
'''simple docstring'''







import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]=0							):



      """simple docstring"""





      if name is None:
            lowercase_     :					int													=	None
      else:
            lowercase_     :					Dict													=	'''.''' * max(0				,				spaces - 2							) + '''# {:''' + str(50 - spaces							) + '''s}'''
            lowercase_     :					Any													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



      """simple docstring"""





      lowercase_     :					Tuple													=	param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowercase_     :					Any													=	(num_heads, hidden_size, num_splits) + input_shape[1:]
            lowercase_     :					Union[str, Any]													=	param.view(*__SCREAMING_SNAKE_CASE							)
            lowercase_     :					Union[str, Any]													=	param.transpose(0				,				2							)
            lowercase_     :					List[Any]													=	param.transpose(1				,				2							).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowercase_     :					List[Any]													=	(num_heads, num_splits, hidden_size) + input_shape[1:]
            lowercase_     :					str													=	param.view(*__SCREAMING_SNAKE_CASE							)
            lowercase_     :					Optional[Any]													=	param.transpose(0				,				1							).contiguous()
      lowercase_     :					List[str]													=	param.view(*__SCREAMING_SNAKE_CASE							)
      return param
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			List[Any]							):



      """simple docstring"""





      lowercase_     :					Optional[Any]													=	{}

      # old versions did not store training args
      lowercase_     :					Dict													=	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))

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

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

      # The model.
      lowercase_     :					str													=	input_state_dict['''model''']
      # The language model.
      lowercase_     :					Dict													=	model['''language_model''']
      # The embeddings.
      lowercase_     :					Union[str, Any]													=	lm['''embedding''']

      # The word embeddings.
      lowercase_     :					Tuple													=	embeddings['''word_embeddings''']['''weight''']
      # Truncate the embedding table to vocab_size rows.
      lowercase_     :					Optional[int]													=	word_embeddings[: config.vocab_size, :]
      lowercase_     :					Dict													=	word_embeddings

      # The position embeddings.
      lowercase_     :					Optional[int]													=	embeddings['''position_embeddings''']['''weight''']
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      lowercase_     :					str													=	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.
      lowercase_     :					List[Any]													=	pos_embeddings

      # The transformer.
      lowercase_     :					List[str]													=	lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

      # The simple map of names for "automated" rules.
      lowercase_     :					List[str]													=	{
          '''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.
            lowercase_     :					int													=	layer_re.match(__SCREAMING_SNAKE_CASE							)

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

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

            # The name of the layer.
            lowercase_     :					Union[str, Any]													=	F'''transformer.h.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('''layernorm'''							):
                  lowercase_     :					Dict													=	'''ln_1''' if op_name.startswith('''input'''							) else '''ln_2'''
                  lowercase_     :					Optional[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.
                  lowercase_     :					Optional[Any]													=	torch.tril(torch.ones((n_positions, n_positions)				,				dtype=torch.floataa							)							).view(
                      1				,				1				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
                  lowercase_     :					int													=	causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  lowercase_     :					Tuple													=	torch.tensor(-1E4				,				dtype=torch.floataa							)
                  lowercase_     :					Tuple													=	masked_bias

                  lowercase_     :					List[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.
                  lowercase_     :					List[Any]													=	out_val.transpose(0				,				1							).contiguous()
                  # Store.
                  lowercase_     :					int													=	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":
                  lowercase_     :					Optional[Any]													=	fix_query_key_value_ordering(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				3				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
                  # Store. No change of shape.
                  lowercase_     :					Any													=	out_val

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

            # Copy the bias.
            elif weight_or_bias == "bias":
                  lowercase_     :					Dict													=	megatron_to_transformers[op_name]
                  lowercase_     :					Optional[int]													=	val

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

      # The final layernorm.
      lowercase_     :					Optional[int]													=	transformer['''final_layernorm.weight''']
      lowercase_     :					str													=	transformer['''final_layernorm.bias''']

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

      # It should be done!
      return output_state_dict
def  snake_case_		(    ):



      """simple docstring"""





      lowercase_     :					int													=	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.'''				,				)
      lowercase_     :					List[str]													=	parser.parse_args()

