






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__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			'''The Nymphenburg Palace is a beautiful palace in Munich!'''

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       str				):
			__a						:  List[Any]									=     {
			    'attention_cell': 'multi_head',
			    'num_layers': 4,
			    'units': 1_0_2_4,
			    'hidden_size': 7_6_8,
			    'max_length': 5_1_2,
			    'num_heads': 8,
			    'scaled': True,
			    'dropout': 0.1,
			    'use_residual': True,
			    'embed_size': 1_0_2_4,
			    'embed_dropout': 0.1,
			    'word_embed': None,
			    'layer_norm_eps': 1e-5,
			    'token_type_vocab_size': 2,
			}

			__a						:  Optional[int]									=     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
			__a						:  List[str]									=     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
			__a						:  int									=     'openwebtext_ccnews_stories_books_cased'

			# Specify download folder to Gluonnlp's vocab
			__a						:  Optional[Any]									=     os.path.join(get_home_dir()   ,  'models'				)
			__a						:  Optional[Any]									=     _load_vocab(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  cls=lowerCamelCase_				)

			__a						:  Any									=     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_				)
			__a						:  Dict									=     original_bort._collect_params_with_prefix()

			# Build our config 🤗
			__a						:  Optional[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_				),
			}

			__a						:  str									=     BertConfig.from_dict(lowerCamelCase_				)
			__a						:  Optional[int]									=     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_       :       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(lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       List[str]				):
						__a						:  Optional[int]									=     hf_param.shape

						__a						:  int									=     to_torch(params[gluon_param]				)
						__a						:  int									=     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

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

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

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

						__a						:  Optional[int]									=     check_and_map_params(
						    self_attn.key.bias.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''				)

						__a						:  str									=     check_and_map_params(
						    self_attn.key.weight.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''				)
						__a						:  List[str]									=     check_and_map_params(
						    self_attn.query.bias.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''				)
						__a						:  str									=     check_and_map_params(
						    self_attn.query.weight.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''				)
						__a						:  Dict									=     check_and_map_params(
						    self_attn.value.bias.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''				)
						__a						:  str									=     check_and_map_params(
						    self_attn.value.weight.data   ,  f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''				)

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

						__a						:  Tuple									=     check_and_map_params(
						    self_output.dense.bias   ,  f'''encoder.transformer_cells.{i}.proj.bias'''				)
						__a						:  Dict									=     check_and_map_params(
						    self_output.dense.weight   ,  f'''encoder.transformer_cells.{i}.proj.weight'''				)
						__a						:  Optional[Any]									=     check_and_map_params(
						    self_output.LayerNorm.bias   ,  f'''encoder.transformer_cells.{i}.layer_norm.beta'''				)
						__a						:  Optional[Any]									=     check_and_map_params(
						    self_output.LayerNorm.weight   ,  f'''encoder.transformer_cells.{i}.layer_norm.gamma'''				)

						# intermediate
						__a						:  BertIntermediate									=     layer.intermediate

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

						# output
						__a						:  BertOutput									=     layer.output

						__a						:  str									=     check_and_map_params(
						    bert_output.dense.bias   ,  f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''				)
						__a						:  List[Any]									=     check_and_map_params(
						    bert_output.dense.weight   ,  f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''				)
						__a						:  str									=     check_and_map_params(
						    bert_output.LayerNorm.bias   ,  f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''				)
						__a						:  List[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
			__a						:  Union[str, Any]									=     RobertaTokenizer.from_pretrained('roberta-base'				)

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

			# Get gluon output
			__a						:  Optional[int]									=     mx.nd.array([input_ids]				)
			__a						:  Tuple									=     original_bort(inputs=lowerCamelCase_   ,  token_types=[]				)

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(lowerCamelCase_				)
			__a						:  Optional[Any]									=     BertModel.from_pretrained(lowerCamelCase_				)
			hf_bort_model.eval()

			__a						:  Union[str, Any]									=     tokenizer.encode_plus(lowerCamelCase_   ,  return_tensors='pt'				)
			__a						:  int									=     hf_bort_model(**lowerCamelCase_				)[0]

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

			__a						:  List[Any]									=     np.max(np.abs(hf_layer - gluon_layer				)				).item()
			__a						:  str									=     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__				     =			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__				     =			parser.parse_args()
							convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""





from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def  lowerCamelCase__						(     )				->						List[str]:


					"""simple docstring"""





					import os as original_os
					from os import path as original_path
					from os import rename as original_rename
					from os.path import dirname as original_dirname
					from os.path import join as original_join

					assert _test_patching.os is original_os
					assert _test_patching.path is original_path
					assert _test_patching.join is original_join

					assert _test_patching.renamed_os is original_os
					assert _test_patching.renamed_path is original_path
					assert _test_patching.renamed_join is original_join

