

"""simple docstring"""


import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=0			)     ->     Any:
		# Format the message.
		if name is None:
				__lowerCAmelCase:	str   				=      None
		else:
				__lowerCAmelCase:	Optional[Any]   				=      "." * max(0 ,			spaces - 2			) + "# {:" + str(5_0 - spaces			) + "s}"
				__lowerCAmelCase:	Dict   				=      fmt.format(__SCREAMING_SNAKE_CASE			)

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Tuple:
		# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
		# for compatibility with later versions of NVIDIA Megatron-LM.
		# The inverse operation is performed inside Megatron-LM to read checkpoints:
		# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
		# If param is the weight tensor of the self-attention block, the returned tensor
		# will have to be transposed one more time to be read by HuggingFace GPT2.
		__lowerCAmelCase:	Union[str, Any]   				=      param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				__lowerCAmelCase:	Union[str, Any]   				=      (num_heads, hidden_size, num_splits) + input_shape[1:]
				__lowerCAmelCase:	Optional[int]   				=      param.view(*__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[int]   				=      param.transpose(0 ,			2			)
				__lowerCAmelCase:	Tuple   				=      param.transpose(1 ,			2			).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				__lowerCAmelCase:	List[Any]   				=      (num_heads, num_splits, hidden_size) + input_shape[1:]
				__lowerCAmelCase:	int   				=      param.view(*__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	str   				=      param.transpose(0 ,			1			).contiguous()
		__lowerCAmelCase:	Optional[Any]   				=      param.view(*__SCREAMING_SNAKE_CASE			)
		return param






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		# The converted output model.
		__lowerCAmelCase:	Union[str, Any]   				=      {}

		# old versions did not store training args
		__lowerCAmelCase:	str   				=      input_state_dict.get("args" ,			__SCREAMING_SNAKE_CASE			)
		if ds_args is not None:
				# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
				# from pprint import pprint
				# pprint(vars(ds_args))

				__lowerCAmelCase:	Union[str, Any]   				=      ds_args.padded_vocab_size
				__lowerCAmelCase:	Dict   				=      ds_args.max_position_embeddings
				__lowerCAmelCase:	Dict   				=      ds_args.hidden_size
				__lowerCAmelCase:	Tuple   				=      ds_args.num_layers
				__lowerCAmelCase:	Any   				=      ds_args.num_attention_heads
				__lowerCAmelCase:	List[str]   				=      ds_args.ffn_hidden_size
				# pprint(config)

		# The number of heads.
		__lowerCAmelCase:	List[str]   				=      config.n_head
		# The hidden_size per head.
		__lowerCAmelCase:	Any   				=      config.n_embd // config.n_head
		# Megatron-LM checkpoint version
		if "checkpoint_version" in input_state_dict.keys():
				__lowerCAmelCase:	List[Any]   				=      input_state_dict["checkpoint_version"]
		else:
				__lowerCAmelCase:	Union[str, Any]   				=      0.0

		# The model.
		__lowerCAmelCase:	Union[str, Any]   				=      input_state_dict["model"]
		# The language model.
		__lowerCAmelCase:	Any   				=      model["language_model"]
		# The embeddings.
		__lowerCAmelCase:	Tuple   				=      lm["embedding"]

		# The word embeddings.
		__lowerCAmelCase:	Optional[int]   				=      embeddings["word_embeddings"]["weight"]
		# Truncate the embedding table to vocab_size rows.
		__lowerCAmelCase:	List[Any]   				=      word_embeddings[: config.vocab_size, :]
		__lowerCAmelCase:	Any   				=      word_embeddings

		# The position embeddings.
		__lowerCAmelCase:	str   				=      embeddings["position_embeddings"]["weight"]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		__lowerCAmelCase:	Tuple   				=      pos_embeddings.size(0			)
		if n_positions != config.n_positions:
				raise ValueError(
				    F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"			)
		# Store the position embeddings.
		__lowerCAmelCase:	List[str]   				=      pos_embeddings

		# The transformer.
		__lowerCAmelCase:	str   				=      lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

		# The regex to extract layer names.
		__lowerCAmelCase:	Any   				=      re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"			)

