"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase    :							Tuple													=						get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

__lowerCAmelCase    :							Tuple													=						25_00_04
__lowerCAmelCase    :							Any													=						25_00_20

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Dict			=    MBartTokenizer
					UpperCamelCase_ :	Optional[int]			=    MBartTokenizerFast
					UpperCamelCase_ :	Tuple			=    True
					UpperCamelCase_ :	Tuple			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   MBartTokenizer(snake_case__  ,						keep_accents=snake_case__     )
							tokenizer.save_pretrained(self.tmpdirname     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   MBartTokenizer(snake_case__  ,						keep_accents=snake_case__     )

							lowerCAmelCase__        =   tokenizer.tokenize("""This is a test"""     )
							self.assertListEqual(snake_case__  ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]     )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,						)

							lowerCAmelCase__        =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
							self.assertListEqual(
							    snake_case__  ,						[
							        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(snake_case__     )
							self.assertListEqual(
							    snake_case__  ,						[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]  ,						)

							lowerCAmelCase__        =   tokenizer.convert_ids_to_tokens(snake_case__     )
							self.assertListEqual(
							    snake_case__  ,						[
							        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>""",
							        """.""",
							    ]  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

							lowerCAmelCase__        =   (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
											lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(snake_case__  ,						**snake_case__     )
											lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(snake_case__  ,						**snake_case__     )

											lowerCAmelCase__        =   tempfile.mkdtemp()

											lowerCAmelCase__        =   tokenizer_r.save_pretrained(snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.save_pretrained(snake_case__     )

											# Checks it save with the same files + the tokenizer.json file for the fast one
											self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files     )     )
											lowerCAmelCase__        =   tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f     )
											self.assertSequenceEqual(snake_case__  ,						snake_case__     )

											# Checks everything loads correctly in the same way
											lowerCAmelCase__        =   tokenizer_r.from_pretrained(snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.from_pretrained(snake_case__     )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(snake_case__  ,						snake_case__     )     )
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

											shutil.rmtree(snake_case__     )

											# Save tokenizer rust, legacy_format=True
											lowerCAmelCase__        =   tempfile.mkdtemp()

											lowerCAmelCase__        =   tokenizer_r.save_pretrained(snake_case__  ,						legacy_format=snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.save_pretrained(snake_case__     )

											# Checks it save with the same files
											self.assertSequenceEqual(snake_case__  ,						snake_case__     )

											# Checks everything loads correctly in the same way
											lowerCAmelCase__        =   tokenizer_r.from_pretrained(snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.from_pretrained(snake_case__     )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(snake_case__  ,						snake_case__     )     )

											shutil.rmtree(snake_case__     )

											# Save tokenizer rust, legacy_format=False
											lowerCAmelCase__        =   tempfile.mkdtemp()

											lowerCAmelCase__        =   tokenizer_r.save_pretrained(snake_case__  ,						legacy_format=snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.save_pretrained(snake_case__     )

											# Checks it saved the tokenizer.json file
											self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files     )     )

											# Checks everything loads correctly in the same way
											lowerCAmelCase__        =   tokenizer_r.from_pretrained(snake_case__     )
											lowerCAmelCase__        =   tokenizer_p.from_pretrained(snake_case__     )

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(snake_case__  ,						snake_case__     )     )

											shutil.rmtree(snake_case__     )

@require_torch
@require_sentencepiece
@require_tokenizers
class       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	Any			=    "facebook/mbart-large-en-ro"
					UpperCamelCase_ :	str			=    [
					    " UN Chief Says There Is No Military Solution in Syria",
					    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
					]
					UpperCamelCase_ :	Optional[Any]			=    [
					    "Şeful ONU declară că nu există o soluţie militară în Siria",
					    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
					    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
					    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
					]
					UpperCamelCase_ :	Optional[Any]			=    [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Optional[Any]     ):
							lowerCAmelCase__        =   MBartTokenizer.from_pretrained(
							    cls.checkpoint_name  ,						src_lang="""en_XX"""  ,						tgt_lang="""ro_RO"""     )
							lowerCAmelCase__        =   1
							return cls




