





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()
a__: Any							   =						logging.get_logger(__name__)

a__: Dict							   =						'The Nymphenburg Palace is a beautiful palace in Munich!'


def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str    )->Optional[int]:
       A__		   =		{
           '''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__		   =		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=UpperCamelCase__					,	output_all_encodings=UpperCamelCase__					,	use_residual=predefined_args['''use_residual''']					,	activation=predefined_args.get('''activation'''					,	'''gelu'''    )					,	layer_norm_eps=predefined_args.get('''layer_norm_eps'''					,	UpperCamelCase__    )					,	)

       # 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(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	cls=UpperCamelCase__    )

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

       original_bort.load_parameters(UpperCamelCase__					,	cast_dtype=UpperCamelCase__					,	ignore_extra=UpperCamelCase__    )
       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(UpperCamelCase__    ),
       }

       A__		   =		BertConfig.from_dict(UpperCamelCase__    )
       A__		   =		BertForMaskedLM(UpperCamelCase__    )
       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(UpperCamelCase__     :			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(UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			List[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(UpperCamelCase__    )['''input_ids''']

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

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

       A__		   =		tokenizer.encode_plus(UpperCamelCase__					,	return_tensors='''pt'''    )
       A__		   =		hf_bort_model(**UpperCamelCase__    )[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(UpperCamelCase__					,	UpperCamelCase__					,	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:'''					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[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.'
       )
       a__: str							   =						parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





