

from collections import OrderedDict
from typing import Mapping

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


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

lowercase     :       List[Any]        =       {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Dict											=							'electra'

						def __init__(				self , __UpperCamelCase=3_05_22 , __UpperCamelCase=1_28 , __UpperCamelCase=2_56 , __UpperCamelCase=12 , __UpperCamelCase=4 , __UpperCamelCase=10_24 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=5_12 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , __UpperCamelCase="first" , __UpperCamelCase=True , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0 , __UpperCamelCase="absolute" , __UpperCamelCase=True , __UpperCamelCase=None , **__UpperCamelCase , )							->	Optional[int]:




									'''simple docstring'''


									super().__init__(pad_token_id=__UpperCamelCase , **__UpperCamelCase    )

									__UpperCamelCase    :    Tuple								=      vocab_size
									__UpperCamelCase    :    Union[str, Any]								=      embedding_size
									__UpperCamelCase    :    List[Any]								=      hidden_size
									__UpperCamelCase    :    Optional[int]								=      num_hidden_layers
									__UpperCamelCase    :    Tuple								=      num_attention_heads
									__UpperCamelCase    :    List[Any]								=      intermediate_size
									__UpperCamelCase    :    List[Any]								=      hidden_act
									__UpperCamelCase    :    Dict								=      hidden_dropout_prob
									__UpperCamelCase    :    Union[str, Any]								=      attention_probs_dropout_prob
									__UpperCamelCase    :    Optional[Any]								=      max_position_embeddings
									__UpperCamelCase    :    Any								=      type_vocab_size
									__UpperCamelCase    :    str								=      initializer_range
									__UpperCamelCase    :    Optional[int]								=      layer_norm_eps

									__UpperCamelCase    :    Any								=      summary_type
									__UpperCamelCase    :    List[Any]								=      summary_use_proj
									__UpperCamelCase    :    Optional[Any]								=      summary_activation
									__UpperCamelCase    :    Optional[int]								=      summary_last_dropout
									__UpperCamelCase    :    Any								=      position_embedding_type
									__UpperCamelCase    :    Union[str, Any]								=      use_cache
									__UpperCamelCase    :    int								=      classifier_dropout







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




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




									'''simple docstring'''


									if self.task == "multiple-choice":
												__UpperCamelCase    :    int								=      {0: "batch", 1: "choice", 2: "sequence"}
									else:
												__UpperCamelCase    :    int								=      {0: "batch", 1: "sequence"}
									return OrderedDict(
									    [
									        ("input_ids", dynamic_axis),
									        ("attention_mask", dynamic_axis),
									        ("token_type_ids", dynamic_axis),
									    ]    )


import numpy as np

lowercase     :       Union[str, Any]        =       [
    ["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 		SCREAMING_SNAKE_CASE__					:







						"""simple docstring"""




						def __init__(				self    )							->	None:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      np.array(__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      np.where(letter == self.SQUARE    )
									__UpperCamelCase    :    Tuple								=      np.concatenate([indexa + 1, indexa + 1]    )
									return indexes

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	str:




									'''simple docstring'''


									return self.SQUARE[indexa - 1, indexa - 1]

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    int								=      message.lower()
									__UpperCamelCase    :    List[Any]								=      message.replace("j" , "i"    )

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

									return encoded_message




						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      message.replace(" " , "  "    )
									__UpperCamelCase    :    Optional[int]								=      ""
									for numbers_index in range(int(len(__UpperCamelCase    ) / 2    )    ):
												if message[numbers_index * 2] != " ":
															__UpperCamelCase    :    int								=      message[numbers_index * 2]
															__UpperCamelCase    :    Optional[int]								=      message[numbers_index * 2 + 1]

															__UpperCamelCase    :    str								=      self.numbers_to_letter(int(__UpperCamelCase    ) , int(__UpperCamelCase    )    )
															__UpperCamelCase    :    Optional[Any]								=      decoded_message + letter
												elif message[numbers_index * 2] == " ":
															__UpperCamelCase    :    str								=      decoded_message + " "

									return decoded_message