
'''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()
_lowercase          =						logging.get_logger(__name__)

_lowercase          =						"""The Nymphenburg Palace is a beautiful palace in Munich!"""


def A (__lowerCamelCase :str					,      __lowerCamelCase :str						):
						_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=__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
						_lowerCAmelCase    		=					"""openwebtext_ccnews_stories_books_cased"""

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

						_lowerCAmelCase    		=					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						)
						_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(__lowerCamelCase						),
						}

						_lowerCAmelCase    		=					BertConfig.from_dict(__lowerCamelCase						)
						_lowerCAmelCase    		=					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 :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(__lowerCamelCase :Any					,      __lowerCamelCase :Tuple						):
												_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(__lowerCamelCase						)["""input_ids"""]

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

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

						_lowerCAmelCase    		=					tokenizer.encode_plus(__lowerCamelCase					,      return_tensors="""pt"""						)
						_lowerCAmelCase    		=					hf_bort_model(**__lowerCamelCase						)[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(__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__":
						_lowercase          =						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."""
						)
						_lowercase          =						parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''



_lowercase          =						"""
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowercase          =						[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase          =						{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
