
'''simple docstring'''

import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
     import bitsandbytes as bnb
     import torch
     import torch.nn as nn

     from ..pytorch_utils import ConvaD

if is_accelerate_available():
     from accelerate import init_empty_weights
     from accelerate.utils import find_tied_parameters

UpperCamelCase_   	=				logging.get_logger(__name__)


def 		_UpperCAmelCase (     _lowerCamelCase  :				int     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				List[Any]=None     ,					_lowerCamelCase  :				Optional[int]=None						)			->  Optional[Any]:
  # Recurse if needed
  if "." in tensor_name:
    _lowerCAmelCase				:     List[Any]       =    tensor_name.split("""."""						)
    for split in splits[:-1]:
      _lowerCAmelCase				:     int       =    getattr(_lowerCamelCase     ,					_lowerCamelCase						)
      if new_module is None:
        raise ValueError(f'{module} has no attribute {split}.'						)
      _lowerCAmelCase				:     Tuple       =    new_module
    _lowerCAmelCase				:     int       =    splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(f'{module} does not have a parameter or a buffer named {tensor_name}.'						)
  _lowerCAmelCase				:     Tuple       =    tensor_name in module._buffers
  _lowerCAmelCase				:     Tuple       =    getattr(_lowerCamelCase     ,					_lowerCamelCase						)

  if old_value.device == torch.device("""meta"""						) and device not in ["meta", torch.device("""meta"""						)] and value is None:
    raise ValueError(f'{tensor_name} is on the meta device, we need a `value` to put in on {device}.'						)

  _lowerCAmelCase				:     Optional[Any]       =    False
  _lowerCAmelCase				:     List[str]       =    False
  if is_buffer or not is_bitsandbytes_available():
    _lowerCAmelCase				:     List[Any]       =    False
    _lowerCAmelCase				:     Dict       =    False
  else:
    _lowerCAmelCase				:     Optional[Any]       =    hasattr(bnb.nn     ,					"""Params4bit"""						) and isinstance(module._parameters[tensor_name]     ,					bnb.nn.Paramsabit						)
    _lowerCAmelCase				:     Optional[int]       =    isinstance(module._parameters[tensor_name]     ,					bnb.nn.IntaParams						)

  if is_abit or is_abit:
    _lowerCAmelCase				:     Optional[int]       =    module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        _lowerCAmelCase				:     int       =    old_value.to(_lowerCamelCase						)
      elif isinstance(_lowerCamelCase     ,					torch.Tensor						):
        _lowerCAmelCase				:     Dict       =    value.to("""cpu"""						)
        if value.dtype == torch.inta:
          _lowerCAmelCase				:     List[str]       =    version.parse(importlib.metadata.version("""bitsandbytes"""						)						) > version.parse(
              """0.37.2"""						)
          if not is_abit_serializable:
            raise ValueError(
                """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
                """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."""						)
      else:
        _lowerCAmelCase				:     Union[str, Any]       =    torch.tensor(_lowerCamelCase     ,					device="""cpu"""						)

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls     ,					_lowerCamelCase						) and fpaa_statistics is None:
        _lowerCAmelCase				:     Any       =    new_value.T

      _lowerCAmelCase				:     Union[str, Any]       =    old_value.__dict__
      if is_abit:
        _lowerCAmelCase				:     Optional[Any]       =    bnb.nn.IntaParams(_lowerCamelCase     ,					requires_grad=_lowerCamelCase     ,					**_lowerCamelCase						).to(_lowerCamelCase						)
      elif is_abit:
        _lowerCAmelCase				:     Tuple       =    bnb.nn.Paramsabit(_lowerCamelCase     ,					requires_grad=_lowerCamelCase     ,					**_lowerCamelCase						).to(_lowerCamelCase						)

      _lowerCAmelCase				:     Optional[int]       =    new_value
      if fpaa_statistics is not None:
        setattr(module.weight     ,					"""SCB"""     ,					fpaa_statistics.to(_lowerCamelCase						)						)

  else:
    if value is None:
      _lowerCAmelCase				:     Dict       =    old_value.to(_lowerCamelCase						)
    elif isinstance(_lowerCamelCase     ,					torch.Tensor						):
      _lowerCAmelCase				:     Union[str, Any]       =    value.to(_lowerCamelCase						)
    else:
      _lowerCAmelCase				:     str       =    torch.tensor(_lowerCamelCase     ,					device=_lowerCamelCase						)

    if is_buffer:
      _lowerCAmelCase				:     List[Any]       =    new_value
    else:
      _lowerCAmelCase				:     str       =    nn.Parameter(_lowerCamelCase     ,					requires_grad=old_value.requires_grad						)
      _lowerCAmelCase				:     Optional[int]       =    new_value


