
'''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()
__A								=			logging.get_logger(__name__)

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





def        _A						(							lowercase__						,						lowercase__		):
						lowercase__							       =				{
						    "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__							       =				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__							       =				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=SCREAMING_SNAKE_CASE_						,						output_all_encodings=SCREAMING_SNAKE_CASE_						,						use_residual=predefined_args["""use_residual"""]						,						activation=predefined_args.get("""activation"""						,						"""gelu"""		)						,						layer_norm_eps=predefined_args.get("""layer_norm_eps"""						,						SCREAMING_SNAKE_CASE_		)						,						)

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

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

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

						original_bort.load_parameters(SCREAMING_SNAKE_CASE_						,						cast_dtype=SCREAMING_SNAKE_CASE_						,						ignore_extra=SCREAMING_SNAKE_CASE_		)
						lowercase__							       =				original_bort._collect_params_with_prefix()

						# Build our config 🤗
						lowercase__							       =				{
						    "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(SCREAMING_SNAKE_CASE_		),
						}

						lowercase__							       =				BertConfig.from_dict(SCREAMING_SNAKE_CASE_		)
						lowercase__							       =				BertForMaskedLM(SCREAMING_SNAKE_CASE_		)
						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__		):
												lowercase__							       =				hf_param.shape

												lowercase__							       =				to_torch(params[gluon_param]		)
												lowercase__							       =				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__							       =				check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight						,						"""word_embed.0.weight"""		)
						lowercase__							       =				check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight						,						"""encoder.position_weight"""		)
						lowercase__							       =				check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias						,						"""encoder.layer_norm.beta"""		)
						lowercase__							       =				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__							       =				torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

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

												# self attention
												lowercase__							       =				layer.attention.self

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

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

												# self attention output
												lowercase__							       =				layer.attention.output

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

												# intermediate
												lowercase__							       =				layer.intermediate

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

												# output
												lowercase__							       =				layer.output

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

						lowercase__							       =				tokenizer.encode_plus(SCREAMING_SNAKE_CASE_		)["input_ids"]

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

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

						lowercase__							       =				tokenizer.encode_plus(SCREAMING_SNAKE_CASE_						,						return_tensors="""pt"""		)
						lowercase__							       =				hf_bort_model(**SCREAMING_SNAKE_CASE_		)[0]

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

						lowercase__							       =				np.max(np.abs(hf_layer - gluon_layer		)		).item()
						lowercase__							       =				np.allclose(SCREAMING_SNAKE_CASE_						,						SCREAMING_SNAKE_CASE_						,						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:"""						,						SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				__A								=			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								=			parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



