

'''simple docstring'''



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()
_UpperCamelCase						:  Tuple							       =				logging.get_logger(__name__)

_UpperCamelCase						:  List[Any]							       =				"The Nymphenburg Palace is a beautiful palace in Munich!"


def       snake_case   (	snake_case      : str					,		snake_case      : str			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							{
        '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,
    }

    lowerCAmelCase 						=							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 						=							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=snake_case					,		output_all_encodings=snake_case					,		use_residual=predefined_args['use_residual']					,		activation=predefined_args.get('activation'					,		'gelu'			)					,		layer_norm_eps=predefined_args.get('layer_norm_eps'					,		snake_case			)					,		)

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

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

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

    original_bort.load_parameters(snake_case					,		cast_dtype=snake_case					,		ignore_extra=snake_case			)
    lowerCAmelCase 						=							original_bort._collect_params_with_prefix()

    # Build our config 🤗
    lowerCAmelCase 						=							{
        '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(snake_case			),
    }

    lowerCAmelCase 						=							BertConfig.from_dict(snake_case			)
    lowerCAmelCase 						=							BertForMaskedLM(snake_case			)
    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(snake_case      : Tuple			) -> 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(snake_case      : List[str]					,		snake_case      : Union[str, Any]			):
        lowerCAmelCase 						=							hf_param.shape

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

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

        # self attention
        lowerCAmelCase 						=							layer.attention.self

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

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

        # self attention output
        lowerCAmelCase 						=							layer.attention.output

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

        # intermediate
        lowerCAmelCase 						=							layer.intermediate

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

        # output
        lowerCAmelCase 						=							layer.output

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

    lowerCAmelCase 						=							tokenizer.encode_plus(snake_case			)['input_ids']

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

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

    lowerCAmelCase 						=							tokenizer.encode_plus(snake_case					,		return_tensors='pt'			)
    lowerCAmelCase 						=							hf_bort_model(**snake_case			)[0]

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

    lowerCAmelCase 						=							np.max(np.abs(hf_layer - gluon_layer			)			).item()
    lowerCAmelCase 						=							np.allclose(snake_case					,		snake_case					,		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:'					,		snake_case			)


if __name__ == "__main__":
     _UpperCamelCase						:  Optional[int]							       =				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."
     )
     _UpperCamelCase						:  Any							       =				parser.parse_args()
     convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''



def       snake_case   (	snake_case      : int					,		snake_case      : int					,		snake_case      : int			) ->  int:


    """simple docstring"""


    if exponent == 1:
        return base
    if exponent % 2 == 0:
        lowerCAmelCase 						=							_modexpt(snake_case					,		exponent // 2					,		snake_case			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(snake_case					,		exponent - 1					,		snake_case			)) % modulo_value


def       snake_case   (	snake_case      : int = 1777					,		snake_case      : int = 1855					,		snake_case      : int = 8			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							base
    for _ in range(1					,		snake_case			):
        lowerCAmelCase 						=							_modexpt(snake_case					,		snake_case					,		10**digits			)

    return result


if __name__ == "__main__":
     print(F"""{solution() = }""")





