
'''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	:Union[str, Any]						,					_A	:List[Any]						,					_A	:Tuple						,					_A	:List[str]      )					->      int:







       '''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 						lowercase_    (      self	:Optional[Any]						,					_A	:int						,					_A	:Dict						,					_A	:int=True						,					_A	:Tuple=0      )					->      Tuple:







       '''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 			UpperCamelCase__	(     nn.Module):






 def __init__(      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Tuple						,					_A	:str      )					->      Optional[Any]:







       '''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 						lowercase_    (      self	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:Any						,					_A	:Union[str, Any]=True						,					_A	:List[str]=0      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.mega(_A						,					_A						,					_A						,					_A      )
       return self.mlm_head(self.dropout(_A      )      )





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      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_5_6)						)
      __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__":
 a__		: 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",
 )

 a__		: Tuple  							=							parser.parse_args()

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










import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





