


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							:					Dict     						=     logging.get_logger(__name__)

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Tuple:


						'''simple docstring'''






						__snake_case				=		{
						    '''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,
						}

						__snake_case				=		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
						__snake_case				=		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=_lowercase      ,	output_all_encodings=_lowercase      ,	use_residual=predefined_args["use_residual"]      ,	activation=predefined_args.get("activation"      ,	"gelu" )      ,	layer_norm_eps=predefined_args.get("layer_norm_eps"      ,	_lowercase )      ,	)

						# Vocab information needs to be fetched first
						# It's the same as RoBERTa, so RobertaTokenizer can be used later
						__snake_case				=		'''openwebtext_ccnews_stories_books_cased'''

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

						__snake_case				=		nlp.model.BERTModel(
						    _lowercase      ,	len(_lowercase )      ,	units=predefined_args["units"]      ,	embed_size=predefined_args["embed_size"]      ,	embed_dropout=predefined_args["embed_dropout"]      ,	word_embed=predefined_args["word_embed"]      ,	use_pooler=_lowercase      ,	use_token_type_embed=_lowercase      ,	token_type_vocab_size=predefined_args["token_type_vocab_size"]      ,	use_classifier=_lowercase      ,	use_decoder=_lowercase      ,	)

						original_bort.load_parameters(_lowercase      ,	cast_dtype=_lowercase      ,	ignore_extra=_lowercase )
						__snake_case				=		original_bort._collect_params_with_prefix()

						# Build our config 🤗
						__snake_case				=		{
						    '''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(_lowercase ),
						}

						__snake_case				=		BertConfig.from_dict(_lowercase )
						__snake_case				=		BertForMaskedLM(_lowercase )
						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 ):
												__snake_case				=		hf_param.shape

												__snake_case				=		to_torch(params[gluon_param] )
												__snake_case				=		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

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

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

												# self attention
												__snake_case				=		layer.attention.self

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

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

												# self attention output
												__snake_case				=		layer.attention.output

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

												# intermediate
												__snake_case				=		layer.intermediate

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

												# output
												__snake_case				=		layer.output

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

						__snake_case				=		tokenizer.encode_plus(_lowercase )['''input_ids''']

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

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

						__snake_case				=		tokenizer.encode_plus(_lowercase      ,	return_tensors="pt" )
						__snake_case				=		hf_bort_model(**_lowercase )[0]

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

						__snake_case				=		np.max(np.abs(hf_layer - gluon_layer ) ).item()
						__snake_case				=		np.allclose(_lowercase      ,	_lowercase      ,	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:"      ,	_lowercase )


if __name__ == "__main__":
	A							:					Optional[Any]     						=     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							:					Optional[Any]     						=     parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





