




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  :				Any						=				logging.get_logger(__name__)

_lowerCAmelCase  :				List[Any]						=				"Hello world! cécé herlolip"



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				bool		):




 """simple docstring"""







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

 UpperCAmelCase__								=  DataaVecTextForSequenceClassification(_lowerCAmelCase		) if classification_head else DataaVecTextForMaskedLM(_lowerCAmelCase		)
 model.eval()

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

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

  # self attention
  UpperCAmelCase__								=  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)		)}'''
  )

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

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

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

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

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

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

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

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


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











from __future__ import annotations



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert (
     c % greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		) == 0
 )  # greatest_common_divisor(a,b) function implemented below
 ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							_lowerCAmelCase		)  # extended_gcd(a,b) function implemented below
 UpperCAmelCase__								=  c / d
 return (r * x, r * y)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int = 2		):




 """simple docstring"""







 ((UpperCAmelCase__)	,	(UpperCAmelCase__))								=  diophantine(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)  # Initial value
 UpperCAmelCase__								=  greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		)
 UpperCAmelCase__								=  a // d
 UpperCAmelCase__								=  b // d

 for i in range(_lowerCAmelCase		):
  UpperCAmelCase__								=  xa + i * q
  UpperCAmelCase__								=  ya - i * p
  print(_lowerCAmelCase  ,							_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 if a < b:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a

 while a % b != 0:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a % b

 return b



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  a, 1, 0
 else:
  ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							a % b		)
  UpperCAmelCase__								=  q
  UpperCAmelCase__								=  p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="diophantine", verbose=True)
  testmod(name="diophantine_all_soln", verbose=True)
  testmod(name="extended_gcd", verbose=True)
  testmod(name="greatest_common_divisor", verbose=True)





