
'''simple docstring'''

from dataclasses import dataclass

_UpperCAmelCase   :      List[str] 						=  '''0.1'''
_UpperCAmelCase   :      Dict 						=  '''Lucia Harcekova'''

@dataclass
class 			UpperCAmelCase				:





		"""simple docstring"""





		A__  :   int
		A__  :   int
		A__  :   str
		def __repr__(    self     )				->    str:
				return F'''({self.offset}, {self.length}, {self.indicator})'''







class 			UpperCAmelCase				:





		"""simple docstring"""





		def __init__(    self  ,					_snake_case = 13  ,					_snake_case = 6     )				->    None:
				_UpperCamelCase       : str    =      window_size
				_UpperCamelCase       : Tuple    =      lookahead_buffer_size
				_UpperCamelCase       : Tuple    =      self.window_size - self.lookahead_buffer_size
		def _lowercase  (    self  ,					_snake_case     )				->    list[Token]:
				_UpperCamelCase       : Any    =      []
				_UpperCamelCase       : List[str]    =      ''''''

				# while there are still characters in text to compress
				while text:
						# find the next encoding phrase
						# - triplet with offset, length, indicator (the next encoding character)
						_UpperCamelCase       : Any    =      self._find_encoding_token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE     )

						# update the search buffer:
						# - add new characters from text into it
						# - check if size exceed the max search buffer size, if so, drop the
						#   oldest elements
						search_buffer += text[: token.length + 1]
						if len(__SCREAMING_SNAKE_CASE     ) > self.search_buffer_size:
								_UpperCamelCase       : Optional[int]    =      search_buffer[-self.search_buffer_size :]

						# update the text
						_UpperCamelCase       : Any    =      text[token.length + 1 :]

						# append the token to output
						output.append(__SCREAMING_SNAKE_CASE     )

				return output
		def _lowercase  (    self  ,					_snake_case     )				->    str:
				_UpperCamelCase       : Union[str, Any]    =      ''''''

				for token in tokens:
						for _ in range(token.length     ):
								output += output[-token.offset]
						output += token.indicator

				return output
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case     )				->    Token:

				if not text:
						raise ValueError('''We need some text to work with.'''     )

				# Initialise result parameters to default values
				_UpperCamelCase,							_UpperCamelCase       : Any    =      0, 0

				if not search_buffer:
						return Token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					text[length]     )

				for i, character in enumerate(__SCREAMING_SNAKE_CASE     ):
						_UpperCamelCase       : Dict    =      len(__SCREAMING_SNAKE_CASE     ) - i
						if character == text[0]:
								_UpperCamelCase       : List[Any]    =      self._match_length_from_index(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					0  ,					__SCREAMING_SNAKE_CASE     )
								# if the found length is bigger than the current or if it's equal,
								# which means it's offset is smaller: update offset and length
								if found_length >= length:
										_UpperCamelCase,							_UpperCamelCase       : str    =      found_offset, found_length

				return Token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					text[length]     )




		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    int:
				if not text or text[text_index] != window[window_index]:
						return 0
				return 1 + self._match_length_from_index(
				    __SCREAMING_SNAKE_CASE  ,					window + text[text_index]  ,					text_index + 1  ,					window_index + 1     )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	# Initialize compressor class
	_UpperCAmelCase   :      Optional[int] 						=  LZaaCompressor(window_size=13, lookahead_buffer_size=6)

	# Example
	_UpperCAmelCase   :      List[str] 						=  '''cabracadabrarrarrad'''
	_UpperCAmelCase   :      List[str] 						=  lzaa_compressor.compress(TEXT)
	print(lzaa_compressor.compress("""ababcbababaa"""))
	_UpperCAmelCase   :      List[Any] 						=  lzaa_compressor.decompress(compressed_text)
	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



'''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[Any] 						=  logging.get_logger(__name__)

_UpperCAmelCase   :      List[Any] 						=  {
    """nghuyong/ernie-1.0-base-zh""": """https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json""",
    """nghuyong/ernie-2.0-base-en""": """https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json""",
    """nghuyong/ernie-2.0-large-en""": """https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json""",
    """nghuyong/ernie-3.0-base-zh""": """https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-medium-zh""": """https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-mini-zh""": """https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-micro-zh""": """https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json""",
    """nghuyong/ernie-3.0-nano-zh""": """https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json""",
    """nghuyong/ernie-gram-zh""": """https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json""",
    """nghuyong/ernie-health-zh""": """https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json""",
}

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		A__  :   str          =    'ernie'
		def __init__(    self  ,					_snake_case=30522  ,					_snake_case=768  ,					_snake_case=12  ,					_snake_case=12  ,					_snake_case=3072  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=2  ,					_snake_case=3  ,					_snake_case=False  ,					_snake_case=0.02  ,					_snake_case=1E-12  ,					_snake_case=0  ,					_snake_case="absolute"  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[Any]:
				super().__init__(pad_token_id=_snake_case  ,					**_snake_case     )

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







class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		@property
		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
						_UpperCamelCase       : str    =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
						_UpperCamelCase       : Dict    =      {0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''token_type_ids''', dynamic_axis),
				        ('''task_type_ids''', dynamic_axis),
				    ]     )





