






"""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 						snake_case  (  nn.Module       ):





    def __init__(							self	:					Dict				,		A	:					Optional[int]				,		A	:					Dict				,		A	:					int			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					int				,		A	:					Dict				,		A	:					Optional[Any]=True				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if batch_first:
        a     :			Union[str, 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:
        a     :			List[Any]				=   1 - attention_mask

      # get token embeddings from IDs
      a     :			Dict				=   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:
        a     :			Any				=   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 						snake_case  (  nn.Module       ):





    def __init__(							self	:					List[Any]				,		A	:					Tuple				,		A	:					List[Any]				,		A	:					Tuple			):


      '''simple docstring'''




      super().__init__()
      a     :			Tuple				=   MegaLM(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      a     :			Optional[int]				=   nn.Linear(mega_args.encoder_embed_dim				,		_SCREAMING_SNAKE_CASE			)
      a     :			int				=   nn.Dropout(p=0.1			)





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					Tuple				,		A	:					int				,		A	:					List[Any]=True				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      a     :			Optional[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 			snake_case  (A_      :List[str]	,			A_      :Any	,			A_      :str      ):







  '''simple docstring'''

  with open(os.path.join(_UpperCamelCase	,			'model_args.pkl'      )	,			'rb'      ) as f:
    a     :			List[Any]				=   pkl.load(_UpperCamelCase      )

  # load the original encoder
  a     :			Any				=   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     :			List[Any]				=   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     :			str				=   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     :			Union[str, Any]				=   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     :			List[Any]				=   original_mlm.mega.encoders.state_dict()
  a     :			Any				=   {}
  for module_name in original_state_dict.keys():
    a     :			List[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:
        a     :			int				=   module_name.replace('move.beta'	,			'ema_gate.ema_expansion_matrix'      )
      elif "mega_layer.beta" in module_name:
        a     :			str				=   module_name.replace('beta'	,			'qk_bias'      )
      else:
        a     :			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:
        a     :			List[Any]				=   module_name.replace('move.gamma'	,			'ema_gate.kernel_projection_matrix'      )
      elif "mega_layer.gamma" in module_name:
        a     :			Optional[Any]				=   module_name.replace('gamma'	,			'qk_weight'      )
      else:
        a     :			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:
      a     :			Dict				=   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     :			Tuple				=   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     :			List[str]				=   module_name.replace('move.omega'	,			'ema_gate.residual_weight'      )

    if new_module_name:
      a     :			Union[str, Any]				=   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     :			str				=   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     :			int				=   torch.randint(0	,			hf_config.vocab_size	,			size=(4, 2_5_6)      )
  a     :			List[str]				=   torch.ones_like(_UpperCamelCase      )
  # mask a few tokens to make sure masking is applied appropriately :)
  a     :			Any				=   0

  # run forward passes
  a     :			str				=   original_mlm(_UpperCamelCase	,			_UpperCamelCase	,			batch_first=_UpperCamelCase	,			ignore_mask_value=0      )
  a     :			Tuple				=   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     :			List[str]				=   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     :			Optional[Any]				=   AutoTokenizer.from_pretrained(_UpperCamelCase      )
    tokenizer.save_pretrained(_UpperCamelCase      )


if __name__ == "__main__":
   _UpperCamelCase				:    int     =						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				:    Dict     =						parser.parse_args()

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





'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






