


"""simple docstring"""




from __future__ import annotations

lowerCamelCase						=		tuple[int, int, int]
lowerCamelCase						=		tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase						=		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
lowerCamelCase						=		"EGZWVONAHDCLFQMSIPJBYUKXTR"
lowerCamelCase						=		"FOBHMDKEXQNRAULPGSJVTYICZW"
lowerCamelCase						=		"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
lowerCamelCase						=		{
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
lowerCamelCase						=		"RMDJXFUWGISLHVTCQNKYPBEZOA"
lowerCamelCase						=		"SGLCPQWZHKXAREONTFBVIYJUDM"
lowerCamelCase						=		"HVSICLTYKQUBXDWAJZOMFGPREN"
lowerCamelCase						=		"RZWQHFMVDBKICJLNTUXAGYPSOE"
lowerCamelCase						=		"LFKIJODBEGAMQPXVUHYSTCZRWN"
lowerCamelCase						=		"KOAEGVDHXPQZMLFTYWJNBRCIUS"






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		if (unique_rotsel := len(set(lowerCamelCase__	)	)) < 3:
				lowerCAmelCase__       :      int   					= f'Please use 3 unique rotors (not {unique_rotsel})'
				raise Exception(lowerCamelCase__	)

		# Checks if rotor positions are valid
		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= rotpos
		if not 0 < rotorposa <= len(lowerCamelCase__	):
				lowerCAmelCase__       :      List[Any]   					= f'First rotor position is not within range of 1..26 ({rotorposa}'
				raise ValueError(lowerCamelCase__	)
		if not 0 < rotorposa <= len(lowerCamelCase__	):
				lowerCAmelCase__       :      Optional[int]   					= f'Second rotor position is not within range of 1..26 ({rotorposa})'
				raise ValueError(lowerCamelCase__	)
		if not 0 < rotorposa <= len(lowerCamelCase__	):
				lowerCAmelCase__       :      Tuple   					= f'Third rotor position is not within range of 1..26 ({rotorposa})'
				raise ValueError(lowerCamelCase__	)

		# Validates string and returns dict
		lowerCAmelCase__       :      List[Any]   					= _plugboard(lowerCamelCase__	)

		return rotpos, rotsel, pbdict






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		if not isinstance(lowerCamelCase__     ,       lowerCamelCase__	):
				lowerCAmelCase__       :      int   					= f'Plugboard setting isn\'t type string ({type(lowerCamelCase__	)})'
				raise TypeError(lowerCamelCase__	)
		elif len(lowerCamelCase__	) % 2 != 0:
				lowerCAmelCase__       :      Tuple   					= f'Odd number of symbols ({len(lowerCamelCase__	)})'
				raise Exception(lowerCamelCase__	)
		elif pbstring == "":
				return {}

		pbstring.replace(''' '''     ,       ''''''	)

		# Checks if all characters are unique
		lowerCAmelCase__       :      str   					= set()
		for i in pbstring:
				if i not in abc:
						lowerCAmelCase__       :      Any   					= f'\'{i}\' not in list of symbols'
						raise Exception(lowerCamelCase__	)
				elif i in tmppbl:
						lowerCAmelCase__       :      List[str]   					= f'Duplicate symbol ({i})'
						raise Exception(lowerCamelCase__	)
				else:
						tmppbl.add(lowerCamelCase__	)
		del tmppbl

		# Created the dictionary
		lowerCAmelCase__       :      List[str]   					= {}
		for j in range(0     ,       len(lowerCamelCase__	) - 1     ,       2	):
				lowerCAmelCase__       :      Optional[int]   					= pbstring[j + 1]
				lowerCAmelCase__       :      Any   					= pbstring[j]

		return pb






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a = (rotora, rotora, rotora)     ,       _a = ""     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= text.upper()
		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Tuple   					= _validator(
		    lowerCamelCase__     ,       lowerCamelCase__     ,       plugb.upper()	)

