


"""simple docstring"""
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def 							_snake_case			(      __snake_case       :			List[str]	,   __snake_case       :			Optional[Any]	,   __snake_case       :			List[str]=0      ):




   """simple docstring"""







   if name is None:
      _lowerCamelCase  :				Optional[Any]      =		None
   else:
      _lowerCamelCase  :				List[Any]      =		""".""" * max(0	,   spaces - 2      ) + """# {:""" + str(50 - spaces      ) + """s}"""
      _lowerCamelCase  :				Tuple      =		fmt.format(__snake_case      )

   # Print and recurse (if needed).
   if isinstance(__snake_case	,   __snake_case      ):
      if msg is not None:
         print(__snake_case      )
      for k in val.keys():
         recursive_print(__snake_case	,   val[k]	,   spaces + 2      )
   elif isinstance(__snake_case	,   torch.Tensor      ):
      print(__snake_case	,   """:"""	,   val.size()      )
   else:
      print(__snake_case	,   """:"""	,   __snake_case      )

def 							_snake_case			(      __snake_case       :			Tuple	,   __snake_case       :			Optional[Any]	,   __snake_case       :			int	,   __snake_case       :			int      ):




   """simple docstring"""







   _lowerCamelCase  :				Optional[int]      =		param.size()
   # other versions store [num_heads * num_splits * hidden_size, :]
   _lowerCamelCase  :				Union[str, Any]      =		(num_heads, num_splits, hidden_size) + input_shape[1:]
   _lowerCamelCase  :				Dict      =		param.view(*__snake_case      )
   _lowerCamelCase  :				Dict      =		param.transpose(0	,   1      ).contiguous()
   _lowerCamelCase  :				Optional[int]      =		param.view(*__snake_case      )
   return param

def 							_snake_case			(      __snake_case       :			Tuple	,   __snake_case       :			Tuple      ):




   """simple docstring"""







   _lowerCamelCase  :				Any      =		config.n_positions
   _lowerCamelCase  :				Tuple      =		config.n_layer
   _lowerCamelCase  :				Tuple      =		config.vocab_size
   _lowerCamelCase  :				Any      =		config.n_head
   _lowerCamelCase  :				Dict      =		config.n_embd // config.n_head

   _lowerCamelCase  :				Tuple      =		sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
   _lowerCamelCase  :				Optional[int]      =		{
       """transformer.wte.weight""": word_embeddings,
       """transformer.wpe.weight""": sd_megatron["""model.language_model.embedding.position_embeddings.weight"""],
       """transformer.ln_f.weight""": sd_megatron["""model.language_model.encoder.final_layernorm.weight"""],
       """transformer.ln_f.bias""": sd_megatron["""model.language_model.encoder.final_layernorm.bias"""],
   }

   _lowerCamelCase  :				Tuple      =		"""model.language_model.encoder.layers."""
   for i in range(__snake_case      ):
      _lowerCamelCase  :				Union[str, Any]      =		torch.tril(torch.ones((n_positions, n_positions)	,   dtype=torch.bool      )      )
      _lowerCamelCase  :				Optional[Any]      =		causal_mask.view(1	,   1	,   __snake_case	,   __snake_case      )
      _lowerCamelCase  :				Dict      =		causal_mask
      _lowerCamelCase  :				Tuple      =		torch.tensor(-1E4	,   dtype=torch.bfloataa      )

      _lowerCamelCase  :				Optional[int]      =		sd_megatron[F'{pf}{i}.input_layernorm.weight']
      _lowerCamelCase  :				Tuple      =		sd_megatron[F'{pf}{i}.input_layernorm.bias']

      _lowerCamelCase  :				List[str]      =		sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
      _lowerCamelCase  :				List[Any]      =		fix_query_key_value_ordering(__snake_case	,   3	,   __snake_case	,   __snake_case      )
      _lowerCamelCase  :				List[Any]      =		vala.transpose(0	,   1      ).contiguous()

