
# 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  :  List[Any]    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  Tuple=0				):




			"""simple docstring"""


			if name is None:
						lowerCAmelCase_						=	None
			else:
						lowerCAmelCase_						=	"." * max(0    ,							spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
						lowerCAmelCase_						=	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  :  str    ,							__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Optional[int]    ,							__lowerCAmelCase  :  List[str]				):




			"""simple docstring"""


			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(*__lowerCAmelCase				)
						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(*__lowerCAmelCase				)
						lowerCAmelCase_						=	param.transpose(0    ,							1				).contiguous()
			lowerCAmelCase_						=	param.view(*__lowerCAmelCase				)
			return param






def    lowerCamelCase__				(				__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Optional[Any]    ,							__lowerCAmelCase  :  Union[str, Any]				):




			"""simple docstring"""


			lowerCAmelCase_						=	{}

			# old versions did not store training args
			lowerCAmelCase_						=	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))

						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(__lowerCAmelCase				)

						# 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    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
									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(__lowerCAmelCase    ,							__lowerCAmelCase    ,							3    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
									# 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(__lowerCAmelCase    ,							__lowerCAmelCase    ,							3    ,							__lowerCAmelCase    ,							__lowerCAmelCase				)
									# 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    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	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."    ,							)
			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(__lowerCAmelCase    ,							map_location="cpu"				)
			else:
						lowerCAmelCase_						=	torch.load(args.path_to_checkpoint    ,							map_location="cpu"				)

			lowerCAmelCase_						=	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:
												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=__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=50256    ,							eos_token_id=50256    ,							)
			else:
						lowerCAmelCase_						=	GPTaConfig.from_json_file(args.config_file				)

			lowerCAmelCase_						=	["GPT2LMHeadModel"]

			# Convert.
			print("Converting"				)
			lowerCAmelCase_						=	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:
						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(__lowerCAmelCase				)
			lowerCAmelCase_						=	type(__lowerCAmelCase				).__name__
			lowerCAmelCase_						=	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.
			lowerCAmelCase_						=	os.path.join(__lowerCAmelCase    ,							"pytorch_model.bin"				)
			print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""				)
			torch.save(__lowerCAmelCase    ,							__lowerCAmelCase				)


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

if __name__ == "__main__":
			main()

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''megatron-bert'''




				def __init__(					self				,				_UpperCamelCase=29_056				,				_UpperCamelCase=1_024				,				_UpperCamelCase=24				,				_UpperCamelCase=16				,				_UpperCamelCase=4_096				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-1_2				,				_UpperCamelCase=0				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      int:
							super().__init__(pad_token_id=_UpperCamelCase				,				**_UpperCamelCase     )

							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	position_embedding_type
							lowerCAmelCase_						=	use_cache



