



"""simple docstring"""






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

_lowercase			:			Union[str, Any]										=						"The Nymphenburg Palace is a beautiful palace in Munich!"






def 					_UpperCAmelCase       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     ={
			    '''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,
			}

			lowerCamelCase__		:		Tuple		     =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
			lowerCamelCase__		:		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
			lowerCamelCase__		:		Any		     ='''openwebtext_ccnews_stories_books_cased'''

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

			lowerCamelCase__		:		Union[str, 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     )
			lowerCamelCase__		:		List[Any]		     =original_bort._collect_params_with_prefix()

			# Build our config 🤗
			lowerCamelCase__		:		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     ),
			}

			lowerCamelCase__		:		Optional[int]		     =BertConfig.from_dict(__lowerCamelCase     )
			lowerCamelCase__		:		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    :   Union[str, 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    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]     ):
						lowerCamelCase__		:		str		     =hf_param.shape

						lowerCamelCase__		:		Optional[Any]		     =to_torch(params[gluon_param]     )
						lowerCamelCase__		:		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

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

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

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

						lowerCamelCase__		:		Union[str, Any]		     =check_and_map_params(
						    self_attn.key.bias.data							,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''     )

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

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

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

						# intermediate
						lowerCamelCase__		:		BertIntermediate		     =layer.intermediate

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

						# output
						lowerCamelCase__		:		BertOutput		     =layer.output

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

			lowerCamelCase__		:		Dict		     =tokenizer.encode_plus(__lowerCamelCase     )['''input_ids''']

			# Get gluon output
			lowerCamelCase__		:		Dict		     =mx.nd.array([input_ids]     )
			lowerCamelCase__		:		Tuple		     =original_bort(inputs=__lowerCamelCase							,		token_types=[]     )

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		Optional[int]		     =BertModel.from_pretrained(__lowerCamelCase     )
			hf_bort_model.eval()

			lowerCamelCase__		:		Tuple		     =tokenizer.encode_plus(__lowerCamelCase							,		return_tensors='''pt'''     )
			lowerCamelCase__		:		int		     =hf_bort_model(**__lowerCamelCase     )[0]

			lowerCamelCase__		:		Tuple		     =output_gluon[0].asnumpy()
			lowerCamelCase__		:		Optional[int]		     =output_hf[0].detach().numpy()

			lowerCamelCase__		:		int		     =np.max(np.abs(hf_layer - gluon_layer     )     ).item()
			lowerCamelCase__		:		Dict		     =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__":
		_lowercase			:			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."
		)
		_lowercase			:			str										=						parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp






def 					snake_case__       (		__lowerCamelCase    :   jnp.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1							,		__lowerCamelCase    :   float = 1.0e4							,		__lowerCamelCase    :   bool = False							,		__lowerCamelCase    :   float = 1.0							,		):
			"""simple docstring"""
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
			lowerCamelCase__		:		Any		     =float(embedding_dim // 2     )
			lowerCamelCase__		:		List[str]		     =math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			lowerCamelCase__		:		int		     =min_timescale * jnp.exp(jnp.arange(__lowerCamelCase							,		dtype=jnp.floataa     ) * -log_timescale_increment     )
			lowerCamelCase__		:		Tuple		     =jnp.expand_dims(__lowerCamelCase							,		1     ) * jnp.expand_dims(__lowerCamelCase							,		0     )

			# scale embeddings
			lowerCamelCase__		:		List[str]		     =scale * emb

			if flip_sin_to_cos:
						lowerCamelCase__		:		int		     =jnp.concatenate([jnp.cos(__lowerCamelCase     ), jnp.sin(__lowerCamelCase     )]							,		axis=1     )
			else:
						lowerCamelCase__		:		List[str]		     =jnp.concatenate([jnp.sin(__lowerCamelCase     ), jnp.cos(__lowerCamelCase     )]							,		axis=1     )
			lowerCamelCase__		:		str		     =jnp.reshape(__lowerCamelCase							,		[jnp.shape(__lowerCamelCase     )[0], embedding_dim]     )
			return signal

class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							jnp.floataa



					@nn.compact
					def __call__(	self							:  Optional[Any],  lowerCamelCase							:  int					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_1'''					)(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =nn.silu(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =nn.Dense(self.time_embed_dim,  dtype=self.dtype,  name='''linear_2'''					)(lowerCamelCase					)
								return temb





class 	__SCREAMING_SNAKE_CASE     (				nn.Module       ):


					'''simple docstring'''





					_a             =							3_2
					_a             =							False
					_a             =							1



					@nn.compact
					def __call__(	self							:  Any,  lowerCamelCase							:  int					)-> int:
								return get_sinusoidal_embeddings(
								    lowerCamelCase,  embedding_dim=self.dim,  flip_sin_to_cos=self.flip_sin_to_cos,  freq_shift=self.freq_shift					)




