



'''simple docstring'''





import posixpath
from pathlib import Path
from unittest.mock import patch

import pytest
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
from fsspec.registry import _registry as _fsspec_registry






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[Any]										=	'mock'



 def __init__(self			,       *A			,       A			,       **A	)   ->					int:


      """simple docstring"""



      super().__init__()
      _a     =  LocalFileSystem(*A			,       **A	)
      _a     =  Path(A	).resolve().as_posix() + '''/'''



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Any:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.mkdir(A			,       *A			,       **A	)



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Tuple:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.makedirs(A			,       *A			,       **A	)



 def  a__  (self			,       A	)   ->					str:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.rmdir(A	)



 def  a__  (self			,       A			,       A=True			,       *A			,       **A	)   ->					Dict:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      _a     =  self._fs.ls(A			,       detail=A			,       *A			,       **A	)
      if detail:
           return [{**info, "name": info["name"][len(self.local_root_dir	) :]} for info in out]
      else:
           return [name[len(self.local_root_dir	) :] for name in out]



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      _a     =  dict(self._fs.info(A			,       *A			,       **A	)	)
      _a     =  out['''name'''][len(self.local_root_dir	) :]
      return out



 def  a__  (self			,       A			,       A			,       *A			,       **A	)   ->					int:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.cp_file(A			,       A			,       *A			,       **A	)



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Dict:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.rm_file(A			,       *A			,       **A	)



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Tuple:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.rm(A			,       *A			,       **A	)



 def  a__  (self			,       A			,       *A			,       **A	)   ->					Tuple:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs._open(A			,       *A			,       **A	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.created(A	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  posixpath.join(self.local_root_dir			,       self._strip_protocol(A	)	)
      return self._fs.modified(A	)



 @classmethod
 def  a__  (cls			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  stringify_path(A	)
      if path.startswith('''mock://'''	):
           _a     =  path[7:]
      return path






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Any										=	'tmp'
 __lowerCamelCase   :	Optional[Any]										=	None



 def __init__(self			,       *A			,       **A	)   ->					Union[str, Any]:


      """simple docstring"""



      assert self.tmp_dir is not None, "TmpDirFileSystem.tmp_dir is not set"
      super().__init__(*A			,       **A			,       local_root_dir=self.tmp_dir			,       auto_mkdir=A	)



 @classmethod
 def  a__  (cls			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  stringify_path(A	)
      if path.startswith('''tmp://'''	):
           _a     =  path[6:]
      return path





@pytest.fixture
def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  MockFileSystem
     _a     =  TmpDirFileSystem
     yield
     del _fsspec_registry["mock"]
     del _fsspec_registry["tmp"]




@pytest.fixture
def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  tmp_path_factory.mktemp('''mockfs''')
     return MockFileSystem(local_root_dir=__A  ,     auto_mkdir=__A)




@pytest.fixture
def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  tmp_path_factory.mktemp('''tmpfs''')
     with patch.object(__A  ,     '''tmp_dir'''  ,     __A):
          yield TmpDirFileSystem()








'''simple docstring'''





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()
lowercase_			=  logging.get_logger(__name__)

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     dataavec.eval()  # disable dropout
     _a     =  dataavec.models[0]
     _a     =  dataavec_model.encoder.sentence_encoder
     _a     =  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:
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''  ,     __A)

     _a     =  DataaVecTextForSequenceClassification(__A) if classification_head else DataaVecTextForMaskedLM(__A)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a     =  dataavec_sent_encoder.embed_tokens.weight
     _a     =  dataavec_sent_encoder.embed_positions.weight
     _a     =  torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
     _a     =  dataavec_sent_encoder.layernorm_embedding.weight
     _a     =  dataavec_sent_encoder.layernorm_embedding.bias

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

          # self attention
          _a     =  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))}'''
          )

          _a     =  dataavec_layer.self_attn.q_proj.weight
          _a     =  dataavec_layer.self_attn.q_proj.bias
          _a     =  dataavec_layer.self_attn.k_proj.weight
          _a     =  dataavec_layer.self_attn.k_proj.bias
          _a     =  dataavec_layer.self_attn.v_proj.weight
          _a     =  dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          _a     =  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}'''
          _a     =  dataavec_layer.self_attn.out_proj.weight
          _a     =  dataavec_layer.self_attn.out_proj.bias
          _a     =  dataavec_layer.self_attn_layer_norm.weight
          _a     =  dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _a     =  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}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias

          # output
          _a     =  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}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias
          _a     =  dataavec_layer.final_layer_norm.weight
          _a     =  dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].dense.bias
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          _a     =  dataavec_model.encoder.lm_head.dense.weight
          _a     =  dataavec_model.encoder.lm_head.dense.bias
          _a     =  dataavec_model.encoder.lm_head.layer_norm.weight
          _a     =  dataavec_model.encoder.lm_head.layer_norm.bias
          _a     =  dataavec_model.encoder.lm_head.weight
          _a     =  dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     _a     =  dataavec.encode(__A).unsqueeze(0)  # batch of size 1

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

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


if __name__ == "__main__":
  lowercase_			=  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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