def 		_UpperCAmelCase (     _lowerCamelCase  :				Union[str, Any]     ,					_lowerCamelCase  :				int=None     ,					_lowerCamelCase  :				Any=None     ,					_lowerCamelCase  :				Optional[int]=None     ,					_lowerCamelCase  :				Optional[Any]=False						)			->  str:
  for name, module in model.named_children():
    if current_key_name is None:
      _lowerCAmelCase				:     Optional[Any]       =    []
    current_key_name.append(_lowerCamelCase						)

    if (isinstance(_lowerCamelCase     ,					nn.Linear						) or isinstance(_lowerCamelCase     ,					_lowerCamelCase						)) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in """.""".join(_lowerCamelCase						) for key in modules_to_not_convert						):
        with init_empty_weights():
          if isinstance(_lowerCamelCase     ,					_lowerCamelCase						):
            _lowerCAmelCase				:     str       =    module.weight.shape
          else:
            _lowerCAmelCase				:     int       =    module.in_features
            _lowerCAmelCase				:     List[Any]       =    module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            _lowerCAmelCase				:     int       =    bnb.nn.LinearabitLt(
                _lowerCamelCase     ,					_lowerCamelCase     ,					module.bias is not None     ,					has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight     ,					threshold=quantization_config.llm_inta_threshold     ,					)
            _lowerCAmelCase				:     List[Any]       =    True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              _lowerCAmelCase				:     Any       =    bnb.nn.Linearabit(
                  _lowerCamelCase     ,					_lowerCamelCase     ,					module.bias is not None     ,					quantization_config.bnb_abit_compute_dtype     ,					compress_statistics=quantization_config.bnb_abit_use_double_quant     ,					quant_type=quantization_config.bnb_abit_quant_type     ,					)
              _lowerCAmelCase				:     Union[str, Any]       =    True
                    # Store the module class in case we need to transpose the weight later
          _lowerCAmelCase				:     str       =    type(_lowerCamelCase						)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(_lowerCamelCase						)
    if len(list(module.children()						)						) > 0:
      _lowerCAmelCase				:     str       =    _replace_with_bnb_linear(
          _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					has_been_replaced=_lowerCamelCase     ,					)
    # Remove the last key for recursion
    current_key_name.pop(-1						)
  return model, has_been_replaced


def 		_UpperCAmelCase (     _lowerCamelCase  :				Optional[int]     ,					_lowerCamelCase  :				Optional[Any]=None     ,					_lowerCamelCase  :				int=None     ,					_lowerCamelCase  :				Tuple=None						)			->  Any:
  _lowerCAmelCase				:     Optional[int]       =    ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
  _lowerCAmelCase				:     Optional[Any]       =    _replace_with_bnb_linear(
      _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase						)

  if not has_been_replaced:
    logger.warning(
        """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
        """ Please double check your model architecture, or submit an issue on github if you think this is"""
        """ a bug."""						)

  return model


def 		_UpperCAmelCase (     *_lowerCamelCase  :				Any     ,					**_lowerCamelCase  :				List[Any]						)			->  str:
  warnings.warn(
      """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""     ,					_lowerCamelCase     ,					)
  return replace_with_bnb_linear(*_lowerCamelCase     ,					**_lowerCamelCase						)


def 		_UpperCAmelCase (     *_lowerCamelCase  :				str     ,					**_lowerCamelCase  :				Optional[Any]						)			->  Tuple:
  warnings.warn(
      """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""     ,					_lowerCamelCase     ,					)
  return set_module_quantized_tensor_to_device(*_lowerCamelCase     ,					**_lowerCamelCase						)



def 		_UpperCAmelCase (     _lowerCamelCase  :				str						)			->  Dict:
  _lowerCAmelCase				:     str       =    deepcopy(_lowerCamelCase						)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  _lowerCAmelCase				:     Tuple       =    find_tied_parameters(_lowerCamelCase						)
  # For compatibility with Accelerate < 0.18
  if isinstance(_lowerCamelCase     ,					_lowerCamelCase						):
    _lowerCAmelCase				:     Optional[int]       =    sum(list(tied_params.values()						)     ,					[]						) + list(tied_params.keys()						)
  else:
    _lowerCAmelCase				:     Dict       =    sum(_lowerCamelCase     ,					[]						)
  _lowerCAmelCase				:     Optional[int]       =    len(_lowerCamelCase						) > 0

  # Check if it is a base model
  _lowerCAmelCase				:     Dict       =    not hasattr(_lowerCamelCase     ,					model.base_model_prefix						)

