



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()
a__      =		logging.get_logger(__name__)

a__      =		"""Hello world! cécé herlolip"""



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		bool			)					->       str:
 _snake_case		,      _snake_case      :					Optional[int]					=   os.path.split(SCREAMING_SNAKE_CASE__			)
 _snake_case      :					List[str]					=   DataaVecTextModel.from_pretrained(
     SCREAMING_SNAKE_CASE__			,    checkpoint_file=SCREAMING_SNAKE_CASE__			)
 dataavec.eval()  # disable dropout
 _snake_case      :					List[Any]					=   dataavec.models[0]
 _snake_case      :					Any					=   dataavec_model.encoder.sentence_encoder
 _snake_case      :					Dict					=   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      :					List[Any]					=   dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
 print("""Our BERT config:"""			,    SCREAMING_SNAKE_CASE__			)

 _snake_case      :					Any					=   DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE__			) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE__			)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 _snake_case      :					Tuple					=   dataavec_sent_encoder.embed_tokens.weight
 _snake_case      :					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      :					Union[str, Any]					=   dataavec_sent_encoder.layernorm_embedding.weight
 _snake_case      :					List[str]					=   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      :					str					=   dataavec_layer.self_attn.q_proj.weight
  _snake_case      :					List[Any]					=   dataavec_layer.self_attn.q_proj.bias
  _snake_case      :					Union[str, Any]					=   dataavec_layer.self_attn.k_proj.weight
  _snake_case      :					List[Any]					=   dataavec_layer.self_attn.k_proj.bias
  _snake_case      :					List[Any]					=   dataavec_layer.self_attn.v_proj.weight
  _snake_case      :					Tuple					=   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      :					int					=   dataavec_layer.self_attn.out_proj.weight
  _snake_case      :					List[str]					=   dataavec_layer.self_attn.out_proj.bias
  _snake_case      :					str					=   dataavec_layer.self_attn_layer_norm.weight
  _snake_case      :					Tuple					=   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      :					Any					=   dataavec_layer.fca.weight
  _snake_case      :					Optional[int]					=   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      :					int					=   dataavec_layer.fca.weight
  _snake_case      :					str					=   dataavec_layer.fca.bias
  _snake_case      :					Optional[Any]					=   dataavec_layer.final_layer_norm.weight
  _snake_case      :					List[Any]					=   dataavec_layer.final_layer_norm.bias
  # end of layer

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

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

 _snake_case      :					Optional[int]					=   model(SCREAMING_SNAKE_CASE__			)[0]
 if classification_head:
  _snake_case      :					Dict					=   dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(SCREAMING_SNAKE_CASE__			)			)
 else:
  _snake_case      :					List[Any]					=   dataavec_model(SCREAMING_SNAKE_CASE__			)[0]
 print(our_output.shape			,    their_output.shape			)
 _snake_case      :					Dict					=   torch.max(torch.abs(our_output - their_output			)			).item()
 print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
 _snake_case      :					Optional[int]					=   torch.allclose(SCREAMING_SNAKE_CASE__			,    SCREAMING_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(SCREAMING_SNAKE_CASE__			).mkdir(parents=SCREAMING_SNAKE_CASE__			,    exist_ok=SCREAMING_SNAKE_CASE__			)
 print(F'''Saving model to {pytorch_dump_folder_path}'''			)
 model.save_pretrained(SCREAMING_SNAKE_CASE__			)


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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


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

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
