
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()
__lowerCAmelCase									=logging.get_logger(__name__)

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



def   __UpperCamelCase						(				_lowerCAmelCase	,						_lowerCAmelCase ):
					"""simple docstring"""





					UpperCAmelCase				   =  {
					    "attention_cell": "multi_head",
					    "num_layers": 4,
					    "units": 10_24,
					    "hidden_size": 7_68,
					    "max_length": 5_12,
					    "num_heads": 8,
					    "scaled": True,
					    "dropout": 0.1,
					    "use_residual": True,
					    "embed_size": 10_24,
					    "embed_dropout": 0.1,
					    "word_embed": None,
					    "layer_norm_eps": 1E-5,
					    "token_type_vocab_size": 2,
					}

					UpperCAmelCase				   =  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
					UpperCAmelCase				   =  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
					UpperCAmelCase				   =  "openwebtext_ccnews_stories_books_cased"

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

					UpperCAmelCase				   =  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 )
					UpperCAmelCase				   =  original_bort._collect_params_with_prefix()

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

					UpperCAmelCase				   =  BertConfig.from_dict(__lowerCamelCase )
					UpperCAmelCase				   =  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 ):
										UpperCAmelCase				   =  hf_param.shape

										UpperCAmelCase				   =  to_torch(params[gluon_param] )
										UpperCAmelCase				   =  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

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

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

										# self attention
										UpperCAmelCase				   =  layer.attention.self

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

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

										# self attention output
										UpperCAmelCase				   =  layer.attention.output

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

										# intermediate
										UpperCAmelCase				   =  layer.intermediate

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

										# output
										UpperCAmelCase				   =  layer.output

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

					UpperCAmelCase				   =  tokenizer.encode_plus(__lowerCamelCase )["input_ids"]

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

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

					UpperCAmelCase				   =  tokenizer.encode_plus(__lowerCamelCase	,						return_tensors="pt" )
					UpperCAmelCase				   =  hf_bort_model(**__lowerCamelCase )[0]

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

					UpperCAmelCase				   =  np.max(np.abs(hf_layer - gluon_layer ) ).item()
					UpperCAmelCase				   =  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__":
				__lowerCAmelCase									=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."
				)
				__lowerCAmelCase									=parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''



import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_lowercase          =						False
try:
						_lowercase          =						_is_package_available("""google.colab""")
except ModuleNotFoundError:
						pass




@input.register
class     UpperCAmelCase_	:






						'''simple docstring'''






						def __init__(   self       ,  _lowercase = None       ,  _lowercase = [] ):

												"""simple docstring"""



												_lowerCAmelCase    		=					0
												_lowerCAmelCase    		=					choices
												_lowerCAmelCase    		=					prompt
												if sys.platform == "win32":
																		_lowerCAmelCase    		=					"""*"""
												else:
																		_lowerCAmelCase    		=					"""➔ """




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = "" ):

												"""simple docstring"""



												if sys.platform != "win32":
																		writeColor(self.choices[index]       ,  32       ,  _lowercase )
												else:
																		forceWrite(self.choices[index]       ,  _lowercase )




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												if index == self.position:
																		forceWrite(F' {self.arrow_char} ' )
																		self.write_choice(_lowercase )
												else:
																		forceWrite(F'    {self.choices[index]}' )
												reset_cursor()




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = 1 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.position
												if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices ):
																								return
																		self.position += num_spaces
												else:
																		if self.position - 1 < 0:
																								return
																		self.position -= num_spaces
												clear_line()
												self.print_choice(_lowercase )
												move_cursor(_lowercase       ,  direction.name )
												self.print_choice(self.position )




						@input.mark(KEYMAP["""up"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.UP )




						@input.mark(KEYMAP["""down"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.DOWN )




						@input.mark(KEYMAP["""newline"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												move_cursor(len(self.choices ) - self.position       ,  """DOWN""" )
												return self.position




						@input.mark(KEYMAP["""interrupt"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												move_cursor(len(self.choices ) - self.position       ,  """DOWN""" )
												raise KeyboardInterrupt




						@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					int(chr(self.current_selection ) )
												_lowerCAmelCase    		=					index - self.position
												if index == self.position:
																		return
												if index < len(self.choices ):
																		if self.position > index:
																								self.move_direction(Direction.UP       ,  -movement )
																		elif self.position < index:
																								self.move_direction(Direction.DOWN       ,  _lowercase )
																		else:
																								return
												else:
																		return




						def    _lowercase      (   self       ,  _lowercase = 0 ):

												"""simple docstring"""



												if self.prompt:
																		linebreak()
																		forceWrite(self.prompt       ,  """\n""" )
																		if in_colab:
																								forceWrite("""Please input a choice index (starting from 0), and press enter"""       ,  """\n""" )
																		else:
																								forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter"""       ,  """\n""" )
												_lowerCAmelCase    		=					default_choice
												for i in range(len(self.choices ) ):
																		self.print_choice(_lowercase )
																		forceWrite("""\n""" )
												move_cursor(len(self.choices ) - self.position       ,  """UP""" )
												with cursor.hide():
																		while True:
																								if in_colab:
																														try:
																																				_lowerCAmelCase    		=					int(builtins.input() )
																														except ValueError:
																																				_lowerCAmelCase    		=					default_choice
																								else:
																														_lowerCAmelCase    		=					self.handle_input()
																								if choice is not None:
																														reset_cursor()
																														for _ in range(len(self.choices ) + 1 ):
																																				move_cursor(1       ,  """UP""" )
																																				clear_line()
																														self.write_choice(_lowercase       ,  """\n""" )
																														return choice