		# The simple map of names for "automated" rules.
		__lowerCAmelCase:	Tuple   				=      {
		    "attention.dense": ".attn.c_proj.",
		    "self_attention.dense": ".attn.c_proj.",
		    "mlp.dense_h_to_4h": ".mlp.c_fc.",
		    "mlp.dense_4h_to_h": ".mlp.c_proj.",
		}

		# Extract the layers.
		for key, val in transformer.items():
				# Match the name.
				__lowerCAmelCase:	Union[str, Any]   				=      layer_re.match(__SCREAMING_SNAKE_CASE			)

				# Stop if that's not a layer
				if m is None:
						break

				# The index of the layer.
				__lowerCAmelCase:	Optional[int]   				=      int(m.group(1			)			)
				# The name of the operation.
				__lowerCAmelCase:	int   				=      m.group(2			)
				# Is it a weight or a bias?
				__lowerCAmelCase:	int   				=      m.group(3			)

				# The name of the layer.
				__lowerCAmelCase:	List[str]   				=      F"transformer.h.{layer_idx}"

				# For layernorm(s), simply store the layer norm.
				if op_name.endswith("layernorm"			):
						__lowerCAmelCase:	Union[str, Any]   				=      "ln_1" if op_name.startswith("input"			) else "ln_2"
						__lowerCAmelCase:	int   				=      val

				# Transpose the QKV matrix.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "weight":
						# Insert a tensor of 1x1xDxD bias.
						__lowerCAmelCase:	List[Any]   				=      torch.tril(torch.ones((n_positions, n_positions) ,			dtype=torch.floataa			)			).view(
						    1 ,			1 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						__lowerCAmelCase:	List[Any]   				=      causal_mask

						# Insert a "dummy" tensor for masked_bias.
						__lowerCAmelCase:	Optional[Any]   				=      torch.tensor(-1E4 ,			dtype=torch.floataa			)
						__lowerCAmelCase:	Any   				=      masked_bias

						__lowerCAmelCase:	Optional[Any]   				=      fix_query_key_value_ordering(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			3 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
						__lowerCAmelCase:	str   				=      out_val.transpose(0 ,			1			).contiguous()
						# Store.
						__lowerCAmelCase:	Dict   				=      out_val

				# Transpose the bias.
				elif (
				    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
				) and weight_or_bias == "bias":
						__lowerCAmelCase:	Union[str, Any]   				=      fix_query_key_value_ordering(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			3 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
						# Store. No change of shape.
						__lowerCAmelCase:	Optional[int]   				=      out_val

				# Transpose the weights.
				elif weight_or_bias == "weight":
						__lowerCAmelCase:	List[str]   				=      megatron_to_transformers[op_name]
						__lowerCAmelCase:	List[Any]   				=      val.transpose(0 ,			1			)

				# Copy the bias.
				elif weight_or_bias == "bias":
						__lowerCAmelCase:	str   				=      megatron_to_transformers[op_name]
						__lowerCAmelCase:	List[str]   				=      val

    # DEBUG.
		assert config.n_layer == layer_idx + 1

		# The final layernorm.
		__lowerCAmelCase:	Tuple   				=      transformer["final_layernorm.weight"]
		__lowerCAmelCase:	Union[str, Any]   				=      transformer["final_layernorm.bias"]

		# For LM head, transformers' wants the matrix to weight embeddings.
		__lowerCAmelCase:	List[str]   				=      word_embeddings

		# It should be done!
		return output_state_dict






def    a__   (   )     ->     Tuple:
		# Create the argument parser.
		__lowerCAmelCase:	List[str]   				=      argparse.ArgumentParser()
		parser.add_argument("--print-checkpoint-structure" ,			action="store_true"			)
		parser.add_argument(
		    "path_to_checkpoint" ,			type=__SCREAMING_SNAKE_CASE ,			help="Path to the checkpoint file (.zip archive or direct .pt file)" ,			)
		parser.add_argument(
		    "--config_file" ,			default="" ,			type=__SCREAMING_SNAKE_CASE ,			help="An optional config json file describing the pre-trained model." ,			)
		__lowerCAmelCase:	Tuple   				=      parser.parse_args()

