


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    __magic_name__						(  nn.Module):


     def __init__(   self				:  Tuple				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  Optional[Any]				,						lowerCamelCase__				:  Dict      )	->   Optional[int]:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__       :     Optional[int] 							= mega_args
      UpperCamelCase__       :     Any 							= nn.Embedding(_lowercase				,						self.mega_args.encoder_embed_dim      )
      UpperCamelCase__       :     Dict 							= nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(_lowercase      )]      )
      UpperCamelCase__       :     Optional[int] 							= depth


     def   UpperCAmelCase__						(   self				:  List[str]				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Tuple=True				,						lowerCamelCase__				:  Tuple=0      )	->   int:





      '''simple docstring'''






      if batch_first:
       UpperCamelCase__       :     List[Any] 							= 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:
       UpperCamelCase__       :     List[str] 							= 1 - attention_mask

      # get token embeddings from IDs
      UpperCamelCase__       :     str 							= self.embedding_layer(_lowercase      )

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

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



class    __magic_name__						(  nn.Module):


     def __init__(   self				:  Tuple				,						lowerCamelCase__				:  Optional[int]				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  str      )	->   int:





      '''simple docstring'''






      super().__init__()
      UpperCamelCase__       :     List[str] 							= MegaLM(_lowercase				,						_lowercase				,						_lowercase      )
      UpperCamelCase__       :     int 							= nn.Linear(mega_args.encoder_embed_dim				,						_lowercase      )
      UpperCamelCase__       :     Tuple 							= nn.Dropout(p=0.1      )


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  Union[str, Any]				,						lowerCamelCase__				:  List[Any]=True				,						lowerCamelCase__				:  Dict=0      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= self.mega(_lowercase				,						_lowercase				,						_lowercase				,						_lowercase      )
      return self.mlm_head(self.dropout(_lowercase      )      )



def       _a	(					SCREAMING_SNAKE_CASE						:					Dict     ,			SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					List[Any]  ):







 """simple docstring"""



 with open(os.path.join(_a     ,			'''model_args.pkl'''  )     ,			'''rb'''  ) as f:
  UpperCamelCase__       :     Optional[Any] 							= pkl.load(_a  )

 # load the original encoder
 UpperCamelCase__       :     int 							= 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
 UpperCamelCase__       :     Dict 							= 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     ,			)

 UpperCamelCase__       :     List[Any] 							= 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
 UpperCamelCase__       :     str 							= 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
 UpperCamelCase__       :     Dict 							= original_mlm.mega.encoders.state_dict()
 UpperCamelCase__       :     Union[str, Any] 							= {}
 for module_name in original_state_dict.keys():
  UpperCamelCase__       :     Union[str, Any] 							= 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:
    UpperCamelCase__       :     int 							= module_name.replace('''move.beta'''     ,			'''ema_gate.ema_expansion_matrix'''  )
   elif "mega_layer.beta" in module_name:
    UpperCamelCase__       :     Dict 							= module_name.replace('''beta'''     ,			'''qk_bias'''  )
   else:
    UpperCamelCase__       :     List[str] 							= 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:
    UpperCamelCase__       :     List[Any] 							= module_name.replace('''move.gamma'''     ,			'''ema_gate.kernel_projection_matrix'''  )
   elif "mega_layer.gamma" in module_name:
    UpperCamelCase__       :     Union[str, Any] 							= module_name.replace('''gamma'''     ,			'''qk_weight'''  )
   else:
    UpperCamelCase__       :     Tuple 							= module_name.replace('''gamma'''     ,			'''g_param'''  )
        # alpha is used in EMA and positional bias; renaming to improve readability
  elif "move.alpha" in module_name:
   UpperCamelCase__       :     str 							= 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:
   UpperCamelCase__       :     int 							= module_name.replace('''move.delta'''     ,			'''ema_gate.damping_factor'''  )
  # omega is only used in EMA; renaming to improve readability
  elif "omega" in module_name:
   UpperCamelCase__       :     int 							= module_name.replace('''move.omega'''     ,			'''ema_gate.residual_weight'''  )

  if new_module_name:
   UpperCamelCase__       :     str 							= 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():
  UpperCamelCase__       :     Dict 							= 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
 UpperCamelCase__       :     Union[str, Any] 							= torch.randint(0     ,			hf_config.vocab_size     ,			size=(4, 256)  )
 UpperCamelCase__       :     List[str] 							= torch.ones_like(_a  )
 # mask a few tokens to make sure masking is applied appropriately :)
 UpperCamelCase__       :     str 							= 0

 # run forward passes
 UpperCamelCase__       :     Any 							= original_mlm(_a     ,			_a     ,			batch_first=_a     ,			ignore_mask_value=0  )
 UpperCamelCase__       :     Union[str, Any] 							= 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
 UpperCamelCase__       :     List[Any] 							= 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'''  )
  UpperCamelCase__       :     List[Any] 							= AutoTokenizer.from_pretrained(_a  )
  tokenizer.save_pretrained(_a  )


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









import math





class   lowercase__     :







  def __init__(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		len(_lowercase    )
       lowerCAmelCase_       :							Union[str, Any]			 =		[0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1     ,    0     ,    self.N - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2 + 1







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if l == r:
            lowerCAmelCase_       :							Dict			 =		A[l]
       else:
            lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
            self.build(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase    )
            self.build(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase    )
            lowerCAmelCase_       :							Dict			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       return self.update_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return True
       if l == r:
            lowerCAmelCase_       :							List[str]			 =		val
            return True
       lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
       self.update_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       self.update_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )
       return True







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       return self.query_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return -math.inf
       if l >= a and r <= b:
            return self.st[idx]
       lowerCAmelCase_       :							int			 =		(l + r) // 2
       lowerCAmelCase_       :							List[Any]			 =		self.query_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[str]			 =		self.query_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       return max(_lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[Any]			 =		[]
       for i in range(1     ,    N + 1    ):
            show_list += [self.query(_lowercase     ,    _lowercase    )]
       print(_lowercase    )


if __name__ == "__main__":
      UpperCAmelCase_       : Any							=    [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
      UpperCAmelCase_       : Optional[Any]							=    15
      UpperCAmelCase_       : List[Any]							=    SegmentTree(A)
      print(segt.query(4, 6))
      print(segt.query(7, 11))
      print(segt.query(7, 12))
      segt.update(1, 3, 1_11)
      print(segt.query(1, 15))
      segt.update(7, 8, 2_35)
      segt.show_data()
