


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       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    """The Nymphenburg Palace is a beautiful palace in Munich!"""




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	{
						    '''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,
						}

						__UpperCAmelCase			:							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
						__UpperCAmelCase			:							Tuple	    =	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
						__UpperCAmelCase			:							str	    =	'''openwebtext_ccnews_stories_books_cased'''

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

						__UpperCAmelCase			:							str	    =	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_      )
						__UpperCAmelCase			:							str	    =	original_bort._collect_params_with_prefix()

						# Build our config 🤗
						__UpperCAmelCase			:							int	    =	{
						    '''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''': 0.0_2,
						    '''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_      ),
						}

						__UpperCAmelCase			:							Optional[int]	    =	BertConfig.from_dict(lowercase_      )
						__UpperCAmelCase			:							Tuple	    =	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(lowercase_      ) -> 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(lowercase_      ,       lowercase_      ):
												__UpperCAmelCase			:							Union[str, Any]	    =	hf_param.shape

												__UpperCAmelCase			:							Tuple	    =	to_torch(params[gluon_param]      )
												__UpperCAmelCase			:							Dict	    =	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

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

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

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

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

												__UpperCAmelCase			:							List[str]	    =	check_and_map_params(
												    self_attn.key.weight.data      ,       f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"      )
												__UpperCAmelCase			:							Union[str, Any]	    =	check_and_map_params(
												    self_attn.query.bias.data      ,       f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"      )
												__UpperCAmelCase			:							Optional[Any]	    =	check_and_map_params(
												    self_attn.query.weight.data      ,       f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"      )
												__UpperCAmelCase			:							Tuple	    =	check_and_map_params(
												    self_attn.value.bias.data      ,       f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"      )
												__UpperCAmelCase			:							List[Any]	    =	check_and_map_params(
												    self_attn.value.weight.data      ,       f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"      )

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

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

												# intermediate
												__UpperCAmelCase			:							BertIntermediate	    =	layer.intermediate

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

												# output
												__UpperCAmelCase			:							BertOutput	    =	layer.output

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

						__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.encode_plus(lowercase_      )['''input_ids''']

						# Get gluon output
						__UpperCAmelCase			:							List[Any]	    =	mx.nd.array([input_ids]      )
						__UpperCAmelCase			:							List[Any]	    =	original_bort(inputs=lowercase_      ,       token_types=[]      )

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

						__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.encode_plus(lowercase_      ,       return_tensors='''pt'''      )
						__UpperCAmelCase			:							Any	    =	hf_bort_model(**lowercase_      )[0]

						__UpperCAmelCase			:							str	    =	output_gluon[0].asnumpy()
						__UpperCAmelCase			:							Any	    =	output_hf[0].detach().numpy()

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





import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

