
from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
					from ..models.auto import AutoTokenizer

class    a		:







							def    UpperCAmelCase__       (    self	:	Union[str, Any]   ,					snake_case__	:	List[str]			):
								"""simple docstring"""







								raise NotImplementedError()







							def    UpperCAmelCase__       (    self	:	Any			):
								"""simple docstring"""







								raise NotImplementedError()

class    a		( __UpperCAmelCase       ):







							def __init__(    self	:	List[str]   ,					snake_case__	:	Optional[Any]   ,					snake_case__	:	int = False   ,					**snake_case__	:	int			):
								"""simple docstring"""







								__lowerCAmelCase    =				tokenizer
								__lowerCAmelCase    =				skip_prompt
								__lowerCAmelCase    =				decode_kwargs

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







							def    UpperCAmelCase__       (    self	:	List[Any]   ,					snake_case__	:	Optional[Any]			):
								"""simple docstring"""







								if len(value.shape			) > 1 and value.shape[0] > 1:
									raise ValueError("TextStreamer only supports batch size 1"			)
								elif len(value.shape			) > 1:
									__lowerCAmelCase    =				value[0]

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

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

								# After the symbol for a new line, we flush the cache.
								if text.endswith("\n"			):
									__lowerCAmelCase    =				text[self.print_len :]
									__lowerCAmelCase    =				[]
									__lowerCAmelCase    =				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]			)			):
									__lowerCAmelCase    =				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:
									__lowerCAmelCase    =				text[self.print_len : text.rfind(" "			) + 1]
									self.print_len += len(lowerCamelCase_			)

								self.on_finalized_text(lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	Dict			):
								"""simple docstring"""







								if len(self.token_cache			) > 0:
									__lowerCAmelCase    =				self.tokenizer.decode(self.token_cache   ,					**self.decode_kwargs			)
									__lowerCAmelCase    =				text[self.print_len :]
									__lowerCAmelCase    =				[]
									__lowerCAmelCase    =				0
								else:
									__lowerCAmelCase    =				''''''

								__lowerCAmelCase    =				True
								self.on_finalized_text(lowerCamelCase_   ,					stream_end=lowerCamelCase_			)







							def    UpperCAmelCase__       (    self	:	Dict   ,					snake_case__	:	Tuple   ,					snake_case__	:	Tuple = False			):
								"""simple docstring"""







								print(lowerCamelCase_   ,					flush=lowerCamelCase_   ,					end="" if not stream_end else None			)







							def    UpperCAmelCase__       (    self	:	List[str]   ,					snake_case__	:	Optional[int]			):
								"""simple docstring"""







								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    a		( __UpperCAmelCase       ):







							def __init__(    self	:	Tuple   ,					snake_case__	:	int   ,					snake_case__	:	str = False   ,					snake_case__	:	Optional[Any] = None   ,					**snake_case__	:	Optional[int]			):
								"""simple docstring"""







								super().__init__(lowerCamelCase_   ,					lowerCamelCase_   ,					**lowerCamelCase_			)
								__lowerCAmelCase    =				Queue()
								__lowerCAmelCase    =				None
								__lowerCAmelCase    =				timeout







							def    UpperCAmelCase__       (    self	:	List[Any]   ,					snake_case__	:	List[Any]   ,					snake_case__	:	Any = False			):
								"""simple docstring"""







								self.text_queue.put(lowerCamelCase_   ,					timeout=self.timeout			)
								if stream_end:
									self.text_queue.put(self.stop_signal   ,					timeout=self.timeout			)







							def __iter__(    self	:	Union[str, Any]			):
								"""simple docstring"""







								return self







							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  :						List[str]					=      logging.get_logger(__name__)

UpperCamelCase__  :						str					=      {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Dict   				= '''camembert'''




      def __init__(					self       ,lowerCamelCase_=30522       ,lowerCamelCase_=768       ,lowerCamelCase_=12       ,lowerCamelCase_=12       ,lowerCamelCase_=3072       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=1e-12       ,lowerCamelCase_=1       ,lowerCamelCase_=0       ,lowerCamelCase_=2       ,lowerCamelCase_="absolute"       ,lowerCamelCase_=True       ,lowerCamelCase_=None       ,**lowerCamelCase_       ,)      ->      str:



         '''simple docstring'''



         super().__init__(pad_token_id=lowerCamelCase_       ,bos_token_id=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)

         UpperCAmelCase__				:					int 					=	vocab_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Tuple 					=	num_hidden_layers
         UpperCAmelCase__				:					List[str] 					=	num_attention_heads
         UpperCAmelCase__				:					int 					=	hidden_act
         UpperCAmelCase__				:					Tuple 					=	intermediate_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					str 					=	max_position_embeddings
         UpperCAmelCase__				:					Union[str, Any] 					=	type_vocab_size
         UpperCAmelCase__				:					int 					=	initializer_range
         UpperCAmelCase__				:					Optional[Any] 					=	layer_norm_eps
         UpperCAmelCase__				:					Optional[Any] 					=	position_embedding_type
         UpperCAmelCase__				:					str 					=	use_cache
         UpperCAmelCase__				:					List[Any] 					=	classifier_dropout




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      @property
      def 		lowerCAmelCase__       (					self				)      ->      Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         if self.task == "multiple-choice":
            UpperCAmelCase__				:					Tuple 					=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            UpperCAmelCase__				:					Union[str, Any] 					=	{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]				)




