code: 
  
  
  '''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 :	str,					a_ :	Optional[Any],					a_ :	Tuple,					a_ :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  				UpperCamelCase__ 						=						mega_args
  				UpperCamelCase__ 						=						nn.Embedding(a_,					self.mega_args.encoder_embed_dim					)
  				UpperCamelCase__ 						=						nn.ModuleList([MegaEncoderLayer(self.mega_args					) for _ in range(a_					)]					)
  				UpperCamelCase__ 						=						depth
  
  	def 		lowercase_						( self :	Optional[Any],					a_ :	List[str],					a_ :	Any,					a_ :	Any=True,					a_ :	int=0					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if batch_first:
  							UpperCamelCase__ 						=						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:
  							UpperCamelCase__ 						=						1 - attention_mask
  
  				# get token embeddings from IDs
  				UpperCamelCase__ 						=						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:
  							UpperCamelCase__ 						=						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 :	str,					a_ :	List[Any],					a_ :	Any,					a_ :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  				UpperCamelCase__ 						=						MegaLM(a_,					a_,					a_					)
  				UpperCamelCase__ 						=						nn.Linear(mega_args.encoder_embed_dim,					a_					)
  				UpperCamelCase__ 						=						nn.Dropout(p=0.1					)
  
  	def 		lowercase_						( self :	Dict,					a_ :	Tuple,					a_ :	str,					a_ :	int=True,					a_ :	int=0					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.mega(a_,					a_,					a_,					a_					)
  				return self.mlm_head(self.dropout(a_					)					)
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				List[Any]   )		->					int:
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			with open(os.path.join(_UpperCamelCase    ,		"model_args.pkl"   )    ,		"rb"   ) as f:
  						UpperCamelCase__ 						=						pkl.load(_UpperCamelCase   )
  
  			# load the original encoder
  			UpperCamelCase__ 						=						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
  			UpperCamelCase__ 						=						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    ,		)
  
  			UpperCamelCase__ 						=						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
  			UpperCamelCase__ 						=						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
  			UpperCamelCase__ 						=						original_mlm.mega.encoders.state_dict()
  			UpperCamelCase__ 						=						{}
  			for module_name in original_state_dict.keys():
  						UpperCamelCase__ 						=						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:
  												UpperCamelCase__ 						=						module_name.replace("move.beta"    ,		"ema_gate.ema_expansion_matrix"   )
  									elif "mega_layer.beta" in module_name:
  												UpperCamelCase__ 						=						module_name.replace("beta"    ,		"qk_bias"   )
  									else:
  												UpperCamelCase__ 						=						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:
  												UpperCamelCase__ 						=						module_name.replace("move.gamma"    ,		"ema_gate.kernel_projection_matrix"   )
  									elif "mega_layer.gamma" in module_name:
  												UpperCamelCase__ 						=						module_name.replace("gamma"    ,		"qk_weight"   )
  									else:
  												UpperCamelCase__ 						=						module_name.replace("gamma"    ,		"g_param"   )
          # alpha is used in EMA and positional bias; renaming to improve readability
  						elif "move.alpha" in module_name:
  									UpperCamelCase__ 						=						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:
  									UpperCamelCase__ 						=						module_name.replace("move.delta"    ,		"ema_gate.damping_factor"   )
  						# omega is only used in EMA; renaming to improve readability
  						elif "omega" in module_name:
  									UpperCamelCase__ 						=						module_name.replace("move.omega"    ,		"ema_gate.residual_weight"   )
  
  						if new_module_name:
  									UpperCamelCase__ 						=						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():
  						UpperCamelCase__ 						=						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
  			UpperCamelCase__ 						=						torch.randint(0    ,		hf_config.vocab_size    ,		size=(4, 2_56)   )
  			UpperCamelCase__ 						=						torch.ones_like(_UpperCamelCase   )
  			# mask a few tokens to make sure masking is applied appropriately :)
  			UpperCamelCase__ 						=						0
  
  			# run forward passes
  			UpperCamelCase__ 						=						original_mlm(_UpperCamelCase    ,		_UpperCamelCase    ,		batch_first=_UpperCamelCase    ,		ignore_mask_value=0   )
  			UpperCamelCase__ 						=						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
  			UpperCamelCase__ 						=						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"   )
  						UpperCamelCase__ 						=						AutoTokenizer.from_pretrained(_UpperCamelCase   )
  						tokenizer.save_pretrained(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
  							__lowercase:		Dict							=     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",
  							)
  
  							__lowercase:		List[Any]							=     parser.parse_args()
  
  							convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."