
'''simple docstring'''




import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=13						,			SCREAMING_SNAKE_CASE=7						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=99						,			SCREAMING_SNAKE_CASE=32						,			SCREAMING_SNAKE_CASE=5						,			SCREAMING_SNAKE_CASE=4						,			SCREAMING_SNAKE_CASE=4						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=4						,			SCREAMING_SNAKE_CASE=None						,			) ->				str:






         """simple docstring"""






         A       :   Optional[Any]					=						parent
         A       :   Optional[Any]					=						batch_size
         A       :   Union[str, Any]					=						seq_length
         A       :   List[Any]					=						is_training
         A       :   int					=						use_input_mask
         A       :   Optional[Any]					=						use_token_type_ids
         A       :   int					=						use_labels
         A       :   List[str]					=						vocab_size
         A       :   Optional[int]					=						hidden_size
         A       :   int					=						num_hidden_layers
         A       :   Tuple					=						num_attention_heads
         A       :   Optional[Any]					=						intermediate_multiple_size
         A       :   Optional[int]					=						hidden_act
         A       :   int					=						hidden_dropout
         A       :   Tuple					=						attention_dropout
         A       :   Optional[int]					=						weight_tying
         A       :   List[str]					=						max_position_embeddings
         A       :   Any					=						type_vocab_size
         A       :   Tuple					=						type_sequence_label_size
         A       :   Any					=						initializer_range
         A       :   List[Any]					=						num_labels
         A       :   Optional[Any]					=						num_choices
         A       :   str					=						scope





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   List[str]					=						ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size	)

         A       :   Union[str, Any]					=						None
         if self.use_input_mask:
             A       :   Optional[Any]					=						random_attention_mask([self.batch_size, self.seq_length]	)

         A       :   Optional[int]					=						None
         if self.use_labels:
             A       :   Optional[int]					=						ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels	)

         A       :   Any					=						self.get_config()

         return config, input_ids, input_mask, token_labels





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return GPTNeoXJapaneseConfig(
             vocab_size=self.vocab_size						,			hidden_size=self.hidden_size						,			num_hidden_layers=self.num_hidden_layers						,			num_attention_heads=self.num_attention_heads						,			intermediate_multiple_size=self.intermediate_multiple_size						,			hidden_act=self.hidden_act						,			hidden_dropout=self.hidden_dropout						,			attention_dropout=self.attention_dropout						,			weight_tying=self.weight_tying						,			max_position_embeddings=self.max_position_embeddings						,			type_vocab_size=self.type_vocab_size						,			is_decoder=SCREAMING_SNAKE_CASE						,			initializer_range=self.initializer_range						,			)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A,  A,  A,  A       :   Tuple					=						self.prepare_config_and_inputs()

         A       :   int					=						True

         return config, input_ids, input_mask, token_labels





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   str					=						GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   List[Any]					=						model(SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						model(SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   Any					=						True
         A       :   Tuple					=						GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Any					=						model(SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   Union[str, Any]					=						GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Dict					=						model(SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         A       :   List[str]					=						True
         A       :   Optional[int]					=						GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()

         # first forward pass
         A       :   List[str]					=						model(SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE						,			use_cache=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         A       :   Union[str, Any]					=						ids_tensor((self.batch_size, 3)						,			config.vocab_size	)
         A       :   Union[str, Any]					=						ids_tensor((self.batch_size, 3)						,			vocab_size=2	)

         # append to next input_ids and
         A       :   Any					=						torch.cat([input_ids, next_tokens]						,			dim=-1	)
         A       :   Union[str, Any]					=						torch.cat([input_mask, next_mask]						,			dim=-1	)

         A       :   int					=						model(SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE						,			output_hidden_states=SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						output_from_no_past['''hidden_states'''][0]
         A       :   Tuple					=						model(
             SCREAMING_SNAKE_CASE						,			attention_mask=SCREAMING_SNAKE_CASE						,			past_key_values=SCREAMING_SNAKE_CASE						,			output_hidden_states=SCREAMING_SNAKE_CASE						,			)['''hidden_states'''][0]

         # select random slice
         A       :   Dict					=						ids_tensor((1,)						,			output_from_past.shape[-1]	).item()
         A       :   int					=						output_from_no_past[:, -3:, random_slice_idx].detach()
         A       :   Union[str, Any]					=						output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			atol=1e-3	)	)





