




"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
   raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
   raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Any			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[str]			=	"""Hello, World!"""
SCREAMING_SNAKE_CASE__:Any			=	"""en_XX"""



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  Path("data_bin"							)
       __a     				=  FairseqXmodModel.from_pretrained(
           model_name_or_path=str(Path(__snake_case							).parent							) ,  checkpoint_file=Path(__snake_case							).name ,  _name="xmod_base" ,  arch="xmod_base" ,  task="multilingual_masked_lm" ,  data_name_or_path=str(__snake_case							) ,  bpe="sentencepiece" ,  sentencepiece_model=str(Path(__snake_case							).parent / "sentencepiece.bpe.model"							) ,  src_dict=str(data_dir / "dict.txt"							) ,  )
       xmod.eval()  # disable dropout
       print(__snake_case							)

       __a     				=  xmod.model.encoder.sentence_encoder
       __a     				=  XmodConfig(
           vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,  hidden_size=xmod.cfg.model.encoder_embed_dim ,  num_hidden_layers=xmod.cfg.model.encoder_layers ,  num_attention_heads=xmod.cfg.model.encoder_attention_heads ,  intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,  max_position_embeddings=5_1_4 ,  type_vocab_size=1 ,  layer_norm_eps=1E-5 ,  pre_norm=xmod.cfg.model.encoder_normalize_before ,  adapter_reduction_factor=getattr(xmod.cfg.model ,  "bottleneck" ,  2							) ,  adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,  adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,  ln_before_adapter=xmod.cfg.model.ln_before_adapter ,  languages=xmod.cfg.model.languages ,  )
       if classification_head:
              __a     				=  xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

       print("Our X-MOD config:" ,  __snake_case							)

       __a     				=  XmodForSequenceClassification(__snake_case							) if classification_head else XmodForMaskedLM(__snake_case							)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       __a     				=  xmod_sent_encoder.embed_tokens.weight
       __a     				=  xmod_sent_encoder.embed_positions.weight
       __a     				=  torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c xmod doesn't use them.

       __a     				=  xmod_sent_encoder.layernorm_embedding.weight
       __a     				=  xmod_sent_encoder.layernorm_embedding.bias

       for i in range(config.num_hidden_layers							):
              # Encoder: start of layer
              __a     				=  model.roberta.encoder.layer[i]
              __a     				=  xmod_sent_encoder.layers[i]

              # self attention
              __a     				=  layer.attention.self
              if not (
                  xmod_layer.self_attn.k_proj.weight.data.shape
                  == xmod_layer.self_attn.q_proj.weight.data.shape
                  == xmod_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)							)
              ):
                     raise AssertionError("Dimensions of self-attention weights do not match."							)

              __a     				=  xmod_layer.self_attn.q_proj.weight
              __a     				=  xmod_layer.self_attn.q_proj.bias
              __a     				=  xmod_layer.self_attn.k_proj.weight
              __a     				=  xmod_layer.self_attn.k_proj.bias
              __a     				=  xmod_layer.self_attn.v_proj.weight
              __a     				=  xmod_layer.self_attn.v_proj.bias

              # self-attention output
              __a     				=  layer.attention.output
              if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                     raise AssertionError("Dimensions of self-attention output weights do not match."							)
              __a     				=  xmod_layer.self_attn.out_proj.weight
              __a     				=  xmod_layer.self_attn.out_proj.bias
              __a     				=  xmod_layer.self_attn_layer_norm.weight
              __a     				=  xmod_layer.self_attn_layer_norm.bias

              # intermediate
              __a     				=  layer.intermediate
              if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError("Dimensions of intermediate weights do not match."							)
              __a     				=  xmod_layer.fca.weight
              __a     				=  xmod_layer.fca.bias

              # output
              __a     				=  layer.output
              if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                     raise AssertionError("Dimensions of feed-forward weights do not match."							)
              __a     				=  xmod_layer.fca.weight
              __a     				=  xmod_layer.fca.bias
              __a     				=  xmod_layer.final_layer_norm.weight
              __a     				=  xmod_layer.final_layer_norm.bias
              if bert_output.adapter_layer_norm is not None:
                     __a     				=  xmod_layer.adapter_layer_norm.weight
                     __a     				=  xmod_layer.adapter_layer_norm.bias

              if sorted(bert_output.adapter_modules.keys()							) != sorted(xmod_layer.adapter_modules.keys()							):
                     raise AssertionError("Lists of language adapters do not match."							)
              for lang_code, adapter in xmod_layer.adapter_modules.items():
                     __a     				=  bert_output.adapter_modules[lang_code]
                     __a     				=  xmod_layer.adapter_modules[lang_code]
                     __a     				=  from_adapter.fca.weight
                     __a     				=  from_adapter.fca.bias
                     __a     				=  from_adapter.fca.weight
                     __a     				=  from_adapter.fca.bias

              # end of layer

       if xmod_sent_encoder.layer_norm is not None:
              __a     				=  xmod_sent_encoder.layer_norm.weight
              __a     				=  xmod_sent_encoder.layer_norm.bias

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

       # Let's check that we get the same results.
       __a     				=  xmod.encode(__snake_case							).unsqueeze(0							)  # batch of size 1
       model.roberta.set_default_language(__snake_case							)

       __a     				=  model(__snake_case							)[0]
       if classification_head:
              __a     				=  xmod.model.classification_heads["mnli"](xmod.extract_features(__snake_case							)							)
       else:
              __a     				=  xmod.model(__snake_case ,  lang_id=[SAMPLE_LANGUAGE]							)[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"							)

       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__":
   SCREAMING_SNAKE_CASE__:List[str]			=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--xmod_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."""
   )
   SCREAMING_SNAKE_CASE__:int			=	parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )

"""simple docstring"""



import math



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  2
       __a     				=  int(math.sqrt(a							)							)  # Size of every segment
       __a     				=  [True] * (end + 1)
       __a     				=  []

       while start <= end:
              if temp[start] is True:
                     in_prime.append(a							)
                     for i in range(start * start ,  end + 1 ,  a							):
                            __a     				=  False
              start += 1
       prime += in_prime

       __a     				=  end + 1
       __a     				=  min(2 * end ,  a							)

       while low <= n:
              __a     				=  [True] * (high - low + 1)
              for each in in_prime:
                     __a     				=  math.floor(low / each							) * each
                     if t < low:
                            t += each

                     for j in range(a ,  high + 1 ,  a							):
                            __a     				=  False

              for j in range(len(a							)							):
                     if temp[j] is True:
                            prime.append(j + low							)

              __a     				=  high + 1
              __a     				=  min(high + end ,  a							)

       return prime




print(sieve(10**6))
