





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			:int					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:List[str]				)->      Any:
         super().__init__()
         __A      =       mega_args
         __A      =       nn.Embedding(_UpperCamelCase					,     self.mega_args.encoder_embed_dim				)
         __A      =       nn.ModuleList([MegaEncoderLayer(self.mega_args				) for _ in range(_UpperCamelCase				)]				)
         __A      =       depth






   def _lowerCAmelCase   (self			:str					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:List[str]=True					,     _UpperCamelCase			:Any=0				)->      Union[str, Any]:

         # Mega expects embeddings to be (time, batch, embedding size), but
         # Hugging Face returns tokens as (batch, time)
         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(_UpperCamelCase				)

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

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





class A_      (    nn.Module     ):




   def __init__(self			:List[str]					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:Any					,     _UpperCamelCase			:Tuple				)->      Optional[int]:
         super().__init__()
         __A      =       MegaLM(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)
         __A      =       nn.Linear(mega_args.encoder_embed_dim					,     _UpperCamelCase				)
         __A      =       nn.Dropout(p=0.1				)






   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:List[str]=True					,     _UpperCamelCase			:int=0				)->      Optional[int]:
         __A      =       self.mega(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)
         return self.mlm_head(self.dropout(_UpperCamelCase				)				)





def _a       (			lowerCamelCase:     Tuple      ,       lowerCamelCase:     List[Any]      ,       lowerCamelCase:     Any				)			->		Dict:







      '''simple docstring'''






      with open(os.path.join(lowerCamelCase      ,       '''model_args.pkl'''				)      ,       '''rb'''				) as f:
            __A      =       pkl.load(lowerCamelCase				)

      # load the original encoder
      __A      =       OriginalMegaForMaskedLM(**lowerCamelCase				).eval()

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

      __A      =       MegaForMaskedLM(lowerCamelCase				).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(lowerCamelCase				) != 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(lowerCamelCase				)

      # 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(lowerCamelCase				)				)

      # load the MLM head weights directly
      print(
          '''HF Mega MLM layer:'''      ,       hf_mlm.mlm_head.load_state_dict(
              torch.load(os.path.join(lowerCamelCase      ,       '''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(lowerCamelCase				)
      # mask a few tokens to make sure masking is applied appropriately :)
      __A      =       0

      # run forward passes
      __A      =       original_mlm(lowerCamelCase      ,       lowerCamelCase      ,       batch_first=lowerCamelCase      ,       ignore_mask_value=0				)
      __A      =       hf_mlm(lowerCamelCase      ,       lowerCamelCase				)[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(lowerCamelCase      ,       lowerCamelCase      ,       atol=1e-3				)

      if success:
            print('''Yay!'''				)
            hf_mlm.save_pretrained(lowerCamelCase				)
      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(lowerCamelCase				)
            tokenizer.save_pretrained(lowerCamelCase				)


if __name__ == "__main__":
       snake_case__      :							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',
       )

       snake_case__      :							Tuple									=  parser.parse_args()

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












from __future__ import annotations



def _a       (			lowerCamelCase:     list[list[int]]      ,       lowerCamelCase:     int      ,       lowerCamelCase:     int      ,       lowerCamelCase:     int | float				)			->		None:







      '''simple docstring'''






      __A     ,		__A      =       m - 1, 0
      while i >= 0 and j < n:
            if key == mat[i][j]:
                  print(F"""Key {key} found at row- {i + 1} column- {j + 1}"""				)
                  return
            if key < mat[i][j]:
                  i -= 1
            else:
                  j += 1
      print(F"""Key {key} not found"""				)



def _a       (			)			->		None:







      '''simple docstring'''






      __A      =       [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
      __A      =       int(input('''Enter the element to be searched:'''				)				)
      print(lowerCamelCase				)
      search_in_a_sorted_matrix(lowerCamelCase      ,       len(lowerCamelCase				)      ,       len(mat[0]				)      ,       lowerCamelCase				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()





