


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
 raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
__lowerCAmelCase									=				logging.get_logger(__name__)

__lowerCAmelCase									=				'''Hello world! cécé herlolip'''




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case	)    ->							List[Any]:
       lowercase__     ,		lowercase__:  Optional[Any]         =  os.path.split(snake_case	)
       lowercase__:  Any         =  DataaVecTextModel.from_pretrained(
           snake_case     ,				checkpoint_file=snake_case	)
       dataavec.eval()  # disable dropout
       lowercase__:  Tuple         =  dataavec.models[0]
       lowercase__:  Dict         =  dataavec_model.encoder.sentence_encoder
       lowercase__:  List[Any]         =  DataaVecTextConfig(
           vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,				hidden_size=dataavec_model.args.encoder_embed_dim     ,				num_hidden_layers=dataavec_model.args.encoder_layers     ,				num_attention_heads=dataavec_model.args.encoder_attention_heads     ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,				max_position_embeddings=5_14     ,				type_vocab_size=1     ,				layer_norm_eps=1e-5     ,				)
       if classification_head:
              lowercase__:  List[Any]         =  dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'     ,				snake_case	)

       lowercase__:  Union[str, Any]         =  DataaVecTextForSequenceClassification(snake_case	) if classification_head else DataaVecTextForMaskedLM(snake_case	)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       lowercase__:  List[Any]         =  dataavec_sent_encoder.embed_tokens.weight
       lowercase__:  List[str]         =  dataavec_sent_encoder.embed_positions.weight
       lowercase__:  Optional[int]         =  torch.zeros_like(
           model.dataavec_text.embeddings.token_type_embeddings.weight	)  # just zero them out b/c data2vec doesn't use them.
       lowercase__:  Optional[Any]         =  dataavec_sent_encoder.layernorm_embedding.weight
       lowercase__:  List[str]         =  dataavec_sent_encoder.layernorm_embedding.bias

       for i in range(config.num_hidden_layers	):
              # Encoder: start of layer
              lowercase__:  BertLayer         =  model.dataavec_text.encoder.layer[i]
              lowercase__:  TransformerSentenceEncoderLayer         =  dataavec_sent_encoder.layers[i]

              # self attention
              lowercase__:  BertSelfAttention         =  layer.attention.self
              assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)	), (
                  "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                  f' {torch.Size((config.hidden_size, config.hidden_size)	)}'
              )
              assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)	), (
                  "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                  f' {torch.Size((config.hidden_size, config.hidden_size)	)}'
              )
              assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)	), (
                  "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                  f' {torch.Size((config.hidden_size, config.hidden_size)	)}'
              )

              lowercase__:  List[str]         =  dataavec_layer.self_attn.q_proj.weight
              lowercase__:  Any         =  dataavec_layer.self_attn.q_proj.bias
              lowercase__:  int         =  dataavec_layer.self_attn.k_proj.weight
              lowercase__:  Tuple         =  dataavec_layer.self_attn.k_proj.bias
              lowercase__:  Dict         =  dataavec_layer.self_attn.v_proj.weight
              lowercase__:  List[Any]         =  dataavec_layer.self_attn.v_proj.bias

              # self-attention output
              lowercase__:  BertSelfOutput         =  layer.attention.output
              assert (
                  self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
              ), f'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
              lowercase__:  str         =  dataavec_layer.self_attn.out_proj.weight
              lowercase__:  Union[str, Any]         =  dataavec_layer.self_attn.out_proj.bias
              lowercase__:  Union[str, Any]         =  dataavec_layer.self_attn_layer_norm.weight
              lowercase__:  Tuple         =  dataavec_layer.self_attn_layer_norm.bias

              # intermediate
              lowercase__:  BertIntermediate         =  layer.intermediate
              assert (
                  intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
              ), f'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
              lowercase__:  Optional[Any]         =  dataavec_layer.fca.weight
              lowercase__:  Dict         =  dataavec_layer.fca.bias

              # output
              lowercase__:  BertOutput         =  layer.output
              assert (
                  bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
              ), f'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
              lowercase__:  Optional[int]         =  dataavec_layer.fca.weight
              lowercase__:  Dict         =  dataavec_layer.fca.bias
              lowercase__:  int         =  dataavec_layer.final_layer_norm.weight
              lowercase__:  str         =  dataavec_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              lowercase__:  List[str]         =  dataavec.model.classification_heads['mnli'].dense.weight
              lowercase__:  Any         =  dataavec.model.classification_heads['mnli'].dense.bias
              lowercase__:  Union[str, Any]         =  dataavec.model.classification_heads['mnli'].out_proj.weight
              lowercase__:  Tuple         =  dataavec.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              lowercase__:  Any         =  dataavec_model.encoder.lm_head.dense.weight
              lowercase__:  Any         =  dataavec_model.encoder.lm_head.dense.bias
              lowercase__:  Optional[int]         =  dataavec_model.encoder.lm_head.layer_norm.weight
              lowercase__:  int         =  dataavec_model.encoder.lm_head.layer_norm.bias
              lowercase__:  Optional[Any]         =  dataavec_model.encoder.lm_head.weight
              lowercase__:  Union[str, Any]         =  dataavec_model.encoder.lm_head.bias

       # Let's check that we get the same results.
       lowercase__:  torch.Tensor         =  dataavec.encode(snake_case	).unsqueeze(0	)  # batch of size 1

       lowercase__:  Optional[Any]         =  model(snake_case	)[0]
       if classification_head:
              lowercase__:  List[str]         =  dataavec.model.classification_heads['mnli'](dataavec.extract_features(snake_case	)	)
       else:
              lowercase__:  List[Any]         =  dataavec_model(snake_case	)[0]
       print(our_output.shape     ,				their_output.shape	)
       lowercase__:  List[str]         =  torch.max(torch.abs(our_output - their_output	)	).item()
       print(f'max_absolute_diff = {max_absolute_diff}'	)  # ~ 1e-7
       lowercase__:  List[Any]         =  torch.allclose(snake_case     ,				snake_case     ,				atol=1e-3	)
       print('Do both models output the same tensors?'     ,				'🔥' if success else '💩'	)
       if not success:
              raise Exception('Something went wRoNg'	)

       pathlib.Path(snake_case	).mkdir(parents=snake_case     ,				exist_ok=snake_case	)
       print(f'Saving model to {pytorch_dump_folder_path}'	)
       model.save_pretrained(snake_case	)


if __name__ == "__main__":
 __lowerCAmelCase									=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
 )
 __lowerCAmelCase									=				parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase									=				{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
 from .tokenization_phobert import PhobertTokenizer

else:
 import sys

 __lowerCAmelCase									=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


