





# 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    __UpperCamelCase      (    a,     a,     a=0) ->Optional[Any]:
					# Format the message.
					if name is None:
										lowerCamelCase__       =       None
					else:
										lowerCamelCase__       =       "." * max(0,     spaces - 2) + "# {:" + str(50 - spaces) + "s}"
										lowerCamelCase__       =       fmt.format(a)

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





def    __UpperCamelCase      (    a,     a,     a,     a,     a) ->int:
					# 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__       =       param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										lowerCamelCase__       =       (num_heads, hidden_size, num_splits) + input_shape[1:]
										lowerCamelCase__       =       param.view(*a)
										lowerCamelCase__       =       param.transpose(0,     2)
										lowerCamelCase__       =       param.transpose(1,     2).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										lowerCamelCase__       =       (num_heads, num_splits, hidden_size) + input_shape[1:]
										lowerCamelCase__       =       param.view(*a)
										lowerCamelCase__       =       param.transpose(0,     1).contiguous()
					lowerCamelCase__       =       param.view(*a)
					return param





def    __UpperCamelCase      (    a,     a,     a) ->List[str]:
					# The converted output model.
					lowerCamelCase__       =       {}

					# old versions did not store training args
					lowerCamelCase__       =       input_state_dict.get("args",     a)
					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__       =       ds_args.padded_vocab_size
										lowerCamelCase__       =       ds_args.max_position_embeddings
										lowerCamelCase__       =       ds_args.hidden_size
										lowerCamelCase__       =       ds_args.num_layers
										lowerCamelCase__       =       ds_args.num_attention_heads
										lowerCamelCase__       =       ds_args.ffn_hidden_size
										# pprint(config)

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

					# The model.
					lowerCamelCase__       =       input_state_dict["model"]
					# The language model.
					lowerCamelCase__       =       model["language_model"]
					# The embeddings.
					lowerCamelCase__       =       lm["embedding"]

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

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

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

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

					# The simple map of names for "automated" rules.
					lowerCamelCase__       =       {
					    "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__       =       layer_re.match(a)

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

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

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

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

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

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

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

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

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

					# The final layernorm.
					lowerCamelCase__       =       transformer["final_layernorm.weight"]
					lowerCamelCase__       =       transformer["final_layernorm.bias"]

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

					# It should be done!
					return output_state_dict





def    __UpperCamelCase      (    ) ->Dict:
					# Create the argument parser.
					lowerCamelCase__       =       argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure",     action="store_true")
					parser.add_argument(
					    "path_to_checkpoint",     type=a,     help="Path to the checkpoint file (.zip archive or direct .pt file)",     )
					parser.add_argument(
					    "--config_file",     default="",     type=a,     help="An optional config json file describing the pre-trained model.",     )
					lowerCamelCase__       =       parser.parse_args()

					# Extract the basename.
					lowerCamelCase__       =       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__       =       torch.load(a,     map_location="cpu")
					else:
										lowerCamelCase__       =       torch.load(args.path_to_checkpoint,     map_location="cpu")

					lowerCamelCase__       =       input_state_dict.get("args",     a)

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

										# Spell out all parameters in case the defaults change.
										lowerCamelCase__       =       GPTaConfig(
										    vocab_size=50257,     n_positions=1024,     n_embd=1024,     n_layer=24,     n_head=16,     n_inner=4096,     activation_function=a,     resid_pdrop=0.1,     embd_pdrop=0.1,     attn_pdrop=0.1,     layer_norm_epsilon=1E-5,     initializer_range=0.0_2,     summary_type="cls_index",     summary_use_proj=a,     summary_activation=a,     summary_proj_to_labels=a,     summary_first_dropout=0.1,     scale_attn_weights=a,     use_cache=a,     bos_token_id=50256,     eos_token_id=50256,     )
					else:
										lowerCamelCase__       =       GPTaConfig.from_json_file(args.config_file)

					lowerCamelCase__       =       ["GPT2LMHeadModel"]

					# Convert.
					print("Converting")
					lowerCamelCase__       =       convert_megatron_checkpoint(a,     a,     a)

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

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

					lowerCamelCase__       =       AutoTokenizer.from_pretrained(a)
					lowerCamelCase__       =       type(a).__name__
					lowerCamelCase__       =       tokenizer_class

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

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

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


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

if __name__ == "__main__":
						main()

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








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

