





'''simple docstring'''



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig




def  _lowerCAmelCase    (					lowerCamelCase_      :	Union[str, Any]		,							lowerCamelCase_      :	str		,							lowerCamelCase_      :	Union[str, Any]=0					):







			if name is None:
						__lowercase					    =				None
			else:
						__lowercase					    =				'''.''' * max(0		,							spaces - 2					) + '''# {:''' + str(5_0 - spaces					) + '''s}'''
						__lowercase					    =				fmt.format(lowerCamelCase_					)

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




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




def  _lowerCAmelCase    (					lowerCamelCase_      :	Union[str, Any]		,							lowerCamelCase_      :	Optional[Any]		,							lowerCamelCase_      :	Dict					):
			__lowercase					    =				{}

			# old versions did not store training args
			__lowercase					    =				input_state_dict.get('''args'''		,							lowerCamelCase_					)
			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					    =				ds_args.padded_vocab_size
						__lowercase					    =				ds_args.max_position_embeddings
						__lowercase					    =				ds_args.hidden_size
						__lowercase					    =				ds_args.num_layers
						__lowercase					    =				ds_args.num_attention_heads
						__lowercase					    =				ds_args.ffn_hidden_size
						# pprint(config)

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

			# The model.
			__lowercase					    =				input_state_dict['''model''']
			# The language model.
			__lowercase					    =				model['''language_model''']
			# The embeddings.
			__lowercase					    =				lm['''embedding''']

			# The word embeddings.
			__lowercase					    =				embeddings['''word_embeddings''']['''weight''']
			# Truncate the embedding table to vocab_size rows.
			__lowercase					    =				word_embeddings[: config.vocab_size, :]
			__lowercase					    =				word_embeddings

			# The position embeddings.
			__lowercase					    =				embeddings['''position_embeddings''']['''weight''']
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			__lowercase					    =				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					    =				pos_embeddings

			# The transformer.
			__lowercase					    =				lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

			# The simple map of names for "automated" rules.
			__lowercase					    =				{
			    '''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					    =				layer_re.match(lowerCamelCase_					)

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

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

						# The name of the layer.
						__lowercase					    =				f"transformer.h.{layer_idx}"

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('''layernorm'''					):
									__lowercase					    =				'''ln_1''' if op_name.startswith('''input'''					) else '''ln_2'''
									__lowercase					    =				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					    =				torch.tril(torch.ones((n_positions, n_positions)		,							dtype=torch.floataa					)					).view(
									    1		,							1		,							lowerCamelCase_		,							lowerCamelCase_					)
									__lowercase					    =				causal_mask

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

									__lowercase					    =				fix_query_key_value_ordering(lowerCamelCase_		,							lowerCamelCase_		,							3		,							lowerCamelCase_		,							lowerCamelCase_					)
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									__lowercase					    =				out_val.transpose(0		,							1					).contiguous()
									# Store.
									__lowercase					    =				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					    =				fix_query_key_value_ordering(lowerCamelCase_		,							lowerCamelCase_		,							3		,							lowerCamelCase_		,							lowerCamelCase_					)
									# Store. No change of shape.
									__lowercase					    =				out_val

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

						# Copy the bias.
						elif weight_or_bias == "bias":
									__lowercase					    =				megatron_to_transformers[op_name]
									__lowercase					    =				val

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

			# The final layernorm.
			__lowercase					    =				transformer['''final_layernorm.weight''']
			__lowercase					    =				transformer['''final_layernorm.bias''']

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

			# It should be done!
			return output_state_dict




def  _lowerCAmelCase    (					):
			__lowercase					    =				argparse.ArgumentParser()
			parser.add_argument('''--print-checkpoint-structure'''		,							action='''store_true'''					)
			parser.add_argument(
			    '''path_to_checkpoint'''		,							type=lowerCamelCase_		,							help='''Path to the checkpoint file (.zip archive or direct .pt file)'''		,							)
			parser.add_argument(
			    '''--config_file'''		,							default=''''''		,							type=lowerCamelCase_		,							help='''An optional config json file describing the pre-trained model.'''		,							)
			__lowercase					    =				parser.parse_args()

			# Extract the basename.
			__lowercase					    =				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					    =				torch.load(lowerCamelCase_		,							map_location='''cpu'''					)
			else:
						__lowercase					    =				torch.load(args.path_to_checkpoint		,							map_location='''cpu'''					)

			__lowercase					    =				input_state_dict.get('''args'''		,							lowerCamelCase_					)

			# 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					    =				'''gelu_fast'''
									elif ds_args.openai_gelu:
												__lowercase					    =				'''gelu_new'''
									else:
												__lowercase					    =				'''gelu'''
						else:
									# in the very early days this used to be "gelu_new"
									__lowercase					    =				'''gelu_new'''

						# Spell out all parameters in case the defaults change.
						__lowercase					    =				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=lowerCamelCase_		,							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=lowerCamelCase_		,							summary_activation=lowerCamelCase_		,							summary_proj_to_labels=lowerCamelCase_		,							summary_first_dropout=0.1		,							scale_attn_weights=lowerCamelCase_		,							use_cache=lowerCamelCase_		,							bos_token_id=5_0_2_5_6		,							eos_token_id=5_0_2_5_6		,							)
			else:
						__lowercase					    =				GPTaConfig.from_json_file(args.config_file					)

			__lowercase					    =				['''GPT2LMHeadModel''']

			# Convert.
			print('''Converting'''					)
			__lowercase					    =				convert_megatron_checkpoint(lowerCamelCase_		,							lowerCamelCase_		,							lowerCamelCase_					)

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

			# Add tokenizer class info to config
			# see https://github.com/huggingface/transformers/issues/13906)
			if ds_args is not None:
						__lowercase					    =				ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									__lowercase					    =				'''gpt2'''
						elif tokenizer_type == "PretrainedFromHF":
									__lowercase					    =				ds_args.tokenizer_name_or_path
						else:
									raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}"					)
			else:
						__lowercase					    =				'''gpt2'''

			__lowercase					    =				AutoTokenizer.from_pretrained(lowerCamelCase_					)
			__lowercase					    =				type(lowerCamelCase_					).__name__
			__lowercase					    =				tokenizer_class

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

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

			# Store the state_dict to file.
			__lowercase					    =				os.path.join(lowerCamelCase_		,							'''pytorch_model.bin'''					)
			print(f"Saving checkpoint to \"{output_checkpoint_file}\""					)
			torch.save(lowerCamelCase_		,							lowerCamelCase_					)


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

if __name__ == "__main__":
	main()

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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      A_							       =		[]
      for char_count in range(SCREAMING_SNAKE_CASE   ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]   )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]   )
      return "".join(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)