		# Extract the basename.
		__lowerCAmelCase:	Optional[int]   				=      os.path.dirname(args.path_to_checkpoint			)

		# Load the model.
		# the .zip is very optional, let's keep it for backward compatibility
		print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"			)
		if args.path_to_checkpoint.endswith(".zip"			):
				with zipfile.ZipFile(args.path_to_checkpoint ,			"r"			) as checkpoint:
						with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"			) as pytorch_dict:
								__lowerCAmelCase:	List[str]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)
		else:
				__lowerCAmelCase:	Dict   				=      torch.load(args.path_to_checkpoint ,			map_location="cpu"			)

		__lowerCAmelCase:	Any   				=      input_state_dict.get("args" ,			__SCREAMING_SNAKE_CASE			)

		# Read the config, or default to the model released by NVIDIA.
		if args.config_file == "":
				if ds_args is not None:
						if ds_args.bias_gelu_fusion:
								__lowerCAmelCase:	Union[str, Any]   				=      "gelu_fast"
						elif ds_args.openai_gelu:
								__lowerCAmelCase:	int   				=      "gelu_new"
						else:
								__lowerCAmelCase:	Dict   				=      "gelu"
				else:
						# in the very early days this used to be "gelu_new"
						__lowerCAmelCase:	Union[str, Any]   				=      "gelu_new"

				# Spell out all parameters in case the defaults change.
				__lowerCAmelCase:	List[Any]   				=      GPTaConfig(
				    vocab_size=5_0_2_5_7 ,			n_positions=1_0_2_4 ,			n_embd=1_0_2_4 ,			n_layer=2_4 ,			n_head=1_6 ,			n_inner=4_0_9_6 ,			activation_function=__SCREAMING_SNAKE_CASE ,			resid_pdrop=0.1 ,			embd_pdrop=0.1 ,			attn_pdrop=0.1 ,			layer_norm_epsilon=1E-5 ,			initializer_range=0.02 ,			summary_type="cls_index" ,			summary_use_proj=__SCREAMING_SNAKE_CASE ,			summary_activation=__SCREAMING_SNAKE_CASE ,			summary_proj_to_labels=__SCREAMING_SNAKE_CASE ,			summary_first_dropout=0.1 ,			scale_attn_weights=__SCREAMING_SNAKE_CASE ,			use_cache=__SCREAMING_SNAKE_CASE ,			bos_token_id=5_0_2_5_6 ,			eos_token_id=5_0_2_5_6 ,			)
		else:
				__lowerCAmelCase:	Union[str, Any]   				=      GPTaConfig.from_json_file(args.config_file			)

		__lowerCAmelCase:	str   				=      ["GPT2LMHeadModel"]

		# Convert.
		print("Converting"			)
		__lowerCAmelCase:	str   				=      convert_megatron_checkpoint(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

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

		# Add tokenizer class info to config
		# see https://github.com/huggingface/transformers/issues/13906)
		if ds_args is not None:
				__lowerCAmelCase:	Tuple   				=      ds_args.tokenizer_type
				if tokenizer_type == "GPT2BPETokenizer":
						__lowerCAmelCase:	List[Any]   				=      "gpt2"
				elif tokenizer_type == "PretrainedFromHF":
						__lowerCAmelCase:	Any   				=      ds_args.tokenizer_name_or_path
				else:
						raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"			)
		else:
				__lowerCAmelCase:	Dict   				=      "gpt2"

		__lowerCAmelCase:	List[Any]   				=      AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      type(__SCREAMING_SNAKE_CASE			).__name__
		__lowerCAmelCase:	Dict   				=      tokenizer_class

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

		# Save tokenizer based on args
		print(F"Adding {tokenizer_class} tokenizer files"			)
		tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE			)

		# Store the state_dict to file.
		__lowerCAmelCase:	Any   				=      os.path.join(__SCREAMING_SNAKE_CASE ,			"pytorch_model.bin"			)
		print(F"Saving checkpoint to \"{output_checkpoint_file}\""			)
		torch.save(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


####################################################################################################

if __name__ == "__main__":
					main()

####################################################################################################





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


