"""simple docstring"""


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   UpperCAmelCase_	(				nn.Module   ):
    def __init__(	self			:       List[str]   ,						_lowercase			:       List[str]   ,						_lowercase			:       List[str]   ,						_lowercase			:       List[Any]		)						->    List[Any]:
       super().__init__()
       _lowercase							     =       mega_args
       _lowercase							     =       nn.Embedding(snake_case__   ,						self.mega_args.encoder_embed_dim		)
       _lowercase							     =       nn.ModuleList([MegaEncoderLayer(self.mega_args		) for _ in range(snake_case__		)]		)
       _lowercase							     =       depth




    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Optional[int]=True   ,						_lowercase			:       int=0		)						->    int:





       if batch_first:
          _lowercase							     =       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:
          _lowercase							     =       1 - attention_mask

       # get token embeddings from IDs
       _lowercase							     =       self.embedding_layer(snake_case__		)

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

       # return according to the shape specified
       if batch_first:
          # (T, B, H) --> (B, T, H)
          return torch.transpose(snake_case__   ,						0   ,						1		)
       else:
          return embeds
class   UpperCAmelCase_	(				nn.Module   ):
    def __init__(	self			:       Union[str, Any]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]		)						->    Optional[int]:
       super().__init__()
       _lowercase							     =       MegaLM(snake_case__   ,						snake_case__   ,						snake_case__		)
       _lowercase							     =       nn.Linear(mega_args.encoder_embed_dim   ,						snake_case__		)
       _lowercase							     =       nn.Dropout(p=0.1		)




    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Tuple=True   ,						_lowercase			:       Tuple=0		)						->    str:
       _lowercase							     =       self.mega(snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__		)
       return self.mlm_head(self.dropout(snake_case__		)		)


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Tuple,				_snake_case		:       Optional[Any]       ):
   with open(os.path.join(a_,				"model_args.pkl"       ),				"rb"       ) as f:
      _lowercase							     =       pkl.load(a_       )

   # load the original encoder
   _lowercase							     =       OriginalMegaForMaskedLM(**a_       ).eval()

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

   # create a new config from the old one
   _lowercase							     =       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=a_,				norm_affine=a_,				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=a_,				add_lm_hidden_dense_layer=a_,				)

   _lowercase							     =       MegaForMaskedLM(a_       ).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
   _lowercase							     =       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
   _lowercase							     =       original_mlm.mega.encoders.state_dict()
   _lowercase							     =       {}
   for module_name in original_state_dict.keys():
      _lowercase							     =       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:
            _lowercase							     =       module_name.replace("move.beta",				"ema_gate.ema_expansion_matrix"       )
         elif "mega_layer.beta" in module_name:
            _lowercase							     =       module_name.replace("beta",				"qk_bias"       )
         else:
            _lowercase							     =       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:
            _lowercase							     =       module_name.replace("move.gamma",				"ema_gate.kernel_projection_matrix"       )
         elif "mega_layer.gamma" in module_name:
            _lowercase							     =       module_name.replace("gamma",				"qk_weight"       )
         else:
            _lowercase							     =       module_name.replace("gamma",				"g_param"       )
        # alpha is used in EMA and positional bias; renaming to improve readability
      elif "move.alpha" in module_name:
         _lowercase							     =       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:
         _lowercase							     =       module_name.replace("move.delta",				"ema_gate.damping_factor"       )
      # omega is only used in EMA; renaming to improve readability
      elif "omega" in module_name:
         _lowercase							     =       module_name.replace("move.omega",				"ema_gate.residual_weight"       )

      if new_module_name:
         _lowercase							     =       new_module_name

   if len(a_       ) != 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():
      _lowercase							     =       original_state_dict.pop(a_       )

   # 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(a_       )       )

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

   # test on a randomly generated input sequence
   _lowercase							     =       torch.randint(0,				hf_config.vocab_size,				size=(4, 2_5_6)       )
   _lowercase							     =       torch.ones_like(a_       )
   # mask a few tokens to make sure masking is applied appropriately :)
   _lowercase							     =       0

   # run forward passes
   _lowercase							     =       original_mlm(a_,				a_,				batch_first=a_,				ignore_mask_value=0       )
   _lowercase							     =       hf_mlm(a_,				a_       )[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
   _lowercase							     =       torch.allclose(a_,				a_,				atol=1e-3       )

   if success:
      print("Yay!"       )
      hf_mlm.save_pretrained(a_       )
   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"       )
      _lowercase							     =       AutoTokenizer.from_pretrained(a_       )
      tokenizer.save_pretrained(a_       )


if __name__ == "__main__":
  __UpperCamelCase       :    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",
  )

  __UpperCamelCase       :    Any         =		parser.parse_args()

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

from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




