
					print(SCREAMING_SNAKE_CASE_       ,							flush=SCREAMING_SNAKE_CASE_       ,							end='''''' if not stream_end else None       )




	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Optional[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 >= 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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			"AutoTokenizer"       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional[float] = None       ,							**SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Any:
					super().__init__(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					Queue()
					lowercase_       =					None
					lowercase_       =					timeout
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			bool = False       )       ->     Tuple:
					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  :			Tuple       )       ->     Union[str, Any]:
					return self




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









import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