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  _lowerCAmelCase				:     Tuple       =    list(model.named_children()						)
  _lowerCAmelCase				:     Optional[int]       =    [list_modules[-1][0]]

  # add last module together with tied weights
  _lowerCAmelCase				:     List[Any]       =    set(_lowerCamelCase						) - set(_lowerCamelCase						)
  _lowerCAmelCase				:     int       =    list(set(_lowerCamelCase						)						) + list(_lowerCamelCase						)

  # remove ".weight" from the keys
  _lowerCAmelCase				:     List[Any]       =    ['.weight', '.bias']
  _lowerCAmelCase				:     int       =    []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        _lowerCAmelCase				:     int       =    name.replace(_lowerCamelCase     ,					""""""						)
    filtered_module_names.append(_lowerCamelCase						)

  return filtered_module_names









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()
UpperCAmelCase						:   List[Any]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   Optional[Any]			  =     '''The Nymphenburg Palace is a beautiful palace in Munich!'''







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Optional[Any]:
					__A	:						Any       =				{
					    '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,
					}

					__A	:						str       =				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	:						Optional[int]       =				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
					__A	:						Union[str, Any]       =				'openwebtext_ccnews_stories_books_cased'

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

					__A	:						Dict       =				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  )
					__A	:						Union[str, Any]       =				original_bort._collect_params_with_prefix()

					# Build our config 🤗
					__A	:						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(a  ),
					}

					__A	:						int       =				BertConfig.from_dict(a  )
					__A	:						Union[str, Any]       =				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  ) -> 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					,						a  ):
										__A	:						Tuple       =				hf_param.shape

										__A	:						str       =				to_torch(params[gluon_param]  )
										__A	:						Union[str, Any]       =				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	:						Tuple       =				check_and_map_params(
					    hf_bort_model.bert.embeddings.position_embeddings.weight					,						'encoder.position_weight'  )
					__A	:						List[str]       =				check_and_map_params(
					    hf_bort_model.bert.embeddings.LayerNorm.bias					,						'encoder.layer_norm.beta'  )
					__A	:						Tuple       =				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	:						Tuple       =				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[Any]       =				check_and_map_params(
										    self_attn.key.bias.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""  )

										__A	:						Optional[int]       =				check_and_map_params(
										    self_attn.key.weight.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""  )
										__A	:						Union[str, Any]       =				check_and_map_params(
										    self_attn.query.bias.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""  )
										__A	:						Optional[Any]       =				check_and_map_params(
										    self_attn.query.weight.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""  )
										__A	:						Union[str, Any]       =				check_and_map_params(
										    self_attn.value.bias.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""  )
										__A	:						Optional[int]       =				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	:						int       =				check_and_map_params(
										    self_output.dense.weight					,						F"""encoder.transformer_cells.{i}.proj.weight"""  )
										__A	:						List[Any]       =				check_and_map_params(
										    self_output.LayerNorm.bias					,						F"""encoder.transformer_cells.{i}.layer_norm.beta"""  )
										__A	:						str       =				check_and_map_params(
										    self_output.LayerNorm.weight					,						F"""encoder.transformer_cells.{i}.layer_norm.gamma"""  )

										# intermediate
										__A	:						BertIntermediate       =				layer.intermediate

										__A	:						int       =				check_and_map_params(
										    intermediate.dense.bias					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""  )
										__A	:						List[Any]       =				check_and_map_params(
										    intermediate.dense.weight					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""  )

										# output
										__A	:						BertOutput       =				layer.output

										__A	:						List[Any]       =				check_and_map_params(
										    bert_output.dense.bias					,						F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""  )
										__A	:						Dict       =				check_and_map_params(
										    bert_output.dense.weight					,						F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""  )
										__A	:						Optional[int]       =				check_and_map_params(
										    bert_output.LayerNorm.bias					,						F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""  )
										__A	:						Dict       =				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	:						Any       =				RobertaTokenizer.from_pretrained('roberta-base'  )

					__A	:						List[str]       =				tokenizer.encode_plus(a  )['input_ids']

					# Get gluon output
					__A	:						List[str]       =				mx.nd.array([input_ids]  )
					__A	:						Union[str, Any]       =				original_bort(inputs=a					,						token_types=[]  )

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

					__A	:						Tuple       =				tokenizer.encode_plus(a					,						return_tensors='pt'  )
					__A	:						Any       =				hf_bort_model(**a  )[0]

					__A	:						Union[str, Any]       =				output_gluon[0].asnumpy()
					__A	:						Tuple       =				output_hf[0].detach().numpy()

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




