
'''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 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Dict								=		mega_args
    SCREAMING_SNAKE_CASE  :  List[str]								=		nn.Embedding(__SCREAMING_SNAKE_CASE    ,self.mega_args.encoder_embed_dim      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(__SCREAMING_SNAKE_CASE      )]      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		depth






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0      ):

    # Mega expects embeddings to be (time, batch, embedding size), but
    # Hugging Face returns tokens as (batch, time)
    if batch_first:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		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:
     SCREAMING_SNAKE_CASE  :  Dict								=		1 - attention_mask

    # get token embeddings from IDs
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.embedding_layer(__SCREAMING_SNAKE_CASE      )

    # pass through the Mega layers
    # input is (time, batch, encoder dim) and output is the same
    for encoder in self.encoders:
     SCREAMING_SNAKE_CASE  :  List[str]								=		encoder(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

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



class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		MegaLM(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.Linear(mega_args.encoder_embed_dim    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		nn.Dropout(p=0.1      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.mega(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
    return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE      )      )



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict   ,     snake_case_				:	Any   ,     snake_case_				:	List[Any]							)       ->	Dict:
 with open(os.path.join(snake_case_   ,     'model_args.pkl'							)   ,     'rb'							) as f:
  SCREAMING_SNAKE_CASE  :  Any								=		pkl.load(snake_case_							)

 # load the original encoder
 SCREAMING_SNAKE_CASE  :  Any								=		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
 SCREAMING_SNAKE_CASE  :  str								=		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_   ,     )

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

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

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


if __name__ == "__main__":
   __UpperCAmelCase												=   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												=   parser.parse_args()

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




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if number != int(snake_case_							):
  raise ValueError('factorial() only accepts integral values'							)
 if number < 0:
  raise ValueError('factorial() not defined for negative values'							)
 SCREAMING_SNAKE_CASE  :  str								=		1
 for i in range(1   ,     number + 1							):
  value *= i
 return value



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if not isinstance(snake_case_   ,     snake_case_							):
  raise ValueError('factorial() only accepts integral values'							)
 if n < 0:
  raise ValueError('factorial() not defined for negative values'							)
 return 1 if n == 0 or n == 1 else n * factorial(n - 1							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __UpperCAmelCase												=   int(input('Enter a positive integer: ').strip() or 0)
   print(f"""factorial{n} is {factorial(n)}""")


