




import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
   from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
   raise ImportError("You need to install the version of fairseq from the Mega repo!")




class     a__ (      nn.Module   ):







     def __init__(			self						,	A						,	A						,	A				)						->					List[str]:



          '''simple docstring'''



          super().__init__()
          a											=   mega_args
          a											=   nn.Embedding(A						,	self.mega_args.encoder_embed_dim				)
          a											=   nn.ModuleList([MegaEncoderLayer(self.mega_args				) for _ in range(A				)]				)
          a											=   depth







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=True						,	A=0				)						->					List[Any]:



          '''simple docstring'''



          if batch_first:
               a											=   input_ids.T

          # to make things more confusing, Mega expects the attention mask to
          # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
          # which is the opposite of what HF returns
          if ignore_mask_value == 0:
               a											=   1 - attention_mask

          # get token embeddings from IDs
          a											=   self.embedding_layer(A				)

          # pass through the Mega layers
          # input is (time, batch, encoder dim) and output is the same
          for encoder in self.encoders:
               a											=   encoder(A						,	A				)

          # return according to the shape specified
          if batch_first:
               # (T, B, H) --> (B, T, H)
               return torch.transpose(A						,	0						,	1				)
          else:
               return embeds







class     a__ (      nn.Module   ):







     def __init__(			self						,	A						,	A						,	A				)						->					Dict:



          '''simple docstring'''



          super().__init__()
          a											=   MegaLM(A						,	A						,	A				)
          a											=   nn.Linear(mega_args.encoder_embed_dim						,	A				)
          a											=   nn.Dropout(p=0.1				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=True						,	A=0				)						->					int:



          '''simple docstring'''



          a											=   self.mega(A						,	A						,	A						,	A				)
          return self.mlm_head(self.dropout(A				)				)







def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  Dict:
     with open(os.path.join(__UpperCamelCase	,      "model_args.pkl")	,      "rb") as f:
          a											=   pkl.load(__UpperCamelCase)

     # load the original encoder
     a											=   OriginalMegaForMaskedLM(**__UpperCamelCase).eval()

     # load its weights
     print(
         "Original Mega encoder:"	,      original_mlm.mega.load_state_dict(
             torch.load(os.path.join(__UpperCamelCase	,      "encoder_weights.pt")	,      map_location="cpu"))	,      )
     print(
         "Original Mega MLM layer:"	,      original_mlm.mlm_head.load_state_dict(
             torch.load(os.path.join(__UpperCamelCase	,      "mlm_head_weights.pt")	,      map_location="cpu"))	,      )

     # create a new config from the old one
     a											=   MegaConfig(
         num_hidden_layers=mega_original_args["depth"]	,      vocab_size=mega_original_args["vocab_size"]	,      hidden_size=mega_original_args["mega_args"].encoder_embed_dim	,      shared_representation_size=mega_original_args["mega_args"].encoder_z_dim	,      intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim	,      ema_projection_size=mega_original_args["mega_args"].encoder_n_dim	,      dropout_prob=mega_original_args["mega_args"].dropout	,      attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout	,      hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout	,      activation=mega_original_args["mega_args"].activation_fn	,      attention_activation=mega_original_args["mega_args"].attention_activation_fn	,      bidirectional=mega_original_args["mega_args"].bidirectional	,      use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0	,      chunk_size=mega_original_args["mega_args"].encoder_chunk_size	,      truncation=mega_original_args["mega_args"].truncation_length	,      normalization_type=mega_original_args["mega_args"].normalization_type	,      normalize_before_mega=__UpperCamelCase	,      norm_affine=__UpperCamelCase	,      use_feature_dropout=mega_original_args["mega_args"].feature_dropout	,      relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias	,      max_positions=mega_original_args["mega_args"].max_source_positions	,      nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim	,      normalize_before_ffn=mega_original_args["mega_args"].normalize_before	,      nffn_activation_dropout_prob=0.0	,      add_token_type_embeddings=__UpperCamelCase	,      add_lm_hidden_dense_layer=__UpperCamelCase	,      )

     a											=   MegaForMaskedLM(__UpperCamelCase).eval()

     # the originl checkpoint just uses nn.Embedding for the word embeddings
     # we use a wrapper module for embeddings to add support for positional embeddings
     a											=   original_mlm.mega.embedding_layer.weight

     # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
     # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
     # also renaming previously confusing parameter names
     a											=   original_mlm.mega.encoders.state_dict()
     a											=   {}
     for module_name in original_state_dict.keys():
          a											=   None
          # have to handle gamma, beta, and alpha differently due to their use
          # in multiple modules within the original repository;
          # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
          # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
          if "beta" in module_name:
               # EMA sub-layers were always called "move" in the original repo
               if "move.beta" in module_name:
                    a											=   module_name.replace("move.beta"	,      "ema_gate.ema_expansion_matrix")
               elif "mega_layer.beta" in module_name:
                    a											=   module_name.replace("beta"	,      "qk_bias")
               else:
                    a											=   module_name.replace("beta"	,      "b_param")
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
          elif "gamma" in module_name:
               if "move.gamma" in module_name:
                    a											=   module_name.replace("move.gamma"	,      "ema_gate.kernel_projection_matrix")
               elif "mega_layer.gamma" in module_name:
                    a											=   module_name.replace("gamma"	,      "qk_weight")
               else:
                    a											=   module_name.replace("gamma"	,      "g_param")
        # alpha is used in EMA and positional bias; renaming to improve readability
          elif "move.alpha" in module_name:
               a											=   module_name.replace("move.alpha"	,      "ema_gate.decay_factor")
          # delta is only used in EMA; renaming to improve readability
          elif "move.delta" in module_name:
               a											=   module_name.replace("move.delta"	,      "ema_gate.damping_factor")
          # omega is only used in EMA; renaming to improve readability
          elif "omega" in module_name:
               a											=   module_name.replace("move.omega"	,      "ema_gate.residual_weight")

          if new_module_name:
               a											=   new_module_name

     if len(__UpperCamelCase) != 0:
          print(f'''Renaming these keys: {updated_keys.keys()}''')
     else:
          print("No need to rename state dict entries")
     for old, new in updated_keys.items():
          a											=   original_state_dict.pop(__UpperCamelCase)

     # now attempt to load the state dictionary with updated names
     # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
     print("HF Mega encoder:"	,      hf_mlm.mega.layers.load_state_dict(__UpperCamelCase))

     # load the MLM head weights directly
     print(
         "HF Mega MLM layer:"	,      hf_mlm.mlm_head.load_state_dict(
             torch.load(os.path.join(__UpperCamelCase	,      "mlm_head_weights.pt")	,      map_location="cpu"))	,      )

     # test on a randomly generated input sequence
     a											=   torch.randint(0	,      hf_config.vocab_size	,      size=(4, 2_56))
     a											=   torch.ones_like(__UpperCamelCase)
     # mask a few tokens to make sure masking is applied appropriately :)
     a											=   0

     # run forward passes
     a											=   original_mlm(__UpperCamelCase	,      __UpperCamelCase	,      batch_first=__UpperCamelCase	,      ignore_mask_value=0)
     a											=   hf_mlm(__UpperCamelCase	,      __UpperCamelCase)[0]

     # print shapes and diff
     print(f'''original output {original_output.shape}''')
     print(f'''hf output {hf_output.shape}''')
     print(f'''max diff: {(original_output - hf_output).max()}''')  # 0.0
     a											=   torch.allclose(__UpperCamelCase	,      __UpperCamelCase	,      atol=1e-3)

     if success:
          print("Yay!")
          hf_mlm.save_pretrained(__UpperCamelCase)
     else:
          raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}''')

     if includes_tokenizer:
          print("Transferring tokenizer")
          a											=   AutoTokenizer.from_pretrained(__UpperCamelCase)
          tokenizer.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
   lowercase__   :    Optional[int] 					=      argparse.ArgumentParser()

   parser.add_argument(
       "--pretrained_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help="Point to the directory containing your model weights using the official Mega repo",
   )

   parser.add_argument(
       "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
   )

   parser.add_argument(
       "--includes_tokenizer",
       action="store_true",
       help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
   )

   lowercase__   :    str 					=      parser.parse_args()

   convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







from __future__ import annotations

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  int | None:
     for r in range(1	,      __UpperCamelCase):
          a											=   []
          for x in range(n - 1):
               a											=   pow(__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)
               if val in li:
                    break
               li.append(__UpperCamelCase)
          else:
               return r
     return None


if __name__ == "__main__":
   lowercase__   :    Tuple 					=      int(input("Enter a prime number q: "))
   lowercase__   :    List[Any] 					=      find_primitive(q)
   if a is None:
      print(F'Cannot find the primitive for the value: {a!r}')
   else:
      lowercase__   :    Any 					=      int(input("Enter private key of A: "))
      lowercase__   :    Optional[int] 					=      pow(a, a_private, q)
      lowercase__   :    Optional[Any] 					=      int(input("Enter private key of B: "))
      lowercase__   :    Any 					=      pow(a, b_private, q)

      lowercase__   :    Optional[Any] 					=      pow(b_public, a_private, q)
      lowercase__   :    List[str] 					=      pow(a_public, b_private, q)

      print("The key value generated by A is: ", a_secret)
      print("The key value generated by B is: ", b_secret)

