



'''simple docstring'''
import numpy as np

_UpperCamelCase											=			[
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     np.array(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     np.ndarray:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     np.where(letter == self.SQUARE				)
					__UpperCAmelCase   :      List[str]										=     np.concatenate([indexa + 1, indexa + 1]				)
					return indexes





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					return self.SQUARE[indexa - 1, indexa - 1]





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     message.lower()
					__UpperCAmelCase   :      int										=     message.replace("""j""" ,       """i"""				)

					__UpperCAmelCase   :      List[Any]										=     """"""
					for letter_index in range(len(__UpperCAmelCase				)				):
								if message[letter_index] != " ":
											__UpperCAmelCase   :      Dict										=     self.letter_to_numbers(message[letter_index]				)
											__UpperCAmelCase   :      Any										=     encoded_message + str(numbers[0]				) + str(numbers[1]				)
								elif message[letter_index] == " ":
											__UpperCAmelCase   :      Optional[int]										=     encoded_message + """ """

					return encoded_message





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     message.replace(""" """ ,       """  """				)
					__UpperCAmelCase   :      int										=     """"""
					for numbers_index in range(int(len(__UpperCAmelCase				) / 2				)				):
								if message[numbers_index * 2] != " ":
											__UpperCAmelCase   :      str										=     message[numbers_index * 2]
											__UpperCAmelCase   :      int										=     message[numbers_index * 2 + 1]

											__UpperCAmelCase   :      Tuple										=     self.numbers_to_letter(int(__UpperCAmelCase				) ,       int(__UpperCAmelCase				)				)
											__UpperCAmelCase   :      str										=     decoded_message + letter
								elif message[numbers_index * 2] == " ":
											__UpperCAmelCase   :      List[str]										=     decoded_message + """ """

					return decoded_message









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

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



class _A  (    __SCREAMING_SNAKE_CASE    ):





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

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




