from collections import OrderedDict
from typing import Mapping

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


A_:      Optional[Any]        =			logging.get_logger(__name__)

A_:      Optional[int]        =			{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							lowerCAmelCase__       =					MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__       =					'mobilebert'



							def __init__(    self     ,   UpperCAmelCase=30522     ,   UpperCAmelCase=512     ,   UpperCAmelCase=24     ,   UpperCAmelCase=4     ,   UpperCAmelCase=512     ,   UpperCAmelCase="relu"     ,   UpperCAmelCase=0.0     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=512     ,   UpperCAmelCase=2     ,   UpperCAmelCase=0.02     ,   UpperCAmelCase=1e-12     ,   UpperCAmelCase=0     ,   UpperCAmelCase=128     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase=128     ,   UpperCAmelCase=False     ,   UpperCAmelCase=True     ,   UpperCAmelCase=4     ,   UpperCAmelCase="no_norm"     ,   UpperCAmelCase=True     ,   UpperCAmelCase=None     ,   **UpperCAmelCase     ,   ):







														'''simple docstring'''






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

														_lowercase				=	vocab_size
														_lowercase				=	hidden_size
														_lowercase				=	num_hidden_layers
														_lowercase				=	num_attention_heads
														_lowercase				=	hidden_act
														_lowercase				=	intermediate_size
														_lowercase				=	hidden_dropout_prob
														_lowercase				=	attention_probs_dropout_prob
														_lowercase				=	max_position_embeddings
														_lowercase				=	type_vocab_size
														_lowercase				=	initializer_range
														_lowercase				=	layer_norm_eps
														_lowercase				=	embedding_size
														_lowercase				=	trigram_input
														_lowercase				=	use_bottleneck
														_lowercase				=	intra_bottleneck_size
														_lowercase				=	use_bottleneck_attention
														_lowercase				=	key_query_shared_bottleneck
														_lowercase				=	num_feedforward_networks
														_lowercase				=	normalization_type
														_lowercase				=	classifier_activation

														if self.use_bottleneck:
																					_lowercase				=	intra_bottleneck_size
														else:
																					_lowercase				=	hidden_size

														_lowercase				=	classifier_dropout






class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							@property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






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



import numpy as np


def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):



							"""simple docstring"""


							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)


def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):



							"""simple docstring"""


							_lowercase				=	int(_A ,2					)
							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
														raise ValueError("""size of parity don't match with size of data"""					)

							_lowercase				=	[]
							_lowercase				=	[]
							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]

							# sorted information data for the size of the output data
							_lowercase				=	[]
							# data position template + parity
							_lowercase				=	[]
							# parity bit counter
							_lowercase				=	0
							# counter position of data bits
							_lowercase				=	0

							for x in range(1 ,size_par + len(_A					) + 1					):
														# Performs a template of bit positions - who should be given,
														# and who should be parity
														if qtd_bp < size_par:
																					if (np.log(_A					) / np.log(2					)).is_integer():
																												data_out_gab.append("""P"""					)
																												_lowercase				=	qtd_bp + 1
																					else:
																												data_out_gab.append("""D"""					)
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_ord.append(data[cont_data]					)
																					cont_data += 1
														else:
																					data_ord.append(_A					)

    # Calculates parity
							_lowercase				=	0  # parity bit counter
							for bp in range(1 ,size_par + 1					):
														# Bit counter one for a given parity
														_lowercase				=	0
														# counter to control the loop reading
														_lowercase				=	0
														for x in data_ord:
																					if x is not None:
																												try:
																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
																												except IndexError:
																																			_lowercase				=	"""0"""
																												if aux == "1" and x == "1":
																																			cont_bo += 1
																					cont_loop += 1
														parity.append(cont_bo % 2					)

														qtd_bp += 1

							# Mount the message
							_lowercase				=	0  # parity bit counter
							for x in range(0 ,size_par + len(_A					)					):
														if data_ord[x] is None:
																					data_out.append(str(parity[cont_bp]					)					)
																					cont_bp += 1
														else:
																					data_out.append(data_ord[x]					)

							return data_out


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							_lowercase				=	[]
							# Parity bit counter
							_lowercase				=	0
							# Counter p data bit reading
							_lowercase				=	0
							# list of parity received
							_lowercase				=	[]
							_lowercase				=	[]

							for x in range(1 ,len(_A					) + 1					):
														# Performs a template of bit positions - who should be given,
														#  and who should be parity
														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
																					data_out_gab.append("""P"""					)
																					_lowercase				=	qtd_bp + 1
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_output.append(data[cont_data]					)
														else:
																					parity_received.append(data[cont_data]					)
														cont_data += 1

							# -----------calculates the parity with the data
							_lowercase				=	[]
							_lowercase				=	[]
							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]

							#  sorted information data for the size of the output data
							_lowercase				=	[]
							# Data position feedback + parity
							_lowercase				=	[]
							# Parity bit counter
							_lowercase				=	0
							# Counter p data bit reading
							_lowercase				=	0

							for x in range(1 ,size_par + len(_A					) + 1					):
														# Performs a template position of bits - who should be given,
														# and who should be parity
														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
																					data_out_gab.append("""P"""					)
																					_lowercase				=	qtd_bp + 1
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_ord.append(data_output[cont_data]					)
																					cont_data += 1
														else:
																					data_ord.append(_A					)

    # Calculates parity
							_lowercase				=	0  # parity bit counter
							for bp in range(1 ,size_par + 1					):
														# Bit counter one for a certain parity
														_lowercase				=	0
														# Counter to control loop reading
														_lowercase				=	0
														for x in data_ord:
																					if x is not None:
																												try:
																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
																												except IndexError:
																																			_lowercase				=	"""0"""
																												if aux == "1" and x == "1":
																																			cont_bo += 1
																					cont_loop += 1
														parity.append(str(cont_bo % 2					)					)

														qtd_bp += 1

							# Mount the message
							_lowercase				=	0  # Parity bit counter
							for x in range(0 ,size_par + len(_A					)					):
														if data_ord[x] is None:
																					data_out.append(str(parity[cont_bp]					)					)
																					cont_bp += 1
														else:
																					data_out.append(data_ord[x]					)

							_lowercase				=	parity_received == parity
							return data_output, ack