					_UpperCamelCase									=    '''__test_patch_submodule_mock__'''
					with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
										# Every way to access os.path.join must be patched, and the rest must stay untouched

										# check os.path.join
										assert isinstance(_test_patching.os,	_PatchedModuleObj      )
										assert isinstance(_test_patching.os.path,	_PatchedModuleObj      )
										assert _test_patching.os.path.join is mock

										# check path.join
										assert isinstance(_test_patching.path,	_PatchedModuleObj      )
										assert _test_patching.path.join is mock

										# check join
										assert _test_patching.join is mock

										# check that the other attributes are untouched
										assert _test_patching.os.rename is original_rename
										assert _test_patching.path.dirname is original_dirname
										assert _test_patching.os.path.dirname is original_dirname

										# Even renamed modules or objects must be patched

										# check renamed_os.path.join
										assert isinstance(_test_patching.renamed_os,	_PatchedModuleObj      )
										assert isinstance(_test_patching.renamed_os.path,	_PatchedModuleObj      )
										assert _test_patching.renamed_os.path.join is mock

										# check renamed_path.join
										assert isinstance(_test_patching.renamed_path,	_PatchedModuleObj      )
										assert _test_patching.renamed_path.join is mock

										# check renamed_join
										assert _test_patching.renamed_join is mock

										# check that the other attributes are untouched
										assert _test_patching.renamed_os.rename is original_rename
										assert _test_patching.renamed_path.dirname is original_dirname
										assert _test_patching.renamed_os.path.dirname is original_dirname

					# check that everthing is back to normal when the patch is over

					assert _test_patching.os is original_os
					assert _test_patching.path is original_path
					assert _test_patching.join is original_join

					assert _test_patching.renamed_os is original_os
					assert _test_patching.renamed_path is original_path
					assert _test_patching.renamed_join is original_join



def  lowerCamelCase__						(     )				->						List[str]:


					"""simple docstring"""





					assert _test_patching.open is open

					_UpperCamelCase									=    '''__test_patch_submodule_builtin_mock__'''
					# _test_patching has "open" in its globals
					assert _test_patching.open is open
					with patch_submodule(_test_patching,	'''open''',	__snake_case      ):
										assert _test_patching.open is mock

					# check that everthing is back to normal when the patch is over

					assert _test_patching.open is open



def  lowerCamelCase__						(     )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_missing_mock__'''
					with patch_submodule(_test_patching,	'''pandas.read_csv''',	__snake_case      ):
										pass



def  lowerCamelCase__						(     )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_missing_builtin_mock__'''
					# _test_patching doesn't have "len" in its globals
					assert getattr(_test_patching,	'''len''',	__snake_case      ) is None
					with patch_submodule(_test_patching,	'''len''',	__snake_case      ):
										assert _test_patching.len is mock
					assert _test_patching.len is len



def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_start_and_stop_mock__'''
					_UpperCamelCase									=    patch_submodule(_test_patching,	'''open''',	__snake_case      )
					assert _test_patching.open is open
					patch.start()
					assert _test_patching.open is mock
					patch.stop()
					assert _test_patching.open is open



def  lowerCamelCase__						(     )				->						Optional[int]:


					"""simple docstring"""





					from os import rename as original_rename
					from os.path import dirname as original_dirname
					from os.path import join as original_join

					_UpperCamelCase									=    '''__test_patch_submodule_successive_join__'''
					_UpperCamelCase									=    '''__test_patch_submodule_successive_dirname__'''
					_UpperCamelCase									=    '''__test_patch_submodule_successive_rename__'''
					assert _test_patching.os.path.join is original_join
					assert _test_patching.os.path.dirname is original_dirname
					assert _test_patching.os.rename is original_rename

					with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.rename''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				assert _test_patching.os.path.join is mock_join
																				assert _test_patching.os.path.dirname is mock_dirname
																				assert _test_patching.os.rename is mock_rename

    # try another order
					with patch_submodule(_test_patching,	'''os.rename''',	__snake_case      ):
										with patch_submodule(_test_patching,	'''os.path.join''',	__snake_case      ):
															with patch_submodule(_test_patching,	'''os.path.dirname''',	__snake_case      ):
																				assert _test_patching.os.path.join is mock_join
																				assert _test_patching.os.path.dirname is mock_dirname
																				assert _test_patching.os.rename is mock_rename

					assert _test_patching.os.path.join is original_join
					assert _test_patching.os.path.dirname is original_dirname
					assert _test_patching.os.rename is original_rename



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    '''__test_patch_submodule_doesnt_exist_mock__'''
					with patch_submodule(_test_patching,	'''__module_that_doesn_exist__.__attribute_that_doesn_exist__''',	__snake_case      ):
										pass
					with patch_submodule(_test_patching,	'''os.__attribute_that_doesn_exist__''',	__snake_case      ):
										pass






