"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_pytesseract_available():
			from PIL import Image

			from transformers import LayoutLMvaImageProcessor

class       a_      (     unittest.TestCase			):




					def __init__(	self						:     Optional[Any]  ,						snake_case__						:     List[str]  ,						snake_case__						:     Union[str, Any]=7  ,						snake_case__						:     List[str]=3  ,						snake_case__						:     Tuple=18  ,						snake_case__						:     Dict=30  ,						snake_case__						:     List[str]=400  ,						snake_case__						:     Union[str, Any]=True  ,						snake_case__						:     int=None  ,						snake_case__						:     int=True  ,						):
							lowerCAmelCase__        =   size if size is not None else {"""height""": 18, """width""": 18}
							lowerCAmelCase__        =   parent
							lowerCAmelCase__        =   batch_size
							lowerCAmelCase__        =   num_channels
							lowerCAmelCase__        =   image_size
							lowerCAmelCase__        =   min_resolution
							lowerCAmelCase__        =   max_resolution
							lowerCAmelCase__        =   do_resize
							lowerCAmelCase__        =   size
							lowerCAmelCase__        =   apply_ocr




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}

@require_torch
@require_pytesseract
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	List[Any]			=    LayoutLMvaImageProcessor if is_pytesseract_available() else None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   LayoutLMvaImageProcessingTester(self     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_resize"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""size"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""apply_ocr"""     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   self.image_processing_class.from_dict(self.image_processor_dict     )
							self.assertEqual(image_processor.size  ,						{"""height""": 18, """width""": 18}     )

							lowerCAmelCase__        =   self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42     )
							self.assertEqual(image_processor.size  ,						{"""height""": 42, """width""": 42}     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							# Initialize image_processing
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							# create random PIL images
							lowerCAmelCase__        =   prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						Image.Image     )

							# Test not batched input
							lowerCAmelCase__        =   image_processing(image_inputs[0]  ,						return_tensors="""pt"""     )
							self.assertEqual(
							    encoding.pixel_values.shape  ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)

							self.assertIsInstance(encoding.words  ,						snake_case__     )
							self.assertIsInstance(encoding.boxes  ,						snake_case__     )

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							# Initialize image_processing
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							# create random numpy tensors
							lowerCAmelCase__        =   prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=snake_case__  ,						numpify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						np.ndarray     )

							# Test not batched input
							lowerCAmelCase__        =   image_processing(image_inputs[0]  ,						return_tensors="""pt"""     ).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							# Initialize image_processing
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							# create random PyTorch tensors
							lowerCAmelCase__        =   prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=snake_case__  ,						torchify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						torch.Tensor     )

							# Test not batched input
							lowerCAmelCase__        =   image_processing(image_inputs[0]  ,						return_tensors="""pt"""     ).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,						(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							# with apply_OCR = True
							lowerCAmelCase__        =   LayoutLMvaImageProcessor()

							from datasets import load_dataset

							lowerCAmelCase__        =   load_dataset("""hf-internal-testing/fixtures_docvqa"""  ,						split="""test"""     )

							lowerCAmelCase__        =   Image.open(ds[0]["""file"""]     ).convert("""RGB"""     )

							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     )

							self.assertEqual(encoding.pixel_values.shape  ,						(1, 3, 224, 224)     )
							self.assertEqual(len(encoding.words     )  ,						len(encoding.boxes     )     )

							# fmt: off
							# the words and boxes were obtained with Tesseract 4.1.1
							lowerCAmelCase__        =   [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
							lowerCAmelCase__        =   [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
							# fmt: on

							self.assertListEqual(encoding.words  ,						snake_case__     )
							self.assertListEqual(encoding.boxes  ,						snake_case__     )

							# with apply_OCR = False
							lowerCAmelCase__        =   LayoutLMvaImageProcessor(apply_ocr=snake_case__     )

							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     )

							self.assertEqual(encoding.pixel_values.shape  ,						(1, 3, 224, 224)     )





"""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)




