





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 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Optional[Any]:
       _snake_case				,    _snake_case      :							Optional[Any]     				=						os.path.split(lowercase_  )
       _snake_case      :							Any     				=						DataaVecTextModel.from_pretrained(
           lowercase_      ,		checkpoint_file=lowercase_  )
       dataavec.eval()  # disable dropout
       _snake_case      :							int     				=						dataavec.models[0]
       _snake_case      :							int     				=						dataavec_model.encoder.sentence_encoder
       _snake_case      :							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=514      ,		type_vocab_size=1      ,		layer_norm_eps=1E-5      ,		)
       if classification_head:
              _snake_case      :							Union[str, Any]     				=						dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
       print('''Our BERT config:'''      ,		lowercase_  )

       _snake_case      :							List[Any]     				=						DataaVecTextForSequenceClassification(lowercase_  ) if classification_head else DataaVecTextForMaskedLM(lowercase_  )
       model.eval()

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

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

              # self attention
              _snake_case      :							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)  )}'''
              )

              _snake_case      :							Optional[int]     				=						dataavec_layer.self_attn.q_proj.weight
              _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.q_proj.bias
              _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.k_proj.weight
              _snake_case      :							str     				=						dataavec_layer.self_attn.k_proj.bias
              _snake_case      :							Union[str, Any]     				=						dataavec_layer.self_attn.v_proj.weight
              _snake_case      :							List[Any]     				=						dataavec_layer.self_attn.v_proj.bias

              # self-attention output
              _snake_case      :							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}'''
              _snake_case      :							Dict     				=						dataavec_layer.self_attn.out_proj.weight
              _snake_case      :							str     				=						dataavec_layer.self_attn.out_proj.bias
              _snake_case      :							Any     				=						dataavec_layer.self_attn_layer_norm.weight
              _snake_case      :							Optional[Any]     				=						dataavec_layer.self_attn_layer_norm.bias

              # intermediate
              _snake_case      :							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}'''
              _snake_case      :							Optional[int]     				=						dataavec_layer.fca.weight
              _snake_case      :							List[str]     				=						dataavec_layer.fca.bias

              # output
              _snake_case      :							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}'''
              _snake_case      :							List[Any]     				=						dataavec_layer.fca.weight
              _snake_case      :							Any     				=						dataavec_layer.fca.bias
              _snake_case      :							Optional[Any]     				=						dataavec_layer.final_layer_norm.weight
              _snake_case      :							str     				=						dataavec_layer.final_layer_norm.bias
              # end of layer

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

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

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

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


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
   )













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






