






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 : Any						   =       logging.get_logger(__name__)

lowerCamelCase : int						   =       '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str      ):
							__lowercase      :							List[str]				=     {
							    """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,
							}

							__lowercase      :							Optional[Any]				=     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
							__lowercase      :							Optional[Any]				=     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
							__lowercase      :							Dict				=     """openwebtext_ccnews_stories_books_cased"""

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

							__lowercase      :							Tuple				=     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_      )
							__lowercase      :							int				=     original_bort._collect_params_with_prefix()

							# Build our config 🤗
							__lowercase      :							List[str]				=     {
							    """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_      ),
							}

							__lowercase      :							Tuple				=     BertConfig.from_dict(lowerCAmelCase_      )
							__lowercase      :							Union[str, Any]				=     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_       :						Optional[Any]       ,       lowerCAmelCase_       :						Dict      ):
														__lowercase      :							List[str]				=     hf_param.shape

														__lowercase      :							Any				=     to_torch(params[gluon_param]      )
														__lowercase      :							List[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

							__lowercase      :							Tuple				=     check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight       ,       """word_embed.0.weight"""      )
							__lowercase      :							Dict				=     check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight       ,       """encoder.position_weight"""      )
							__lowercase      :							Dict				=     check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias       ,       """encoder.layer_norm.beta"""      )
							__lowercase      :							Optional[int]				=     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)
							__lowercase      :							int				=     torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data      )

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

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

														__lowercase      :							List[str]				=     check_and_map_params(
														    self_attn.key.bias.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"      )

														__lowercase      :							int				=     check_and_map_params(
														    self_attn.key.weight.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"      )
														__lowercase      :							Optional[int]				=     check_and_map_params(
														    self_attn.query.bias.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"      )
														__lowercase      :							Tuple				=     check_and_map_params(
														    self_attn.query.weight.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"      )
														__lowercase      :							Dict				=     check_and_map_params(
														    self_attn.value.bias.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"      )
														__lowercase      :							str				=     check_and_map_params(
														    self_attn.value.weight.data       ,       F"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"      )

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

														__lowercase      :							List[str]				=     check_and_map_params(
														    self_output.dense.bias       ,       F"encoder.transformer_cells.{i}.proj.bias"      )
														__lowercase      :							Tuple				=     check_and_map_params(
														    self_output.dense.weight       ,       F"encoder.transformer_cells.{i}.proj.weight"      )
														__lowercase      :							Any				=     check_and_map_params(
														    self_output.LayerNorm.bias       ,       F"encoder.transformer_cells.{i}.layer_norm.beta"      )
														__lowercase      :							List[Any]				=     check_and_map_params(
														    self_output.LayerNorm.weight       ,       F"encoder.transformer_cells.{i}.layer_norm.gamma"      )

														# intermediate
														__lowercase      :							BertIntermediate				=     layer.intermediate

														__lowercase      :							int				=     check_and_map_params(
														    intermediate.dense.bias       ,       F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"      )
														__lowercase      :							Tuple				=     check_and_map_params(
														    intermediate.dense.weight       ,       F"encoder.transformer_cells.{i}.ffn.ffn_1.weight"      )

														# output
														__lowercase      :							BertOutput				=     layer.output

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

							__lowercase      :							str				=     tokenizer.encode_plus(lowerCAmelCase_      )["""input_ids"""]

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

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(lowerCAmelCase_      )
							__lowercase      :							Dict				=     BertModel.from_pretrained(lowerCAmelCase_      )
							hf_bort_model.eval()

							__lowercase      :							List[Any]				=     tokenizer.encode_plus(lowerCAmelCase_       ,       return_tensors="""pt"""      )
							__lowercase      :							List[Any]				=     hf_bort_model(**lowerCAmelCase_      )[0]

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

							__lowercase      :							Optional[int]				=     np.max(np.abs(hf_layer - gluon_layer      )      ).item()
							__lowercase      :							Any				=     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 : Dict						   =       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 : List[Any]						   =       parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase : Union[str, Any]						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	int		,     *__a				:	Dict		,     **__a				:	Optional[Any]							)			->  None:


												"""simple docstring"""

												warnings.warn(
												    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use BeitImageProcessor instead."""		,     __a		,     )
												super().__init__(*__a		,     **__a							)