      _lowerCamelCase  :				Dict      =		sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
      _lowerCamelCase  :				Dict      =		fix_query_key_value_ordering(__snake_case	,   3	,   __snake_case	,   __snake_case      )
      _lowerCamelCase  :				List[Any]      =		vala

      _lowerCamelCase  :				List[Any]      =		sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
          0	,   1      )
      _lowerCamelCase  :				str      =		sd_megatron[F'{pf}{i}.self_attention.dense.bias']
      _lowerCamelCase  :				List[str]      =		sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
      _lowerCamelCase  :				Any      =		sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
      _lowerCamelCase  :				str      =		sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0	,   1      )
      _lowerCamelCase  :				Tuple      =		sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
      _lowerCamelCase  :				List[Any]      =		sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
          0	,   1      )
      _lowerCamelCase  :				List[str]      =		sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

   # For LM head, transformers' wants the matrix to weight embeddings.
   _lowerCamelCase  :				Any      =		word_embeddings

   return sd_hf

def 							_snake_case			(      __snake_case       :			Optional[Any]	,   __snake_case       :			str      ):




   """simple docstring"""







   _lowerCamelCase  :				Union[str, Any]      =		64000
   _lowerCamelCase  :				Dict      =		config_megatron["""encoder_seq_length"""]
   _lowerCamelCase  :				Dict      =		config_megatron["""hidden_size"""]
   _lowerCamelCase  :				Union[str, Any]      =		config_megatron["""num_layers"""]
   _lowerCamelCase  :				str      =		config_megatron["""num_attention_heads"""]
   _lowerCamelCase  :				Union[str, Any]      =		config_megatron["""ffn_hidden_size"""]
   _lowerCamelCase  :				Optional[Any]      =		"""gelu"""
   _lowerCamelCase  :				int      =		0.1
   _lowerCamelCase  :				Optional[Any]      =		0.1
   _lowerCamelCase  :				Optional[int]      =		0.1
   _lowerCamelCase  :				Optional[int]      =		config_megatron["""layernorm_epsilon"""]  # 1e-5
   _lowerCamelCase  :				Dict      =		config_megatron["""init_method_std"""]  # 0.02
   _lowerCamelCase  :				Tuple      =		config_megatron["""apply_query_key_layer_scaling"""]  # True
   _lowerCamelCase  :				Union[str, Any]      =		True
   _lowerCamelCase  :				Optional[Any]      =		True

   # This identifies the 6.7B (7B) model which uses a different tokenizer
   if config_megatron["hidden_size"] == 4096:
      _lowerCamelCase  :				str      =		1  # <|endoftext|>
      _lowerCamelCase  :				Optional[Any]      =		1  # <|endoftext|>
      _lowerCamelCase  :				List[Any]      =		0  # <unk>
   else:
      _lowerCamelCase  :				int      =		2  # <s>
      _lowerCamelCase  :				Tuple      =		3  # <|endoftext|>
      _lowerCamelCase  :				Optional[int]      =		0  # <pad>

   return config_hf

def 							_snake_case			(      __snake_case       :			str      ):




   """simple docstring"""







   print(__snake_case      )

   _lowerCamelCase  :				Any      =		args.checkpoint_path
   _lowerCamelCase  :				Tuple      =		args.save_path
   if isfile(__snake_case      ):
      raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'      )

   # Load the model.
   _lowerCamelCase  :				Tuple      =		torch.load(__snake_case	,   map_location="""cpu"""      )

   # Load the config.
   _lowerCamelCase  :				Optional[Any]      =		checkpoint["""hyper_parameters"""]["""cfg"""]
   _lowerCamelCase  :				str      =		GPTaConfig()
   _lowerCamelCase  :				int      =		copy_config(config_hf=__snake_case	,   config_megatron=__snake_case      )
   _lowerCamelCase  :				Tuple      =		["""GPT2LMHeadModel"""]

   _lowerCamelCase  :				Union[str, Any]      =		checkpoint["""state_dict"""]