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]  ,						250001     )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]  ,						250004     )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]  ,						250020     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
							self.assertListEqual(self.expected_src_tokens  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							self.assertIn(snake_case__  ,						self.tokenizer.all_special_ids     )
							lowerCAmelCase__        =   [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
							lowerCAmelCase__        =   self.tokenizer.decode(snake_case__  ,						skip_special_tokens=snake_case__     )
							lowerCAmelCase__        =   self.tokenizer.decode(generated_ids[1:]  ,						skip_special_tokens=snake_case__     )
							self.assertEqual(snake_case__  ,						snake_case__     )
							self.assertNotIn(self.tokenizer.eos_token  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   ["""this is gunna be a long sentence """ * 20]
							assert isinstance(src_text[0]  ,						snake_case__     )
							lowerCAmelCase__        =   10
							lowerCAmelCase__        =   self.tokenizer(snake_case__  ,						max_length=snake_case__  ,						truncation=snake_case__     ).input_ids[0]
							self.assertEqual(ids[-2]  ,						2     )
							self.assertEqual(ids[-1]  ,						snake_case__     )
							self.assertEqual(len(snake_case__     )  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]     )  ,						[250026, 250001]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   tempfile.mkdtemp()
							lowerCAmelCase__        =   self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(snake_case__     )
							lowerCAmelCase__        =   MBartTokenizer.from_pretrained(snake_case__     )
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,						snake_case__     )




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.tokenizer(self.src_text  ,						text_target=self.tgt_text  ,						padding=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   shift_tokens_right(batch["""labels"""]  ,						self.tokenizer.pad_token_id     )

							# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
							assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
							assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
							assert batch.decoder_input_ids[1][-1] == 2
							assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   self.tokenizer(
							    self.src_text  ,						text_target=self.tgt_text  ,						padding=snake_case__  ,						truncation=snake_case__  ,						max_length=len(self.expected_src_tokens     )  ,						return_tensors="""pt"""  ,						)

							lowerCAmelCase__        =   shift_tokens_right(batch["""labels"""]  ,						self.tokenizer.pad_token_id     )

							self.assertIsInstance(snake_case__  ,						snake_case__     )

							self.assertEqual((2, 14)  ,						batch.input_ids.shape     )
							self.assertEqual((2, 14)  ,						batch.attention_mask.shape     )
							lowerCAmelCase__        =   batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens  ,						snake_case__     )
							self.assertEqual(2  ,						batch.decoder_input_ids[0, -1]     )  # EOS
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens  ,						[]     )
							self.assertEqual(self.tokenizer.suffix_tokens  ,						[self.tokenizer.eos_token_id, EN_CODE]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.tokenizer(self.src_text  ,						padding=snake_case__  ,						truncation=snake_case__  ,						max_length=3  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self.tokenizer(
							    text_target=self.tgt_text  ,						padding=snake_case__  ,						truncation=snake_case__  ,						max_length=10  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   targets["""input_ids"""]
							lowerCAmelCase__        =   shift_tokens_right(snake_case__  ,						self.tokenizer.pad_token_id     )

							self.assertEqual(batch.input_ids.shape[1]  ,						3     )
							self.assertEqual(batch.decoder_input_ids.shape[1]  ,						10     )




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   self.tokenizer._build_translation_inputs(
							    """A test"""  ,						return_tensors="""pt"""  ,						src_lang="""en_XX"""  ,						tgt_lang="""ar_AR"""     )

							self.assertEqual(
							    nested_simplify(snake_case__     )  ,						{
							        # A, test, EOS, en_XX
							        """input_ids""": [[62, 3034, 2, 250004]],
							        """attention_mask""": [[1, 1, 1, 1]],
							        # ar_AR
							        """forced_bos_token_id""": 250001,
							    }  ,						)





"""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()
__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Any													=						"The Nymphenburg Palace is a beautiful palace in Munich!"







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {
		    """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__        =   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__        =   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__        =   """openwebtext_ccnews_stories_books_cased"""

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

		lowerCAmelCase__        =   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__        =   original_bort._collect_params_with_prefix()

		# Build our config 🤗
		lowerCAmelCase__        =   {
		    """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__        =   BertConfig.from_dict(lowerCamelCase__     )
		lowerCAmelCase__        =   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__     ) -> 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__			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   hf_param.shape

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

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

				# self attention
				lowerCAmelCase__        =   layer.attention.self

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

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

				# self attention output
				lowerCAmelCase__        =   layer.attention.output

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

				# intermediate
				lowerCAmelCase__        =   layer.intermediate

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

				# output
				lowerCAmelCase__        =   layer.output

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

		lowerCAmelCase__        =   tokenizer.encode_plus(lowerCamelCase__     )["""input_ids"""]

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

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

		lowerCAmelCase__        =   tokenizer.encode_plus(lowerCamelCase__			,  return_tensors="""pt"""     )
		lowerCAmelCase__        =   hf_bort_model(**lowerCamelCase__     )[0]

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

		lowerCAmelCase__        =   np.max(np.abs(hf_layer - gluon_layer     )     ).item()
		lowerCAmelCase__        =   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__":
			__lowerCAmelCase    :							Union[str, Any]													=						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    :							str													=						parser.parse_args()
			convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




