
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   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str							):




			"""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=__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    :    int							) -> 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    :    int       ,				__lowerCAmelCase    :    List[str]							):
						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__":
			_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)





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if version.parse(hfh.__version__							).release < version.parse("0.11.0"							).release:
						# old versions of hfh don't url-encode the file path
						lowerCAmelCase_         =      quote(__lowerCAmelCase							)
			return hfh.hf_hub_url(__lowerCAmelCase       ,				__lowerCAmelCase       ,				repo_type="dataset"       ,				revision=__lowerCAmelCase							)



