





"""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()
__UpperCamelCase    :	List[str]						=     logging.get_logger(__name__)

__UpperCamelCase    :	Any						=     '''Hello world! cécé herlolip'''

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

 lowerCAmelCase													=   DataaVecTextForSequenceClassification(_UpperCAmelCase ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase )
 model.eval()

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

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

  # self attention
  lowerCAmelCase													=   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) )}'
  )

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

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

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

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

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

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

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

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


if __name__ == "__main__":
       __UpperCamelCase    :	Optional[int]						=     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    :	Any						=     parser.parse_args()
       convert_dataavec_checkpoint_to_pytorch(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












"""simple docstring"""
import math

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5     ,    int(math.sqrt(_UpperCAmelCase ) + 1 )     ,    6 ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int = 200_0000 ):
 return sum(num for num in range(3     ,    _UpperCAmelCase     ,    2 ) if is_prime(_UpperCAmelCase ) ) + 2 if n > 2 else 0


if __name__ == "__main__":
       print(f'''{solution() = }''')





