


'''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()
_lowercase      :      Tuple            =					logging.get_logger(__name__)

_lowercase      :      str            =					"""The Nymphenburg Palace is a beautiful palace in Munich!"""


def 				lowerCamelCase__				(	A       :      str				,       A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     {
						    '''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,
						}

						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       :      Optional[int]					) -> 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       :      Tuple				,       A       :      Optional[int]					):
												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__":
	_lowercase      :      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."""
	)
	_lowercase      :      List[str]            =					parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''



from __future__ import annotations

from typing import Any






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			float = 0				)->					None:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     row, column
										UpperCAmelCase					    =     [[default_value for c in range(lowerCAmelCase				)] for r in range(lowerCAmelCase				)]




				def __str__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

										# Make string identifier
										UpperCAmelCase					    =     0
										for row_vector in self.array:
																for obj in row_vector:
																						UpperCAmelCase					    =     max(lowerCAmelCase				,   len(str(lowerCAmelCase				)				)				)
										UpperCAmelCase					    =     F"""%{max_element_length}s"""

										# Make string and return
										def single_line(lowerCAmelCase      :			list[float]				) -> str:
																nonlocal string_format_identifier
																UpperCAmelCase					    =     '''['''
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
																line += "]"
																return line

										s += "\n".join(single_line(lowerCAmelCase				) for row_vector in self.array				)
										return s




				def __repr__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										return str(self				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			tuple[int, int]				)->					bool:

										"""simple docstring"""




										if not (isinstance(lowerCAmelCase				,   (list, tuple)				) and len(lowerCAmelCase				) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True




				def __getitem__(		self      :			int				,   lowerCAmelCase      :			tuple[int, int]				)->					Any:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										return self.array[loc[0]][loc[1]]




				def __setitem__(		self      :			List[str]				,   lowerCAmelCase      :			tuple[int, int]				,   lowerCAmelCase      :			float				)->					None:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										UpperCAmelCase					    =     value




				def __add__(		self      :			int				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == another.row and self.column == another.column

										# Add
										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c] + another[r, c]
										return result




				def __neg__(		self      :			Dict				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     -self[r, c]
										return result




				def __sub__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										return self + (-another)




				def __mul__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			int | float | Matrix				)->					Matrix:

										"""simple docstring"""




										if isinstance(lowerCAmelCase				,   (int, float)				):  # Scalar multiplication
																UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
																for r in range(self.row				):
																						for c in range(self.column				):
																												UpperCAmelCase					    =     self[r, c] * another
																return result
										elif isinstance(lowerCAmelCase				,   lowerCAmelCase				):  # Matrix multiplication
																assert self.column == another.row
																UpperCAmelCase					    =     Matrix(self.row				,   another.column				)
																for r in range(self.row				):
																						for c in range(another.column				):
																												for i in range(self.column				):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																UpperCAmelCase					    =     F"""Unsupported type given for another ({type(lowerCAmelCase				)})"""
																raise TypeError(lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.column				,   self.row				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c]
										return result





				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Matrix				,   lowerCAmelCase      :			Matrix				)->					Any:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				) and isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										UpperCAmelCase					    =     v.transpose()
										UpperCAmelCase					    =     (v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							UpperCAmelCase					    =     Matrix(3				,       3				,       0					)
							for i in range(3					):
													UpperCAmelCase					    =     1
							print(f"""a^(-1) is {ainv}"""					)
							# u, v
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 2, -3
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     4, -2, 5
							print(f"""u is {u}"""					)
							print(f"""v is {v}"""					)
							print(f"""uv^T is {u * v.transpose()}"""					)
							# Sherman Morrison
							print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(A				,       A					)}"""					)


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							import doctest

							doctest.testmod()




	testa()





