






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_=0  )   ->  Tuple:


	"""simple docstring"""



	if name is None:
		a		=			None
	else:
		a		=			'''.''' * max(0,  spaces - 2  ) + '''# {:''' + str(5_0 - spaces  ) + '''s}'''
		a		=			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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  Union[str, Any]:


	"""simple docstring"""



	a		=			param.size()
	# other versions store [num_heads * num_splits * hidden_size, :]
	a		=			(num_heads, num_splits, hidden_size) + input_shape[1:]
	a		=			param.view(*snake_case_  )
	a		=			param.transpose(0,  1  ).contiguous()
	a		=			param.view(*snake_case_  )
	return param





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  Tuple:


	"""simple docstring"""



	a		=			config.n_positions
	a		=			config.n_layer
	a		=			config.vocab_size
	a		=			config.n_head
	a		=			config.n_embd // config.n_head

	a		=			sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
	a		=			{
	    '''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'''],
	}

	a		=			'''model.language_model.encoder.layers.'''
	for i in range(snake_case_  ):
		a		=			torch.tril(torch.ones((n_positions, n_positions),  dtype=torch.bool  )  )
		a		=			causal_mask.view(1,  1,  snake_case_,  snake_case_  )
		a		=			causal_mask
		a		=			torch.tensor(-1e4,  dtype=torch.bfloataa  )

		a		=			sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
		a		=			sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

		a		=			sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
		a		=			fix_query_key_value_ordering(snake_case_,  3,  snake_case_,  snake_case_  )
		a		=			vala.transpose(0,  1  ).contiguous()

		a		=			sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
		a		=			fix_query_key_value_ordering(snake_case_,  3,  snake_case_,  snake_case_  )
		a		=			vala

		a		=			sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
		    0,  1  )
		a		=			sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
		a		=			sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
		a		=			sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
		a		=			sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0,  1  )
		a		=			sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
		a		=			sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
		    0,  1  )
		a		=			sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

	return sd_hf





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  int:


	"""simple docstring"""



	a		=			6_4_0_0_0
	a		=			config_megatron['''encoder_seq_length''']
	a		=			config_megatron['''hidden_size''']
	a		=			config_megatron['''num_layers''']
	a		=			config_megatron['''num_attention_heads''']
	a		=			config_megatron['''ffn_hidden_size''']
	a		=			'''gelu'''
	a		=			0.1
	a		=			0.1
	a		=			0.1
	a		=			config_megatron['''layernorm_epsilon''']  # 1e-5
	a		=			config_megatron['''init_method_std''']  # 0.02
	a		=			config_megatron['''apply_query_key_layer_scaling''']  # True
	a		=			True
	a		=			True

	# This identifies the 6.7B (7B) model which uses a different tokenizer
	if config_megatron["hidden_size"] == 4_0_9_6:
		a		=			1  # <|endoftext|>
		a		=			1  # <|endoftext|>
		a		=			0  # <unk>
	else:
		a		=			2  # <s>
		a		=			3  # <|endoftext|>
		a		=			0  # <pad>

	return config_hf





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Optional[int]:


	"""simple docstring"""



	print(snake_case_  )

	a		=			args.checkpoint_path
	a		=			args.save_path
	if isfile(snake_case_  ):
		raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""  )

	# Load the model.
	a		=			torch.load(snake_case_,  map_location='''cpu'''  )

	# Load the config.
	a		=			checkpoint['''hyper_parameters''']['''cfg''']
	a		=			GPTaConfig()
	a		=			copy_config(config_hf=snake_case_,  config_megatron=snake_case_  )
	a		=			['''GPT2LMHeadModel''']

	a		=			checkpoint['''state_dict''']

	# Convert.
	print('''Converting'''  )
	a		=			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_  )

	a		=			'''GPTSw3Tokenizer'''

	# Store the config to file.
	print('''Saving config'''  )
	config_hf.save_pretrained(snake_case_  )

	# Store the state_dict to file.
	a		=			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__						:      Optional[int]								=					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__						:      Optional[Any]								=					parser.parse_args()
				main(_args)













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



								return F"""{self.letter}:{self.freq}"""







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) as f:
		while True:
			a		=			f.read(1  )
			if not c:
				break
			a		=			chars[c] + 1 if c in chars else 1
	return sorted((Letter(snake_case_,  snake_case_  ) for c, f in chars.items()),  key=lambda snake_case_  : x.freq  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



	a		=			letters  # type: ignore
	while len(snake_case_  ) > 1:
		a		=			response.pop(0  )
		a		=			response.pop(0  )
		a		=			left.freq + right.freq
		a		=			TreeNode(snake_case_,  snake_case_,  snake_case_  )
		response.append(snake_case_  )
		response.sort(key=lambda snake_case_  : x.freq  )
	return response[0]





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	if isinstance(snake_case_,  snake_case_  ):
		a		=			bitstring
		return [root]
	a		=			root  # type: ignore
	a		=			[]
	letters += traverse_tree(treenode.left,  bitstring + '''0'''  )
	letters += traverse_tree(treenode.right,  bitstring + '''1'''  )
	return letters





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    k: v for letter in traverse_tree(snake_case_,  ''''''  ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """  )
	with open(snake_case_  ) as f:
		while True:
			a		=			f.read(1  )
			if not c:
				break
			print(letters[c],  end=''' '''  )
	print()


if __name__ == "__main__":
				# pass the file path to the huffman function
				huffman(sys.argv[1])





