




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   :						str				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict				=			"""The Nymphenburg Palace is a beautiful palace in Munich!"""







def 		lowercase__   (		__A:	Optional[int]							,__A:	int				):





     '''simple docstring'''






     __magic_name__							:	Union[str, Any]    							=							{
         "attention_cell": "multi_head",
         "num_layers": 4,
         "units": 1_0_2_4,
         "hidden_size": 7_6_8,
         "max_length": 5_1_2,
         "num_heads": 8,
         "scaled": True,
         "dropout": 0.1,
         "use_residual": True,
         "embed_size": 1_0_2_4,
         "embed_dropout": 0.1,
         "word_embed": None,
         "layer_norm_eps": 1e-5,
         "token_type_vocab_size": 2,
     }

     __magic_name__							:	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
     __magic_name__							:	List[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=__A							,output_all_encodings=__A							,use_residual=predefined_args['''use_residual''']							,activation=predefined_args.get('''activation'''							,'''gelu'''				)							,layer_norm_eps=predefined_args.get('''layer_norm_eps'''							,__A				)							,)

     # Vocab information needs to be fetched first
     # It's the same as RoBERTa, so RobertaTokenizer can be used later
     __magic_name__							:	List[str]    							=							"openwebtext_ccnews_stories_books_cased"

     # Specify download folder to Gluonnlp's vocab
     __magic_name__							:	Tuple    							=							os.path.join(get_home_dir()							,'''models'''				)
     __magic_name__							:	Tuple    							=							_load_vocab(__A							,__A							,__A							,cls=__A				)

     __magic_name__							:	Any    							=							nlp.model.BERTModel(
         __A							,len(__A				)							,units=predefined_args['''units''']							,embed_size=predefined_args['''embed_size''']							,embed_dropout=predefined_args['''embed_dropout''']							,word_embed=predefined_args['''word_embed''']							,use_pooler=__A							,use_token_type_embed=__A							,token_type_vocab_size=predefined_args['''token_type_vocab_size''']							,use_classifier=__A							,use_decoder=__A							,)

     original_bort.load_parameters(__A							,cast_dtype=__A							,ignore_extra=__A				)
     __magic_name__							:	str    							=							original_bort._collect_params_with_prefix()

     # Build our config 🤗
     __magic_name__							:	Union[str, Any]    							=							{
         "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(__A				),
     }

     __magic_name__							:	str    							=							BertConfig.from_dict(__A				)
     __magic_name__							:	List[str]    							=							BertForMaskedLM(__A				)
     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(__A:	Optional[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(__A:	Any							,__A:	Union[str, Any]				):
          __magic_name__							:	Optional[int]    							=							hf_param.shape

          __magic_name__							:	int    							=							to_torch(params[gluon_param]				)
          __magic_name__							:	List[str]    							=							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

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

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

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

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

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

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

          __magic_name__							:	int    							=							check_and_map_params(
              self_output.dense.bias							,F'''encoder.transformer_cells.{i}.proj.bias'''				)
          __magic_name__							:	Union[str, Any]    							=							check_and_map_params(
              self_output.dense.weight							,F'''encoder.transformer_cells.{i}.proj.weight'''				)
          __magic_name__							:	Optional[Any]    							=							check_and_map_params(
              self_output.LayerNorm.bias							,F'''encoder.transformer_cells.{i}.layer_norm.beta'''				)
          __magic_name__							:	Tuple    							=							check_and_map_params(
              self_output.LayerNorm.weight							,F'''encoder.transformer_cells.{i}.layer_norm.gamma'''				)

          # intermediate
          __magic_name__							:	BertIntermediate    							=							layer.intermediate

          __magic_name__							:	Union[str, Any]    							=							check_and_map_params(
              intermediate.dense.bias							,F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''				)
          __magic_name__							:	List[Any]    							=							check_and_map_params(
              intermediate.dense.weight							,F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''				)

          # output
          __magic_name__							:	BertOutput    							=							layer.output

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

     __magic_name__							:	Tuple    							=							tokenizer.encode_plus(__A				)["input_ids"]

     # Get gluon output
     __magic_name__							:	Optional[Any]    							=							mx.nd.array([input_ids]				)
     __magic_name__							:	Any    							=							original_bort(inputs=__A							,token_types=[]				)

     # Get Transformer output (save and reload model again)
     hf_bort_model.save_pretrained(__A				)
     __magic_name__							:	List[Any]    							=							BertModel.from_pretrained(__A				)
     hf_bort_model.eval()

     __magic_name__							:	Optional[int]    							=							tokenizer.encode_plus(__A							,return_tensors='''pt'''				)
     __magic_name__							:	Tuple    							=							hf_bort_model(**__A				)[0]

     __magic_name__							:	Tuple    							=							output_gluon[0].asnumpy()
     __magic_name__							:	Optional[Any]    							=							output_hf[0].detach().numpy()

     __magic_name__							:	Tuple    							=							np.max(np.abs(hf_layer - gluon_layer				)				).item()
     __magic_name__							:	Optional[int]    							=							np.allclose(__A							,__A							,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:'''							,__A				)


if __name__ == "__main__":
 __lowerCamelCase   :						Tuple				=			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   :						Optional[Any]				=			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.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



