


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     __snake_case							(      nn.Module):
     def __init__(	self					:	str  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[int]     )       ->				Union[str, Any]:



           '''simple docstring'''





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







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



           '''simple docstring'''





           if batch_first:
                 __magic_name__ 		=			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:
                 __magic_name__ 		=			1 - attention_mask

           # get token embeddings from IDs
           __magic_name__ 		=			self.embedding_layer(_lowerCAmelCase     )

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

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







class     __snake_case							(      nn.Module):
     def __init__(	self					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	List[str]     )       ->				Any:



           '''simple docstring'''





           super().__init__()
           __magic_name__ 		=			MegaLM(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
           __magic_name__ 		=			nn.Linear(mega_args.encoder_embed_dim  ,			_lowerCAmelCase     )
           __magic_name__ 		=			nn.Dropout(p=0.1     )







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



           '''simple docstring'''





           __magic_name__ 		=			self.mega(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
           return self.mlm_head(self.dropout(_lowerCAmelCase     )     )







def 							UpperCamelCase_  (				__snake_case			:						Dict	,			__snake_case			:						Optional[Any]	,			__snake_case			:						Optional[Any]     )->      Optional[int]:




      '''simple docstring'''
      with open(os.path.join(__snake_case	,			'model_args.pkl'     )	,			'rb'     ) as f:
            __magic_name__ 		=			pkl.load(__snake_case     )

      # load the original encoder
      __magic_name__ 		=			OriginalMegaForMaskedLM(**__snake_case     ).eval()

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

      # create a new config from the old one
      __magic_name__ 		=			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=__snake_case	,			norm_affine=__snake_case	,			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=__snake_case	,			add_lm_hidden_dense_layer=__snake_case	,			)

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

            if new_module_name:
                  __magic_name__ 		=			new_module_name

      if len(__snake_case     ) != 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():
            __magic_name__ 		=			original_state_dict.pop(__snake_case     )

      # 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(__snake_case     )     )

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

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

      # run forward passes
      __magic_name__ 		=			original_mlm(__snake_case	,			__snake_case	,			batch_first=__snake_case	,			ignore_mask_value=0     )
      __magic_name__ 		=			hf_mlm(__snake_case	,			__snake_case     )[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
      __magic_name__ 		=			torch.allclose(__snake_case	,			__snake_case	,			atol=1E-3     )

      if success:
            print('Yay!'     )
            hf_mlm.save_pretrained(__snake_case     )
      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'     )
            __magic_name__ 		=			AutoTokenizer.from_pretrained(__snake_case     )
            tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
     UpperCamelCase :				List[Any]						    =       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 :				Optional[int]						    =       parser.parse_args()

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





def 							UpperCamelCase_  (				__snake_case			:						list[int]     )->      tuple[int, int]:




						'''simple docstring'''
						# base case
						if len(__snake_case     ) == 1:
												return a[0], a[0]
						else:
												# split A into half.
												__magic_name__ 		=			a[: len(__snake_case     ) // 2]
												__magic_name__ 		=			a[len(__snake_case     ) // 2 :]

												# 2 sub problems, 1/2 of original size.
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )

												# get min of first and max of second
												# linear time
												__magic_name__ 		=			min(__snake_case     )
												__magic_name__ 		=			max(__snake_case     )

												# 3 cases, either (small1, big1),
												# (min_first, max_second), (small2, big2)
												# constant comparisons
												if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																		return smalla, biga
												elif biga - smalla > max_second - min_first:
																		return smalla, biga
												else:
																		return min_first, max_second


if __name__ == "__main__":
					import doctest

					doctest.testmod()