   # Convert.
   print("""Converting"""      )
   _lowerCamelCase  :				Any      =		convert_megatron_checkpoint(__snake_case	,   __snake_case      )

   # Print the structure of converted state dict.
   if args.print_checkpoint_structure:
      recursive_print(__snake_case	,   __snake_case      )

   _lowerCamelCase  :				Optional[Any]      =		"""GPTSw3Tokenizer"""

   # Store the config to file.
   print("""Saving config"""      )
   config_hf.save_pretrained(__snake_case      )

   # Store the state_dict to file.
   _lowerCamelCase  :				List[Any]      =		os.path.join(__snake_case	,   """pytorch_model.bin"""      )
   print(F'Saving checkpoint to "{output_checkpoint_file}"'      )
   torch.save(__snake_case	,   __snake_case      )


if __name__ == "__main__":
      UpperCAmelCase   =	argparse.ArgumentParser()
      parser.add_argument(
          """--checkpoint_path""",
          type=str,
          required=True,
          help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
      )
      parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
      parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
      UpperCAmelCase   =	parser.parse_args()
      main(_args)





"""simple docstring"""
from dataclasses import dataclass

UpperCAmelCase   =	"""0.1"""
UpperCAmelCase   =	"""Lucia Harcekova"""




@dataclass
class   lowercase__							:
   __UpperCAmelCase       					=				42
   __UpperCAmelCase       					=				42
   __UpperCAmelCase       					=				42





   def __repr__(       self)     ->     str:
      return F'({self.offset}, {self.length}, {self.indicator})'







class   lowercase__							:





   def __init__(       self			,      SCREAMING_SNAKE_CASE = 13			,      SCREAMING_SNAKE_CASE = 6)     ->     None:
      _lowerCamelCase  :				Optional[Any]      =		window_size
      _lowerCamelCase  :				List[str]      =		lookahead_buffer_size
      _lowerCamelCase  :				List[Any]      =		self.window_size - self.lookahead_buffer_size





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     list[Token]:
      _lowerCamelCase  :				Union[str, Any]      =		[]
      _lowerCamelCase  :				Tuple      =		""""""

      # 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)
         _lowerCamelCase  :				List[Any]      =		self._find_encoding_token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)

         # 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(SCREAMING_SNAKE_CASE) > self.search_buffer_size:
            _lowerCamelCase  :				int      =		search_buffer[-self.search_buffer_size :]

         # update the text
         _lowerCamelCase  :				Tuple      =		text[token.length + 1 :]

         # append the token to output
         output.append(SCREAMING_SNAKE_CASE)

      return output





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     str:
      _lowerCamelCase  :				str      =		""""""

      for token in tokens:
         for _ in range(token.length):
            output += output[-token.offset]
         output += token.indicator

      return output





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Token:

      if not text:
         raise ValueError("""We need some text to work with.""")

      # Initialise result parameters to default values
      _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		0, 0

      if not search_buffer:
         return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])

      for i, character in enumerate(SCREAMING_SNAKE_CASE):
         _lowerCamelCase  :				Tuple      =		len(SCREAMING_SNAKE_CASE) - i
         if character == text[0]:
            _lowerCamelCase  :				Any      =		self._match_length_from_index(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      0			,      SCREAMING_SNAKE_CASE)
            # 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:
               _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		found_offset, found_length

      return Token(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      text[length])





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     int:
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          SCREAMING_SNAKE_CASE			,      window + text[text_index]			,      text_index + 1			,      window_index + 1)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 # Initialize compressor class
 UpperCAmelCase   =	LZaaCompressor(window_size=13, lookahead_buffer_size=6)

 # Example
 UpperCAmelCase   =	"""cabracadabrarrarrad"""
 UpperCAmelCase   =	lzaa_compressor.compress(TEXT)
 print(lzaa_compressor.compress("""ababcbababaa"""))
 UpperCAmelCase   =	lzaa_compressor.decompress(compressed_text)
 assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."

