

# 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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=0					)							->     str:



		"""simple docstring"""






		if name is None:
				_snake_case						=							None
		else:
				_snake_case						=							'''.''' * max(0			,	spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
				_snake_case						=							fmt.format(_UpperCamelCase					)

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



		"""simple docstring"""






		_snake_case						=							param.size()
		if checkpoint_version == 1.0:
				# version 1.0 stores [num_heads * hidden_size * num_splits, :]
				_snake_case						=							(num_heads, hidden_size, num_splits) + input_shape[1:]
				_snake_case						=							param.view(*_UpperCamelCase					)
				_snake_case						=							param.transpose(0			,	2					)
				_snake_case						=							param.transpose(1			,	2					).contiguous()
		elif checkpoint_version >= 2.0:
				# other versions store [num_heads * num_splits * hidden_size, :]
				_snake_case						=							(num_heads, num_splits, hidden_size) + input_shape[1:]
				_snake_case						=							param.view(*_UpperCamelCase					)
				_snake_case						=							param.transpose(0			,	1					).contiguous()
		_snake_case						=							param.view(*_UpperCamelCase					)
		return param



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     int:



		"""simple docstring"""






		_snake_case						=							{}

		# old versions did not store training args
		_snake_case						=							input_state_dict.get('''args'''			,	_UpperCamelCase					)
		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))

				_snake_case						=							ds_args.padded_vocab_size
				_snake_case						=							ds_args.max_position_embeddings
				_snake_case						=							ds_args.hidden_size
				_snake_case						=							ds_args.num_layers
				_snake_case						=							ds_args.num_attention_heads
				_snake_case						=							ds_args.ffn_hidden_size
				# pprint(config)

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

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

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

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

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

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

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

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

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

				# The name of the layer.
				_snake_case						=							F"""transformer.h.{layer_idx}"""

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

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

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

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

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

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

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

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

		# It should be done!
		return output_state_dict



def 				snake_case_()							->     List[str]:



		"""simple docstring"""






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

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

		_snake_case						=							input_state_dict.get('''args'''			,	_UpperCamelCase					)

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

				# Spell out all parameters in case the defaults change.
				_snake_case						=							GPTaConfig(
				    vocab_size=50_257			,	n_positions=1_024			,	n_embd=1_024			,	n_layer=24			,	n_head=16			,	n_inner=4_096			,	activation_function=_UpperCamelCase			,	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=_UpperCamelCase			,	summary_activation=_UpperCamelCase			,	summary_proj_to_labels=_UpperCamelCase			,	summary_first_dropout=0.1			,	scale_attn_weights=_UpperCamelCase			,	use_cache=_UpperCamelCase			,	bos_token_id=50_256			,	eos_token_id=50_256			,	)
		else:
				_snake_case						=							GPTaConfig.from_json_file(args.config_file					)

		_snake_case						=							['''GPT2LMHeadModel''']

		# Convert.
		print('''Converting'''					)
		_snake_case						=							convert_megatron_checkpoint(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

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

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

		_snake_case						=							AutoTokenizer.from_pretrained(_UpperCamelCase					)
		_snake_case						=							type(_UpperCamelCase					).__name__
		_snake_case						=							tokenizer_class

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

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

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


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

if __name__ == "__main__":
	main()

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




from __future__ import annotations



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     bool:



  """simple docstring"""






  _snake_case						=							get_failure_array(_UpperCamelCase					)

  # 2) Step through text searching for pattern
  _snake_case,					_snake_case						=							0, 0  # index into text, pattern
  while i < len(_UpperCamelCase					):
    if pattern[j] == text[i]:
      if j == (len(_UpperCamelCase					) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _snake_case						=							failure[j - 1]
      continue
    i += 1
  return False



def 				snake_case_(_UpperCamelCase					)							->     list[int]:



  """simple docstring"""






  _snake_case						=							[0]
  _snake_case						=							0
  _snake_case						=							1
  while j < len(_UpperCamelCase					):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _snake_case						=							failure[i - 1]
      continue
    j += 1
    failure.append(_UpperCamelCase					)
  return failure


if __name__ == "__main__":
 # Test 1)
 __A      		=     '''abc1abc12'''
 __A      		=     '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 __A      		=     '''alskfjaldsk23adsfabcabc'''
 assert kmp(pattern, texta) and not kmp(pattern, texta)

 # Test 2)
 __A      		=     '''ABABX'''
 __A      		=     '''ABABZABABYABABX'''
 assert kmp(pattern, text)

 # Test 3)
 __A      		=     '''AAAB'''
 __A      		=     '''ABAAAAAB'''
 assert kmp(pattern, text)

 # Test 4)
 __A      		=     '''abcdabcy'''
 __A      		=     '''abcxabcdabxabcdabcdabcy'''
 assert kmp(pattern, text)

 # Test 5)
 __A      		=     '''aabaabaaa'''
 assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

