



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()
__snake_case						:int      		=     logging.get_logger(__name__)

__snake_case						:str      		=     '''The Nymphenburg Palace is a beautiful palace in Munich!'''





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				{
        '''attention_cell''': '''multi_head''',
        '''num_layers''': 4,
        '''units''': 1024,
        '''hidden_size''': 768,
        '''max_length''': 512,
        '''num_heads''': 8,
        '''scaled''': True,
        '''dropout''': 0.1,
        '''use_residual''': True,
        '''embed_size''': 1024,
        '''embed_dropout''': 0.1,
        '''word_embed''': None,
        '''layer_norm_eps''': 1E-5,
        '''token_type_vocab_size''': 2,
    }

    __a						       =				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						       =				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=_UpperCAmelCase   , output_all_encodings=_UpperCAmelCase   , use_residual=predefined_args['''use_residual''']   , activation=predefined_args.get('''activation'''   , '''gelu'''				)   , layer_norm_eps=predefined_args.get('''layer_norm_eps'''   , _UpperCAmelCase				)   , )

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

    # Specify download folder to Gluonnlp's vocab
    __a						       =				os.path.join(get_home_dir()   , '''models'''				)
    __a						       =				_load_vocab(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , cls=_UpperCAmelCase				)

    __a						       =				nlp.model.BERTModel(
        _UpperCAmelCase   , len(_UpperCAmelCase				)   , units=predefined_args['''units''']   , embed_size=predefined_args['''embed_size''']   , embed_dropout=predefined_args['''embed_dropout''']   , word_embed=predefined_args['''word_embed''']   , use_pooler=_UpperCAmelCase   , use_token_type_embed=_UpperCAmelCase   , token_type_vocab_size=predefined_args['''token_type_vocab_size''']   , use_classifier=_UpperCAmelCase   , use_decoder=_UpperCAmelCase   , )

    original_bort.load_parameters(_UpperCAmelCase   , cast_dtype=_UpperCAmelCase   , ignore_extra=_UpperCAmelCase				)
    __a						       =				original_bort._collect_params_with_prefix()

    # Build our config 🤗
    __a						       =				{
        '''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(_UpperCAmelCase				),
    }

    __a						       =				BertConfig.from_dict(_UpperCAmelCase				)
    __a						       =				BertForMaskedLM(_UpperCAmelCase				)
    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(_UpperCAmelCase				) -> 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(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				hf_param.shape

        __a						       =				to_torch(params[gluon_param]				)
        __a						       =				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						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight   , '''word_embed.0.weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight   , '''encoder.position_weight'''				)
    __a						       =				check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias   , '''encoder.layer_norm.beta'''				)
    __a						       =				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						       =				torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

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

        # self attention
        __a						       =				layer.attention.self

        __a						       =				check_and_map_params(
            self_attn.key.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'				)

        __a						       =				check_and_map_params(
            self_attn.key.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'				)
        __a						       =				check_and_map_params(
            self_attn.query.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'				)
        __a						       =				check_and_map_params(
            self_attn.query.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'				)
        __a						       =				check_and_map_params(
            self_attn.value.bias.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'				)
        __a						       =				check_and_map_params(
            self_attn.value.weight.data   , f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'				)

        # self attention output
        __a						       =				layer.attention.output

        __a						       =				check_and_map_params(
            self_output.dense.bias   , f'encoder.transformer_cells.{i}.proj.bias'				)
        __a						       =				check_and_map_params(
            self_output.dense.weight   , f'encoder.transformer_cells.{i}.proj.weight'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.layer_norm.beta'				)
        __a						       =				check_and_map_params(
            self_output.LayerNorm.weight   , f'encoder.transformer_cells.{i}.layer_norm.gamma'				)

        # intermediate
        __a						       =				layer.intermediate

        __a						       =				check_and_map_params(
            intermediate.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'				)
        __a						       =				check_and_map_params(
            intermediate.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'				)

        # output
        __a						       =				layer.output

        __a						       =				check_and_map_params(
            bert_output.dense.bias   , f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'				)
        __a						       =				check_and_map_params(
            bert_output.dense.weight   , f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'				)
        __a						       =				check_and_map_params(
            bert_output.LayerNorm.bias   , f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'				)
        __a						       =				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						       =				RobertaTokenizer.from_pretrained('''roberta-base'''				)

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase				)['''input_ids''']

    # Get gluon output
    __a						       =				mx.nd.array([input_ids]				)
    __a						       =				original_bort(inputs=_UpperCAmelCase   , token_types=[]				)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(_UpperCAmelCase				)
    __a						       =				BertModel.from_pretrained(_UpperCAmelCase				)
    hf_bort_model.eval()

    __a						       =				tokenizer.encode_plus(_UpperCAmelCase   , return_tensors='''pt'''				)
    __a						       =				hf_bort_model(**_UpperCAmelCase				)[0]

    __a						       =				output_gluon[0].asnumpy()
    __a						       =				output_hf[0].detach().numpy()

    __a						       =				np.max(np.abs(hf_layer - gluon_layer				)				).item()
    __a						       =				np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , 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:'''   , _UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:Optional[Any]      		=     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.'''
 )
 __snake_case						:Union[str, Any]      		=     parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





