
"""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       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str     ):
	A						   =   {
	    '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,
	}

	A						   =   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						   =   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=snake_case__	,      output_all_encodings=snake_case__	,      use_residual=predefined_args['use_residual']	,      activation=predefined_args.get('activation'	,      'gelu'     )	,      layer_norm_eps=predefined_args.get('layer_norm_eps'	,      snake_case__     )	,      )

	# Vocab information needs to be fetched first
	# It's the same as RoBERTa, so RobertaTokenizer can be used later
	A						   =   'openwebtext_ccnews_stories_books_cased'

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

	A						   =   nlp.model.BERTModel(
	    snake_case__	,      len(snake_case__     )	,      units=predefined_args['units']	,      embed_size=predefined_args['embed_size']	,      embed_dropout=predefined_args['embed_dropout']	,      word_embed=predefined_args['word_embed']	,      use_pooler=snake_case__	,      use_token_type_embed=snake_case__	,      token_type_vocab_size=predefined_args['token_type_vocab_size']	,      use_classifier=snake_case__	,      use_decoder=snake_case__	,      )

	original_bort.load_parameters(snake_case__	,      cast_dtype=snake_case__	,      ignore_extra=snake_case__     )
	A						   =   original_bort._collect_params_with_prefix()

	# Build our config 🤗
	A						   =   {
	    '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(snake_case__     ),
	}

	A						   =   BertConfig.from_dict(snake_case__     )
	A						   =   BertForMaskedLM(snake_case__     )
	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(snake_case__   :     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(snake_case__   :     Tuple	,      snake_case__   :     Union[str, Any]     ):
		A						   =   hf_param.shape

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

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

		# self attention
		A						   =   layer.attention.self

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

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

		# self attention output
		A						   =   layer.attention.output

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

		# intermediate
		A						   =   layer.intermediate

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

		# output
		A						   =   layer.output

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

	A						   =   tokenizer.encode_plus(snake_case__     )['input_ids']

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

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

	A						   =   tokenizer.encode_plus(snake_case__	,      return_tensors='pt'     )
	A						   =   hf_bort_model(**snake_case__     )[0]

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

	A						   =   np.max(np.abs(hf_layer - gluon_layer     )     ).item()
	A						   =   np.allclose(snake_case__	,      snake_case__	,      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:'	,      snake_case__     )


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)

"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('download'      )
							download_parser.add_argument(
							    '--cache-dir'  ,type=A_  ,default=A_  ,help='Path to location to store the models'      )
							download_parser.add_argument(
							    '--force'  ,action='store_true'  ,help='Force the model to be download even if already in cache-dir'      )
							download_parser.add_argument(
							    '--trust-remote-code'  ,action='store_true'  ,help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'  ,)
							download_parser.add_argument('model'  ,type=A_  ,help='Name of the model to download'      )
							download_parser.set_defaults(func=A_      )


						def __init__( self     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							from ..models.auto import AutoModel, AutoTokenizer

							AutoModel.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )
							AutoTokenizer.from_pretrained(
							    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code      )