






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						:	List[Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       {
							    """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,
							}

							snake_case__	  =       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
							snake_case__	  =       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
							snake_case__	  =       """openwebtext_ccnews_stories_books_cased"""

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

							snake_case__	  =       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 )
							snake_case__	  =       original_bort._collect_params_with_prefix()

							# Build our config 🤗
							snake_case__	  =       {
							    """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 ),
							}

							snake_case__	  =       BertConfig.from_dict(a )
							snake_case__	  =       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						:	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						:	Any  ,					a						:	List[Any] ):
														snake_case__	  =       hf_param.shape

														snake_case__	  =       to_torch(params[gluon_param] )
														snake_case__	  =       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

							snake_case__	  =       check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight  ,					"""word_embed.0.weight""" )
							snake_case__	  =       check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight  ,					"""encoder.position_weight""" )
							snake_case__	  =       check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias  ,					"""encoder.layer_norm.beta""" )
							snake_case__	  =       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)
							snake_case__	  =       torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

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

														# self attention
														snake_case__	  =       layer.attention.self

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

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

														# self attention output
														snake_case__	  =       layer.attention.output

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

														# intermediate
														snake_case__	  =       layer.intermediate

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

														# output
														snake_case__	  =       layer.output

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

							snake_case__	  =       tokenizer.encode_plus(a )["""input_ids"""]

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

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

							snake_case__	  =       tokenizer.encode_plus(a  ,					return_tensors="""pt""" )
							snake_case__	  =       hf_bort_model(**a )[0]

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

							snake_case__	  =       np.max(np.abs(hf_layer - gluon_layer ) ).item()
							snake_case__	  =       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__":
							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)








def _UpperCAmelCase   (	a						:	int  ,					a						:	Optional[Any] ):
							# "extended trapezoidal rule"
							# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
							snake_case__	  =       (boundary[1] - boundary[0]) / steps
							snake_case__	  =       boundary[0]
							snake_case__	  =       boundary[1]
							snake_case__	  =       make_points(a  ,					a  ,					a )
							snake_case__	  =       0.0
							y += (h / 2.0) * f(a )
							for i in x_i:
														# print(i)
														y += h * f(a )
							y += (h / 2.0) * f(a )
							return y

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       a + h
							while x < (b - h):
														yield x
														snake_case__	  =       x + h

def _UpperCAmelCase   (	a						:	Any ):  # enter your function here
							snake_case__	  =       (x - 0) * (x - 0)
							return y

def _UpperCAmelCase   (	):
							snake_case__	  =       0.0  # Lower bound of integration
							snake_case__	  =       1.0  # Upper bound of integration
							snake_case__	  =       10.0  # define number of steps or resolution
							snake_case__	  =       [a, b]  # define boundary of integration
							snake_case__	  =       method_a(a  ,					a )
							print(F'''y = {y}''' )


if __name__ == "__main__":
	main()
