





"""simple docstring"""


import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_	: str      				="""▁"""

A_	: str      				=get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
class       __a  (    lowerCAmelCase__      ,		unittest.TestCase						):
     SCREAMING_SNAKE_CASE__					: Optional[int]									=     BertGenerationTokenizer
     SCREAMING_SNAKE_CASE__					: Optional[int]									=     False
     SCREAMING_SNAKE_CASE__					: List[str]									=     True



     def 		snake_case_     (			self  ):
            super().setUp()

            _lowerCamelCase             =   BertGenerationTokenizer(A_   ,	keep_accents=A_  )
            tokenizer.save_pretrained(self.tmpdirname  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   '<s>'
            _lowerCamelCase             =   1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_  )   ,	A_  )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_  )   ,	A_  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   list(self.get_tokenizer().get_vocab().keys()  )

            self.assertEqual(vocab_keys[0]   ,	'<unk>'  )
            self.assertEqual(vocab_keys[1]   ,	'<s>'  )
            self.assertEqual(vocab_keys[-1]   ,	'<pad>'  )
            self.assertEqual(len(A_  )   ,	10_02  )



     def 		snake_case_     (			self  ):
            self.assertEqual(self.get_tokenizer().vocab_size   ,	10_00  )



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   BertGenerationTokenizer(A_   ,	keep_accents=A_  )

            _lowerCamelCase             =   tokenizer.tokenize('This is a test'  )
            self.assertListEqual(A_   ,	['▁This', '▁is', '▁a', '▁t', 'est']  )

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(A_  )   ,	[2_85, 46, 10, 1_70, 3_82]   ,	)

            _lowerCamelCase             =   tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
            self.assertListEqual(
                A_   ,	[
                    SPIECE_UNDERLINE + 'I',
                    SPIECE_UNDERLINE + 'was',
                    SPIECE_UNDERLINE + 'b',
                    'or',
                    'n',
                    SPIECE_UNDERLINE + 'in',
                    SPIECE_UNDERLINE + '',
                    '9',
                    '2',
                    '0',
                    '0',
                    '0',
                    ',',
                    SPIECE_UNDERLINE + 'and',
                    SPIECE_UNDERLINE + 'this',
                    SPIECE_UNDERLINE + 'is',
                    SPIECE_UNDERLINE + 'f',
                    'al',
                    's',
                    'é',
                    '.',
                ]   ,	)
            _lowerCamelCase             =   tokenizer.convert_tokens_to_ids(A_  )
            self.assertListEqual(
                A_   ,	[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]   ,	)

            _lowerCamelCase             =   tokenizer.convert_ids_to_tokens(A_  )
            self.assertListEqual(
                A_   ,	[
                    SPIECE_UNDERLINE + 'I',
                    SPIECE_UNDERLINE + 'was',
                    SPIECE_UNDERLINE + 'b',
                    'or',
                    'n',
                    SPIECE_UNDERLINE + 'in',
                    SPIECE_UNDERLINE + '',
                    '<unk>',
                    '2',
                    '0',
                    '0',
                    '0',
                    ',',
                    SPIECE_UNDERLINE + 'and',
                    SPIECE_UNDERLINE + 'this',
                    SPIECE_UNDERLINE + 'is',
                    SPIECE_UNDERLINE + 'f',
                    'al',
                    's',
                    '<unk>',
                    '.',
                ]   ,	)



     @cached_property
     def 		snake_case_     (			self  ):
            return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'  )



     @slow
     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   'Hello World!'
            _lowerCamelCase             =   [1_85_36, 22_60, 1_01]

            self.assertListEqual(A_   ,	self.big_tokenizer.encode(A_  )  )



     @slow
     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   (
                'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'
                ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
            )
            _lowerCamelCase             =   [
                8_71,
                4_19,
                3_58,
                9_46,
                9_91,
                25_21,
                4_52,
                3_58,
                13_57,
                3_87,
                77_51,
                35_36,
                1_12,
                9_85,
                4_56,
                1_26,
                8_65,
                9_38,
                54_00,
                57_34,
                4_58,
                13_68,
                4_67,
                7_86,
                24_62,
                52_46,
                11_59,
                6_33,
                8_65,
                45_19,
                4_57,
                5_82,
                8_52,
                25_57,
                4_27,
                9_16,
                5_08,
                4_05,
                3_43_24,
                4_97,
                3_91,
                4_08,
                1_13_42,
                12_44,
                3_85,
                1_00,
                9_38,
                9_85,
                4_56,
                5_74,
                3_62,
                1_25_97,
                32_00,
                31_29,
                11_72,
            ]

            self.assertListEqual(A_   ,	self.big_tokenizer.encode(A_  )  )



     @require_torch
     @slow
     def 		snake_case_     (			self  ):
            import torch

            from transformers import BertGenerationConfig, BertGenerationEncoder

            # Build sequence
            _lowerCamelCase             =   list(self.big_tokenizer.get_vocab().keys()  )[:10]
            _lowerCamelCase             =   ' '.join(A_  )
            _lowerCamelCase             =   self.big_tokenizer.encode_plus(A_   ,	return_tensors='pt'   ,	return_token_type_ids=A_  )
            _lowerCamelCase             =   self.big_tokenizer.batch_encode_plus(
                [sequence + ' ' + sequence]   ,	return_tensors='pt'   ,	return_token_type_ids=A_  )

            _lowerCamelCase             =   BertGenerationConfig()
            _lowerCamelCase             =   BertGenerationEncoder(A_  )

            assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

            with torch.no_grad():
                   model(**A_  )
                   model(**A_  )



     @slow
     def 		snake_case_     (			self  ):
            # fmt: off
            _lowerCamelCase             =   {'input_ids': [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=A_   ,	model_name='google/bert_for_seq_generation_L-24_bbc_encoder'   ,	revision='c817d1fd1be2ffa69431227a1fe320544943d4db'   ,	)













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()
UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	'The Nymphenburg Palace is a beautiful palace in Munich!'






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       "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__						=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__						=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
   UpperCAmelCase__						="openwebtext_ccnews_stories_books_cased"

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

   UpperCAmelCase__						=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     )
   UpperCAmelCase__						=original_bort._collect_params_with_prefix()

   # Build our config 🤗
   UpperCAmelCase__						={
       "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     ),
   }

   UpperCAmelCase__						=BertConfig.from_dict(A     )
   UpperCAmelCase__						=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     ) -> 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				,							A     ):
      UpperCAmelCase__						=hf_param.shape

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

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

      # self attention
      UpperCAmelCase__						=layer.attention.self

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

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

      # self attention output
      UpperCAmelCase__						=layer.attention.output

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

      # intermediate
      UpperCAmelCase__						=layer.intermediate

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

      # output
      UpperCAmelCase__						=layer.output

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

   UpperCAmelCase__						=tokenizer.encode_plus(A     )["input_ids"]

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

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

   UpperCAmelCase__						=tokenizer.encode_plus(A				,							return_tensors="pt"     )
   UpperCAmelCase__						=hf_bort_model(**A     )[0]

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

   UpperCAmelCase__						=np.max(np.abs(hf_layer - gluon_layer     )     ).item()
   UpperCAmelCase__						=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__":
    UpperCamelCase_												=	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.'
    )
    UpperCamelCase_												=	parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)