		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= rotor_position
		lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= rotor_selection
		rotorposa -= 1
		rotorposa -= 1
		rotorposa -= 1

		lowerCAmelCase__       :      Any   					= []

		# encryption/decryption process --------------------------
		for symbol in text:
				if symbol in abc:
						# 1st plugboard --------------------------
						if symbol in plugboard:
								lowerCAmelCase__       :      List[str]   					= plugboard[symbol]

						# rotor ra --------------------------
						lowerCAmelCase__       :      str   					= abc.index(lowerCamelCase__	) + rotorposa
						lowerCAmelCase__       :      Optional[Any]   					= rotora[index % len(lowerCamelCase__	)]

						# rotor rb --------------------------
						lowerCAmelCase__       :      int   					= abc.index(lowerCamelCase__	) + rotorposa
						lowerCAmelCase__       :      Optional[int]   					= rotora[index % len(lowerCamelCase__	)]

						# rotor rc --------------------------
						lowerCAmelCase__       :      int   					= abc.index(lowerCamelCase__	) + rotorposa
						lowerCAmelCase__       :      Optional[Any]   					= rotora[index % len(lowerCamelCase__	)]

						# reflector --------------------------
						# this is the reason you don't need another machine to decipher

						lowerCAmelCase__       :      List[Any]   					= reflector[symbol]

						# 2nd rotors
						lowerCAmelCase__       :      str   					= abc[rotora.index(lowerCamelCase__	) - rotorposa]
						lowerCAmelCase__       :      Optional[int]   					= abc[rotora.index(lowerCamelCase__	) - rotorposa]
						lowerCAmelCase__       :      List[str]   					= abc[rotora.index(lowerCamelCase__	) - rotorposa]

						# 2nd plugboard
						if symbol in plugboard:
								lowerCAmelCase__       :      Optional[Any]   					= plugboard[symbol]

						# moves/resets rotor positions
						rotorposa += 1
						if rotorposa >= len(lowerCamelCase__	):
								lowerCAmelCase__       :      Any   					= 0
								rotorposa += 1
						if rotorposa >= len(lowerCamelCase__	):
								lowerCAmelCase__       :      Optional[int]   					= 0
								rotorposa += 1
						if rotorposa >= len(lowerCamelCase__	):
								lowerCAmelCase__       :      Any   					= 0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
				result.append(lowerCamelCase__	)

		return "".join(lowerCamelCase__	)


if __name__ == "__main__":
		lowerCamelCase						=		"This is my Python script that emulates the Enigma machine from WWII."
		lowerCamelCase						=		(1, 1, 1)
		lowerCamelCase						=		"pictures"
		lowerCamelCase						=		(rotora, rotora, rotora)
		lowerCamelCase						=		enigma(message, rotor_pos, rotor_sel, pb)

		print('''Encrypted message:''', en)
		print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))








import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
		raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
		raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		'''The Nymphenburg Palace is a beautiful palace in Munich!'''






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {
		    '''attention_cell''': '''multi_head''',
		    '''num_layers''': 4,
		    '''units''': 1_024,
		    '''hidden_size''': 768,
		    '''max_length''': 512,
		    '''num_heads''': 8,
		    '''scaled''': True,
		    '''dropout''': 0.1,
		    '''use_residual''': True,
		    '''embed_size''': 1_024,
		    '''embed_dropout''': 0.1,
		    '''word_embed''': None,
		    '''layer_norm_eps''': 1e-5,
		    '''token_type_vocab_size''': 2,
		}

		lowerCAmelCase__       :      int   					= bort_4_8_768_1024_hparams

		# Let's construct the original Bort model here
		# Taken from official BERT implementation, see:
		# https://github.com/alexa/bort/blob/master/bort/bort.py
		lowerCAmelCase__       :      List[Any]   					= BERTEncoder(
		    attention_cell=predefined_args['''attention_cell''']     ,       num_layers=predefined_args['''num_layers''']     ,       units=predefined_args['''units''']     ,       hidden_size=predefined_args['''hidden_size''']     ,       max_length=predefined_args['''max_length''']     ,       num_heads=predefined_args['''num_heads''']     ,       scaled=predefined_args['''scaled''']     ,       dropout=predefined_args['''dropout''']     ,       output_attention=_a     ,       output_all_encodings=_a     ,       use_residual=predefined_args['''use_residual''']     ,       activation=predefined_args.get('''activation'''     ,       '''gelu'''	)     ,       layer_norm_eps=predefined_args.get('''layer_norm_eps'''     ,       _a	)     ,       )

