





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

_UpperCamelCase   :							Optional[Any]     =	"""Hello world! cécé herlolip"""
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						List[str]:


   '''simple docstring'''







   __A ,						__A				 =       os.path.split(snake_case     )
   __A				 =       DataaVecTextModel.from_pretrained(
       snake_case    ,	checkpoint_file=snake_case     )
   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=5_1_4    ,	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:'''    ,	snake_case     )

   __A				 =       DataaVecTextForSequenceClassification(snake_case     ) if classification_head else DataaVecTextForMaskedLM(snake_case     )
   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(snake_case     ).unsqueeze(0     )  # batch of size 1

   __A				 =       model(snake_case     )[0]
   if classification_head:
      __A				 =       dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(snake_case     )     )
   else:
      __A				 =       dataavec_model(snake_case     )[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(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__":
    _UpperCamelCase   :							str     =	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."""
    )
    _UpperCamelCase   :							Dict     =	parser.parse_args()
    convert_dataavec_checkpoint_to_pytorch(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )








import numpy as np
def 				__UpperCamelCase  (		snake_case     )  ->						Any:


   '''simple docstring'''







   __A ,						__A				 =       a.shape
   __A				 =       min(snake_case    ,	snake_case     )
   __A				 =       np.eye(snake_case     )
   __A				 =       a.copy()

   for k in range(t - 1     ):
      # select a column of modified matrix A':
      __A				 =       r[k:, [k]]
      # construct first basis vector
      __A				 =       np.zeros_like(snake_case     )
      __A				 =       1.0
      # determine scaling factor
      __A				 =       np.linalg.norm(snake_case     )
      # construct vector v for Householder reflection
      __A				 =       x + np.sign(x[0]     ) * alpha * ea
      v /= np.linalg.norm(snake_case     )

      # construct the Householder matrix
      __A				 =       np.eye(m - k     ) - 2.0 * v @ v.T
      # pad with ones and zeros as necessary
      __A				 =       np.block([[np.eye(snake_case     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

      __A				 =       q @ q_k.T
      __A				 =       q_k @ r

   return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()