      # Extract the basename.
      lowercase_     :					Dict													=	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:
                        lowercase_     :					str													=	torch.load(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)
      else:
            lowercase_     :					Any													=	torch.load(args.path_to_checkpoint				,				map_location='''cpu'''							)

      lowercase_     :					Optional[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:
                        lowercase_     :					Optional[int]													=	'''gelu_fast'''
                  elif ds_args.openai_gelu:
                        lowercase_     :					int													=	'''gelu_new'''
                  else:
                        lowercase_     :					List[Any]													=	'''gelu'''
            else:
                  # in the very early days this used to be "gelu_new"
                  lowercase_     :					str													=	'''gelu_new'''

            # Spell out all parameters in case the defaults change.
            lowercase_     :					Tuple													=	GPTaConfig(
                vocab_size=50257				,				n_positions=1024				,				n_embd=1024				,				n_layer=24				,				n_head=16				,				n_inner=4096				,				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=50256				,				eos_token_id=50256				,				)
      else:
            lowercase_     :					Any													=	GPTaConfig.from_json_file(args.config_file							)

      lowercase_     :					List[Any]													=	['''GPT2LMHeadModel''']

      # Convert.
      print('''Converting'''							)
      lowercase_     :					Tuple													=	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:
            lowercase_     :					List[Any]													=	ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  lowercase_     :					Union[str, Any]													=	'''gpt2'''
            elif tokenizer_type == "PretrainedFromHF":
                  lowercase_     :					Any													=	ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}'''							)
      else:
            lowercase_     :					int													=	'''gpt2'''

      lowercase_     :					Optional[int]													=	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE							)
      lowercase_     :					Tuple													=	type(__SCREAMING_SNAKE_CASE							).__name__
      lowercase_     :					str													=	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.
      lowercase_     :					Optional[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase			:  Any   							=	logging.get_logger(__name__)

_lowercase			:  Dict   							=	{
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					'''gpt_neox'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=5_04_32    ,							__SCREAMING_SNAKE_CASE=61_44    ,							__SCREAMING_SNAKE_CASE=44    ,							__SCREAMING_SNAKE_CASE=64    ,							__SCREAMING_SNAKE_CASE=2_45_76    ,							__SCREAMING_SNAKE_CASE="gelu"    ,							__SCREAMING_SNAKE_CASE=0.25    ,							__SCREAMING_SNAKE_CASE=1_00_00    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20_48    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1E-5    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,							eos_token_id=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	vocab_size
											lowercase_     :					Optional[Any]													=	max_position_embeddings
											lowercase_     :					Optional[int]													=	hidden_size
											lowercase_     :					Tuple													=	num_hidden_layers
											lowercase_     :					Any													=	num_attention_heads
											lowercase_     :					str													=	intermediate_size
											lowercase_     :					Any													=	hidden_act
											lowercase_     :					Tuple													=	rotary_pct
											lowercase_     :					Optional[Any]													=	rotary_emb_base
											lowercase_     :					Any													=	attention_dropout
											lowercase_     :					str													=	hidden_dropout
											lowercase_     :					Dict													=	classifier_dropout
											lowercase_     :					Tuple													=	initializer_range
											lowercase_     :					List[str]													=	layer_norm_eps
											lowercase_     :					Union[str, Any]													=	use_cache
											lowercase_     :					int													=	tie_word_embeddings
											lowercase_     :					Tuple													=	use_parallel_residual
											lowercase_     :					Optional[Any]													=	rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											if self.rope_scaling is None:
																	return

											if not isinstance(self.rope_scaling    ,							__SCREAMING_SNAKE_CASE		) or len(self.rope_scaling		) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F'''got {self.rope_scaling}'''		)
											lowercase_     :					List[Any]													=	self.rope_scaling.get('''type'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.rope_scaling.get('''factor'''    ,							__SCREAMING_SNAKE_CASE		)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''		)
											if rope_scaling_factor is None or not isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''		)