		# Vocab information needs to be fetched first
		# It's the same as RoBERTa, so RobertaTokenizer can be used later
		lowerCAmelCase__       :      Union[str, Any]   					= '''openwebtext_ccnews_stories_books_cased'''

		# Specify download folder to Gluonnlp's vocab
		lowerCAmelCase__       :      Optional[Any]   					= os.path.join(get_home_dir()     ,       '''models'''	)
		lowerCAmelCase__       :      Optional[int]   					= _load_vocab(_a     ,       _a     ,       _a     ,       cls=_a	)

		lowerCAmelCase__       :      Any   					= nlp.model.BERTModel(
		    _a     ,       len(_a	)     ,       units=predefined_args['''units''']     ,       embed_size=predefined_args['''embed_size''']     ,       embed_dropout=predefined_args['''embed_dropout''']     ,       word_embed=predefined_args['''word_embed''']     ,       use_pooler=_a     ,       use_token_type_embed=_a     ,       token_type_vocab_size=predefined_args['''token_type_vocab_size''']     ,       use_classifier=_a     ,       use_decoder=_a     ,       )

		original_bort.load_parameters(_a     ,       cast_dtype=_a     ,       ignore_extra=_a	)
		lowerCAmelCase__       :      Tuple   					= original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase__       :      int   					= {
		    '''architectures''': ['''BertForMaskedLM'''],
		    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_act''': '''gelu''',
		    '''hidden_dropout_prob''': predefined_args['''dropout'''],
		    '''hidden_size''': predefined_args['''embed_size'''],
		    '''initializer_range''': 0.02,
		    '''intermediate_size''': predefined_args['''hidden_size'''],
		    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
		    '''max_position_embeddings''': predefined_args['''max_length'''],
		    '''model_type''': '''bort''',
		    '''num_attention_heads''': predefined_args['''num_heads'''],
		    '''num_hidden_layers''': predefined_args['''num_layers'''],
		    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
		    '''vocab_size''': len(_a	),
		}

		lowerCAmelCase__       :      str   					= BertConfig.from_dict(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= BertForMaskedLM(_a	)
		hf_bort_model.eval()

		# Parameter mapping table (Gluonnlp to Transformers)
		# * denotes layer index
		#
		# | Gluon Parameter                                                | Transformers Parameter
		# | -------------------------------------------------------------- | ----------------------
		# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
		# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
		# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
		# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
		# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
		# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
		# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
		# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
		# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
		# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
		# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

		# Helper function to convert MXNET Arrays to PyTorch
		def to_torch(_a	) -> nn.Parameter:
				return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

		# Check param shapes and map new HF param back
		def check_and_map_params(_a     ,       _a	):
				lowerCAmelCase__       :      Dict   					= hf_param.shape

				lowerCAmelCase__       :      List[str]   					= to_torch(params[gluon_param]	)
				lowerCAmelCase__       :      Any   					= gluon_param.shape

				assert (
				    shape_hf == shape_gluon
				), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

				return gluon_param

		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.word_embeddings.weight     ,       '''word_embed.0.weight'''	)
		lowerCAmelCase__       :      List[Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.position_embeddings.weight     ,       '''encoder.position_weight'''	)
		lowerCAmelCase__       :      Any   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.bias     ,       '''encoder.layer_norm.beta'''	)
		lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
		    hf_bort_model.bert.embeddings.LayerNorm.weight     ,       '''encoder.layer_norm.gamma'''	)

		# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
		lowerCAmelCase__       :      Union[str, Any]   					= torch.zeros_like(
		    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

		for i in range(hf_bort_config.num_hidden_layers	):
				lowerCAmelCase__       :      BertLayer   					= hf_bort_model.bert.encoder.layer[i]

				# self attention
				lowerCAmelCase__       :      BertSelfAttention   					= layer.attention.self

				lowerCAmelCase__       :      Optional[Any]   					= check_and_map_params(
				    self_attn.key.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'	)

				lowerCAmelCase__       :      str   					= check_and_map_params(
				    self_attn.key.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    self_attn.query.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'	)
				lowerCAmelCase__       :      Optional[Any]   					= check_and_map_params(
				    self_attn.query.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'	)
				lowerCAmelCase__       :      Any   					= check_and_map_params(
				    self_attn.value.bias.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'	)
				lowerCAmelCase__       :      Any   					= check_and_map_params(
				    self_attn.value.weight.data     ,       f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'	)

				# self attention output
				lowerCAmelCase__       :      BertSelfOutput   					= layer.attention.output

				lowerCAmelCase__       :      Dict   					= check_and_map_params(
				    self_output.dense.bias     ,       f'encoder.transformer_cells.{i}.proj.bias'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    self_output.dense.weight     ,       f'encoder.transformer_cells.{i}.proj.weight'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    self_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= check_and_map_params(
				    self_output.LayerNorm.weight     ,       f'encoder.transformer_cells.{i}.layer_norm.gamma'	)

				# intermediate
				lowerCAmelCase__       :      BertIntermediate   					= layer.intermediate

				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'	)
				lowerCAmelCase__       :      Union[str, Any]   					= check_and_map_params(
				    intermediate.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'	)

				# output
				lowerCAmelCase__       :      BertOutput   					= layer.output

				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.dense.bias     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'	)
				lowerCAmelCase__       :      int   					= check_and_map_params(
				    bert_output.dense.weight     ,       f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'	)
				lowerCAmelCase__       :      Optional[int]   					= check_and_map_params(
				    bert_output.LayerNorm.bias     ,       f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'	)
				lowerCAmelCase__       :      List[str]   					= check_and_map_params(
				    bert_output.LayerNorm.weight     ,       f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'	)

		# Save space and energy 🎄
		hf_bort_model.half()

		# Compare output of both models
		lowerCAmelCase__       :      Dict   					= RobertaTokenizer.from_pretrained('''roberta-base'''	)

		lowerCAmelCase__       :      List[str]   					= tokenizer.encode_plus(_a	)['''input_ids''']

		# Get gluon output
		lowerCAmelCase__       :      str   					= mx.nd.array([input_ids]	)
		lowerCAmelCase__       :      List[str]   					= original_bort(inputs=_a     ,       token_types=[]	)

		# Get Transformer output (save and reload model again)
		hf_bort_model.save_pretrained(_a	)
		lowerCAmelCase__       :      Optional[int]   					= BertModel.from_pretrained(_a	)
		hf_bort_model.eval()

		lowerCAmelCase__       :      Tuple   					= tokenizer.encode_plus(_a     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      Optional[Any]   					= hf_bort_model(**_a	)[0]

		lowerCAmelCase__       :      str   					= output_gluon[0].asnumpy()
		lowerCAmelCase__       :      Optional[Any]   					= output_hf[0].detach().numpy()

		lowerCAmelCase__       :      str   					= np.max(np.abs(hf_layer - gluon_layer	)	).item()
		lowerCAmelCase__       :      int   					= np.allclose(_a     ,       _a     ,       atol=1e-3	)

		if success:
				print('''✔️ Both model do output the same tensors'''	)
		else:
				print('''❌ Both model do **NOT** output the same tensors'''	)
				print('''Absolute difference is:'''     ,       _a	)


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCamelCase						=		parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




