


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

SCREAMING_SNAKE_CASE :   Dict							      =				"The Nymphenburg Palace is a beautiful palace in Munich!"



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							Union[str, Any]   	=	{
        '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,
    }

    _lowercase :							str   	=	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
    _lowercase :							Union[str, 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=lowerCamelCase_							,				output_all_encodings=lowerCamelCase_							,				use_residual=predefined_args['use_residual']							,				activation=predefined_args.get('activation'							,				'gelu'				)							,				layer_norm_eps=predefined_args.get('layer_norm_eps'							,				lowerCamelCase_				)							,				)

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

    # Specify download folder to Gluonnlp's vocab
    _lowercase :							str   	=	os.path.join(get_home_dir()							,				'models'				)
    _lowercase :							str   	=	_load_vocab(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				cls=lowerCamelCase_				)

    _lowercase :							Dict   	=	nlp.model.BERTModel(
        lowerCamelCase_							,				len(lowerCamelCase_				)							,				units=predefined_args['units']							,				embed_size=predefined_args['embed_size']							,				embed_dropout=predefined_args['embed_dropout']							,				word_embed=predefined_args['word_embed']							,				use_pooler=lowerCamelCase_							,				use_token_type_embed=lowerCamelCase_							,				token_type_vocab_size=predefined_args['token_type_vocab_size']							,				use_classifier=lowerCamelCase_							,				use_decoder=lowerCamelCase_							,				)

    original_bort.load_parameters(lowerCamelCase_							,				cast_dtype=lowerCamelCase_							,				ignore_extra=lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	original_bort._collect_params_with_prefix()

    # Build our config 🤗
    _lowercase :							List[Any]   	=	{
        '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(lowerCamelCase_				),
    }

    _lowercase :							Dict   	=	BertConfig.from_dict(lowerCamelCase_				)
    _lowercase :							str   	=	BertForMaskedLM(lowerCamelCase_				)
    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(lowerCamelCase_				) -> 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(lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							List[str]   	=	hf_param.shape

        _lowercase :							int   	=	to_torch(params[gluon_param]				)
        _lowercase :							Optional[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

    _lowercase :							Union[str, Any]   	=	check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight							,				'word_embed.0.weight'				)
    _lowercase :							int   	=	check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight							,				'encoder.position_weight'				)
    _lowercase :							List[str]   	=	check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias							,				'encoder.layer_norm.beta'				)
    _lowercase :							List[str]   	=	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)
    _lowercase :							Any   	=	torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data				)

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

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

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

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

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

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

        # intermediate
        _lowercase :							BertIntermediate   	=	layer.intermediate

        _lowercase :							str   	=	check_and_map_params(
            intermediate.dense.bias							,				F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''				)
        _lowercase :							Optional[Any]   	=	check_and_map_params(
            intermediate.dense.weight							,				F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''				)

        # output
        _lowercase :							BertOutput   	=	layer.output

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

    _lowercase :							Union[str, Any]   	=	tokenizer.encode_plus(lowerCamelCase_				)['input_ids']

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

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(lowerCamelCase_				)
    _lowercase :							Dict   	=	BertModel.from_pretrained(lowerCamelCase_				)
    hf_bort_model.eval()

    _lowercase :							Dict   	=	tokenizer.encode_plus(lowerCamelCase_							,				return_tensors='pt'				)
    _lowercase :							Optional[Any]   	=	hf_bort_model(**lowerCamelCase_				)[0]

    _lowercase :							str   	=	output_gluon[0].asnumpy()
    _lowercase :							str   	=	output_hf[0].detach().numpy()

    _lowercase :							str   	=	np.max(np.abs(hf_layer - gluon_layer				)				).item()
    _lowercase :							Dict   	=	np.allclose(lowerCamelCase_							,				lowerCamelCase_							,				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:'							,				lowerCamelCase_				)


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




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor






class _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	parent




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return {}



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							int   	=	'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

    _lowercase :							List[Any]   	=	'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

    return [html_string_a, html_string_a]






@require_bsa
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Tuple									=    MarkupLMFeatureExtractor if is_bsa_available() else None
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	MarkupLMFeatureExtractionTester(self)
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           return self.feature_extract_tester.prepare_feat_extract_dict()




       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extraction_class()

           # Test not batched input
           _lowercase :							List[Any]   	=	get_html_strings()[0]
           _lowercase :							Optional[Any]   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							str   	=	[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
           _lowercase :							Dict   	=	[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
           # fmt: on

           self.assertEqual(encoding.nodes,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)

           # Test batched
           _lowercase :							Optional[int]   	=	get_html_strings()
           _lowercase :							Tuple   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							List[Any]   	=	expected_nodes + [['My First Heading', 'My first paragraph.']]
           _lowercase :							List[str]   	=	expected_xpaths + [['/html/body/h1', '/html/body/p']]

           self.assertEqual(len(encoding.nodes),   2)
           self.assertEqual(len(encoding.xpaths),   2)

           self.assertEqual(encoding.nodes,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)


