





'''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_					= logging.get_logger(__name__)

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





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Optional[int]    )						->Optional[Any]:
    _SCREAMING_SNAKE_CASE									=			{
        '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,
    }

    _SCREAMING_SNAKE_CASE									=			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
    _SCREAMING_SNAKE_CASE									=			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
    _SCREAMING_SNAKE_CASE									=			'openwebtext_ccnews_stories_books_cased'

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

    _SCREAMING_SNAKE_CASE									=			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    )
    _SCREAMING_SNAKE_CASE									=			original_bort._collect_params_with_prefix()

    # Build our config 🤗
    _SCREAMING_SNAKE_CASE									=			{
        '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    ),
    }

    _SCREAMING_SNAKE_CASE									=			BertConfig.from_dict(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			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 :    Union[str, Any]    ) -> 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 :    Optional[Any]					,       __lowerCamelCase :    str    ):
        _SCREAMING_SNAKE_CASE									=			hf_param.shape

        _SCREAMING_SNAKE_CASE									=			to_torch(params[gluon_param]    )
        _SCREAMING_SNAKE_CASE									=			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

    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight					,       """word_embed.0.weight"""    )
    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight					,       """encoder.position_weight"""    )
    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias					,       """encoder.layer_norm.beta"""    )
    _SCREAMING_SNAKE_CASE									=			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)
    _SCREAMING_SNAKE_CASE									=			torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data    )

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

        # self attention
        _SCREAMING_SNAKE_CASE									=			layer.attention.self

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

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

        # self attention output
        _SCREAMING_SNAKE_CASE									=			layer.attention.output

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

        # intermediate
        _SCREAMING_SNAKE_CASE									=			layer.intermediate

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

        # output
        _SCREAMING_SNAKE_CASE									=			layer.output

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

    _SCREAMING_SNAKE_CASE									=			tokenizer.encode_plus(__lowerCamelCase    )['input_ids']

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

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

    _SCREAMING_SNAKE_CASE									=			tokenizer.encode_plus(__lowerCamelCase					,       return_tensors="""pt"""    )
    _SCREAMING_SNAKE_CASE									=			hf_bort_model(**__lowerCamelCase    )[0]

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

    _SCREAMING_SNAKE_CASE									=			np.max(np.abs(hf_layer - gluon_layer    )    ).item()
    _SCREAMING_SNAKE_CASE									=			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__":
      lowercase_					= 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_					= parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					(float, int)     ):
														__A	:						str       =				[c * other for c in self.__components]
														return Vector(_A     )
									elif isinstance(_A     ,					_A     ) and len(self     ) == len(_A     ):
														__A	:						Union[str, Any]       =				len(self     )
														__A	:						Dict       =				[self.__components[i] * other.component(_A     ) for i in range(_A     )]
														return sum(_A     )
									else:  # error case
														raise Exception('invalid operand!'     )



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ) and -len(self.__components     ) <= i < len(self.__components     ):
														return self.__components[i]
									else:
														raise Exception('index out of range'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



				def 					UpperCAmelCase_		(			self     ):
									if len(self.__components     ) == 0:
														raise Exception('Vector is empty'     )
									__A	:						Optional[Any]       =				[c**2 for c in self.__components]
									return math.sqrt(sum(_A     )     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



				def __str__(			self     ):
									__A	:						Tuple       =				''
									for i in range(self.__height     ):
														ans += "|"
														for j in range(self.__width     ):
																			if j < self.__width - 1:
																								ans += str(self.__matrix[i][j]     ) + ","
																			else:
																								ans += str(self.__matrix[i][j]     ) + "|\n"
									return ans



				def __add__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Optional[Any]       =				[]
														for i in range(self.__height     ):
																			__A	:						Optional[Any]       =				[
																			    self.__matrix[i][j] + other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrix must have the same dimension!'     )



				def __sub__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Tuple       =				[]
														for i in range(self.__height     ):
																			__A	:						str       =				[
																			    self.__matrix[i][j] - other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrices must have the same dimension!'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):  # matrix-vector
														if len(_A     ) == self.__width:
																			__A	:						List[Any]       =				zero_vector(self.__height     )
																			for i in range(self.__height     ):
																								__A	:						List[str]       =				[
																								    self.__matrix[i][j] * other.component(_A     )
																								    for j in range(self.__width     )
																								]
																								ans.change_component(_A     ,					sum(_A     )     )
																			return ans
														else:
																			raise Exception(
																			    'vector must have the same size as the '
																			    'number of columns of the matrix!'     )
									elif isinstance(_A     ,					(int, float)     ):  # matrix-scalar
														__A	:						List[str]       =				[
														    [self.__matrix[i][j] * other for j in range(self.__width     )]
														    for i in range(self.__height     )
														]
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									return None



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														__A	:						int       =				value
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if self.__height < 1:
														raise Exception('Matrix has no element'     )
									elif self.__height == 1:
														return self.__matrix[0][0]
									elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
									else:
														__A	:						List[str]       =				[
														    self.__matrix[0][y] * self.cofactor(0     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