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   List[str]					=						self.prepare_config_and_inputs()
         A,  A,  A,  A       :   Optional[int]					=						config_and_inputs
         A       :   Optional[int]					=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict





@require_torch
class  A				(							__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
     __magic_name__						=		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
     __magic_name__						=		(
         {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
         if is_torch_available()
         else {}
     )
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   int					=						GPTNeoXJapaneseModelTester(self	)
         A       :   Optional[int]					=						ConfigTester(self						,			config_class=SCREAMING_SNAKE_CASE						,			hidden_size=37	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A,  A,  A,  A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A,  A,  A,  A       :   Optional[Any]					=						self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A,  A,  A,  A       :   Optional[Any]					=						self.model_tester.prepare_config_and_inputs_for_decoder()

         A       :   List[str]					=						None

         self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A,  A,  A,  A       :   Optional[Any]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						'''abeja/gpt-neox-japanese-2.7b'''

         A       :   Union[str, Any]					=						['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

         A       :   Any					=						[
             '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
             '''100年後に必要とされる会社は、「人」が中心の会社です。''',
             '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
             '''国境の長いトンネルを抜けると、そこは雪国だった。''',
             '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
         ]

         A       :   str					=						GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE	)

         A       :   str					=						[]
         for prompt in prompts:
             A       :   Any					=						tokenizer(SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).input_ids
             A       :   List[Any]					=						model.generate(SCREAMING_SNAKE_CASE						,			max_length=50	)
             A       :   int					=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE						,			skip_special_tokens=SCREAMING_SNAKE_CASE	)
             predicted_outputs += generated_string
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)











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						:   List[Any]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   Optional[Any]			  =     '''The Nymphenburg Palace is a beautiful palace in Munich!'''







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Optional[Any]:
					__A	:						Any       =				{
					    'attention_cell': 'multi_head',
					    'num_layers': 4,
					    'units': 10_24,
					    'hidden_size': 7_68,
					    'max_length': 5_12,
					    'num_heads': 8,
					    'scaled': True,
					    'dropout': 0.1,
					    'use_residual': True,
					    'embed_size': 10_24,
					    'embed_dropout': 0.1,
					    'word_embed': None,
					    'layer_norm_eps': 1e-5,
					    'token_type_vocab_size': 2,
					}

					__A	:						str       =				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
					__A	:						Optional[int]       =				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
					__A	:						Union[str, Any]       =				'openwebtext_ccnews_stories_books_cased'

					# Specify download folder to Gluonnlp's vocab
					__A	:						Any       =				os.path.join(get_home_dir()					,						'models'  )
					__A	:						List[Any]       =				_load_vocab(a					,						a					,						a					,						cls=a  )

					__A	:						Dict       =				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  )
					__A	:						Union[str, Any]       =				original_bort._collect_params_with_prefix()

					# Build our config 🤗
					__A	:						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  ),
					}

					__A	:						int       =				BertConfig.from_dict(a  )
					__A	:						Union[str, Any]       =				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  ):
										__A	:						Tuple       =				hf_param.shape

										__A	:						str       =				to_torch(params[gluon_param]  )
										__A	:						Union[str, Any]       =				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

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

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

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

										__A	:						Optional[Any]       =				check_and_map_params(
										    self_attn.key.bias.data					,						F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""  )

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

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

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

										# intermediate
										__A	:						BertIntermediate       =				layer.intermediate

										__A	:						int       =				check_and_map_params(
										    intermediate.dense.bias					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""  )
										__A	:						List[Any]       =				check_and_map_params(
										    intermediate.dense.weight					,						F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""  )

										# output
										__A	:						BertOutput       =				layer.output

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

					__A	:						List[str]       =				tokenizer.encode_plus(a  )['input_ids']

					# Get gluon output
					__A	:						List[str]       =				mx.nd.array([input_ids]  )
					__A	:						Union[str, Any]       =				original_bort(inputs=a					,						token_types=[]  )

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

					__A	:						Tuple       =				tokenizer.encode_plus(a					,						return_tensors='pt'  )
					__A	:						Any       =				hf_bort_model(**a  )[0]

					__A	:						Union[str, Any]       =				output_gluon[0].asnumpy()
					__A	:						Tuple       =				output_hf[0].detach().numpy()

					__A	:						int       =				np.max(np.abs(hf_layer - gluon_layer  )  ).item()
					__A	:						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__":
				UpperCAmelCase						:   int			  =     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						:   Dict			  =     parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




