



'''simple docstring'''





import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class __A      (  TensorFormatter[Mapping, np.ndarray, Mapping]						):



 '''simple docstring'''







 def __init__(self			,       A=None			,       **A	)   ->					List[Any]:


      """simple docstring"""



      super().__init__(features=_snake_case	)
      _a     =  np_array_kwargs



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


      """simple docstring"""



      if isinstance(_snake_case			,       _snake_case	):
           if column and all(
               isinstance(_snake_case			,       np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                return np.stack(_snake_case	)
           else:
                # don't use np.array(column, dtype=object)
                # since it fails in certain cases
                # see https://stackoverflow.com/q/51005699
                _a     =  np.empty(len(_snake_case	)			,       dtype=_snake_case	)
                _a     =  column
                return out
      return column



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


      """simple docstring"""



      if isinstance(_snake_case			,       (str, bytes, type(_snake_case	))	):
           return value
      elif isinstance(_snake_case			,       (np.character, np.ndarray)	) and np.issubdtype(value.dtype			,       np.character	):
           return value
      elif isinstance(_snake_case			,       np.number	):
           return value

      _a     =  {}

      if isinstance(_snake_case			,       np.ndarray	) and np.issubdtype(value.dtype			,       np.integer	):
           _a     =  {'dtype': np.intaa}
      elif isinstance(_snake_case			,       np.ndarray	) and np.issubdtype(value.dtype			,       np.floating	):
           _a     =  {'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(_snake_case			,       PIL.Image.Image	):
                return np.asarray(_snake_case			,       **self.np_array_kwargs	)

      return np.asarray(_snake_case			,       **{**default_dtype, **self.np_array_kwargs}	)



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


      """simple docstring"""



      if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(_snake_case			,       torch.Tensor	):
                return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
      if hasattr(_snake_case			,       '''__array__'''	) and not isinstance(_snake_case			,       (np.ndarray, np.character, np.number)	):
           _a     =  data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_snake_case			,       np.ndarray	):
           if data_struct.dtype == object:
                return self._consolidate([self.recursive_tensorize(_snake_case	) for substruct in data_struct]	)
      if isinstance(_snake_case			,       (list, tuple)	):
           return self._consolidate([self.recursive_tensorize(_snake_case	) for substruct in data_struct]	)
      return self._tensorize(_snake_case	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return map_nested(self._recursive_tensorize			,       _snake_case			,       map_list=_snake_case	)



 def  a__  (self			,       A	)   ->					Mapping:


      """simple docstring"""



      _a     =  self.numpy_arrow_extractor().extract_row(_snake_case	)
      _a     =  self.python_features_decoder.decode_row(_snake_case	)
      return self.recursive_tensorize(_snake_case	)



 def  a__  (self			,       A	)   ->					np.ndarray:


      """simple docstring"""



      _a     =  self.numpy_arrow_extractor().extract_column(_snake_case	)
      _a     =  self.python_features_decoder.decode_column(_snake_case			,       pa_table.column_names[0]	)
      _a     =  self.recursive_tensorize(_snake_case	)
      _a     =  self._consolidate(_snake_case	)
      return column



 def  a__  (self			,       A	)   ->					Mapping:


      """simple docstring"""



      _a     =  self.numpy_arrow_extractor().extract_batch(_snake_case	)
      _a     =  self.python_features_decoder.decode_batch(_snake_case	)
      _a     =  self.recursive_tensorize(_snake_case	)
      for column_name in batch:
           _a     =  self._consolidate(batch[column_name]	)
      return batch











'''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
  )



