

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()
a__												=						logging.get_logger(__name__)

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



def   __UpperCAmelCase					(      __a :     str						,__a :     str      )     ->	int:

					"""simple docstring"""

					_a      :       Dict   =						{
					    '''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,
					}

					_a      :       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
					_a      :       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=lowercase__						,output_all_encodings=lowercase__						,use_residual=predefined_args['''use_residual''']						,activation=predefined_args.get('''activation'''						,'''gelu'''      )						,layer_norm_eps=predefined_args.get('''layer_norm_eps'''						,lowercase__      )						,)

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

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

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

					original_bort.load_parameters(lowercase__						,cast_dtype=lowercase__						,ignore_extra=lowercase__      )
					_a      :       int   =						original_bort._collect_params_with_prefix()

					# Build our config 🤗
					_a      :       Dict   =						{
					    '''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(lowercase__      ),
					}

					_a      :       Dict   =						BertConfig.from_dict(lowercase__      )
					_a      :       Optional[Any]   =						BertForMaskedLM(lowercase__      )
					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 :     Optional[Any]      ) -> 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 :     Optional[Any]						,__a :     Any      ):
										_a      :       Tuple   =						hf_param.shape

										_a      :       str   =						to_torch(params[gluon_param]      )
										_a      :       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

					_a      :       List[str]   =						check_and_map_params(
					    hf_bort_model.bert.embeddings.word_embeddings.weight						,'''word_embed.0.weight'''      )
					_a      :       Optional[int]   =						check_and_map_params(
					    hf_bort_model.bert.embeddings.position_embeddings.weight						,'''encoder.position_weight'''      )
					_a      :       str   =						check_and_map_params(
					    hf_bort_model.bert.embeddings.LayerNorm.bias						,'''encoder.layer_norm.beta'''      )
					_a      :       Optional[int]   =						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)
					_a      :       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      ):
										_a      :       int   =						hf_bort_model.bert.encoder.layer[i]

										# self attention
										_a      :       Union[str, Any]   =						layer.attention.self

										_a      :       Optional[int]   =						check_and_map_params(
										    self_attn.key.bias.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""      )

										_a      :       Tuple   =						check_and_map_params(
										    self_attn.key.weight.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""      )
										_a      :       str   =						check_and_map_params(
										    self_attn.query.bias.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""      )
										_a      :       str   =						check_and_map_params(
										    self_attn.query.weight.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""      )
										_a      :       int   =						check_and_map_params(
										    self_attn.value.bias.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""      )
										_a      :       List[Any]   =						check_and_map_params(
										    self_attn.value.weight.data						,F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""      )

										# self attention output
										_a      :       Union[str, Any]   =						layer.attention.output

										_a      :       Union[str, Any]   =						check_and_map_params(
										    self_output.dense.bias						,F"""encoder.transformer_cells.{i}.proj.bias"""      )
										_a      :       Optional[int]   =						check_and_map_params(
										    self_output.dense.weight						,F"""encoder.transformer_cells.{i}.proj.weight"""      )
										_a      :       Optional[int]   =						check_and_map_params(
										    self_output.LayerNorm.bias						,F"""encoder.transformer_cells.{i}.layer_norm.beta"""      )
										_a      :       Any   =						check_and_map_params(
										    self_output.LayerNorm.weight						,F"""encoder.transformer_cells.{i}.layer_norm.gamma"""      )

										# intermediate
										_a      :       Any   =						layer.intermediate

										_a      :       Tuple   =						check_and_map_params(
										    intermediate.dense.bias						,F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""      )
										_a      :       List[Any]   =						check_and_map_params(
										    intermediate.dense.weight						,F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""      )

										# output
										_a      :       str   =						layer.output

										_a      :       Dict   =						check_and_map_params(
										    bert_output.dense.bias						,F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""      )
										_a      :       Union[str, Any]   =						check_and_map_params(
										    bert_output.dense.weight						,F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""      )
										_a      :       str   =						check_and_map_params(
										    bert_output.LayerNorm.bias						,F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""      )
										_a      :       int   =						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
					_a      :       int   =						RobertaTokenizer.from_pretrained('''roberta-base'''      )

					_a      :       List[Any]   =						tokenizer.encode_plus(lowercase__      )['''input_ids''']

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

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

					_a      :       List[Any]   =						tokenizer.encode_plus(lowercase__						,return_tensors='''pt'''      )
					_a      :       Union[str, Any]   =						hf_bort_model(**lowercase__      )[0]

					_a      :       Optional[int]   =						output_gluon[0].asnumpy()
					_a      :       Any   =						output_hf[0].detach().numpy()

					_a      :       Optional[int]   =						np.max(np.abs(hf_layer - gluon_layer      )      ).item()
					_a      :       List[str]   =						np.allclose(lowercase__						,lowercase__						,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:'''						,lowercase__      )


if __name__ == "__main__":
			a__												=						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.'''
			)
			a__												=						parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


