



# 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_bert/convert_megatron_bert_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 MegatronBertConfig


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case=0      ):





					"""simple docstring"""

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

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""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(*snake_case      )
										_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(*snake_case      )
										_lowerCAmelCase      =   param.transpose(0     ,						1      ).contiguous()
					_lowerCAmelCase      =   param.view(*snake_case      )
					return param


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   {}

					# old versions did not store training args
					_lowerCAmelCase      =   input_state_dict.get("""args"""     ,						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))

										_lowerCAmelCase      =   ds_args.tokenizer_type
										_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 if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					_lowerCAmelCase      =   config.num_attention_heads
					# The hidden_size per head.
					_lowerCAmelCase      =   config.hidden_size // heads
					# 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, :]
					# Store the word embeddings.
					_lowerCAmelCase      =   word_embeddings

					# The position embeddings.
					_lowerCAmelCase      =   embeddings["""position_embeddings"""]["""weight"""]
					assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
					# Store the position embeddings.
					_lowerCAmelCase      =   pos_embeddings

					# The token-type embeddings.
					_lowerCAmelCase      =   embeddings["""tokentype_embeddings"""]["""weight"""]
					# Store the position embeddings.
					_lowerCAmelCase      =   tokentype_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""": """.attention.output.dense.""",
					    """self_attention.dense""": """.attention.output.dense.""",
					    """mlp.dense_h_to_4h""": """.intermediate.dense.""",
					    """mlp.dense_4h_to_h""": """.output.dense.""",
					}

					# Keep track of the attention/query/value tensor.
					_lowerCAmelCase      =   None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										_lowerCAmelCase      =   layer_re.match(snake_case      )

										# 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'bert.encoder.layer.{layer_idx}'

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("""layernorm"""      ):
															_lowerCAmelCase      =   """attention.ln""" if op_name.startswith("""input"""      ) else """ln"""
															_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":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															_lowerCAmelCase      =   fix_query_key_value_ordering(snake_case     ,						snake_case     ,						3     ,						snake_case     ,						snake_case      )
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															_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":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															_lowerCAmelCase      =   attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															_lowerCAmelCase      =   attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															_lowerCAmelCase      =   attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															_lowerCAmelCase      =   fix_query_key_value_ordering(snake_case     ,						snake_case     ,						3     ,						snake_case     ,						snake_case      )
															# Split the bias.
															_lowerCAmelCase      =   out_val[0 * config.hidden_size : 1 * config.hidden_size]
															_lowerCAmelCase      =   out_val[1 * config.hidden_size : 2 * config.hidden_size]
															_lowerCAmelCase      =   out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															_lowerCAmelCase      =   q
															_lowerCAmelCase      =   q_bias
															_lowerCAmelCase      =   k
															_lowerCAmelCase      =   k_bias
															_lowerCAmelCase      =   v
															_lowerCAmelCase      =   v_bias

															# Clear the stored tensor.
															_lowerCAmelCase      =   None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															_lowerCAmelCase      =   megatron_to_transformers[op_name]
															_lowerCAmelCase      =   val

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

					# The pooler.
					_lowerCAmelCase      =   lm["""pooler"""]

					# Store the matrix and the bias.
					_lowerCAmelCase      =   pooler["""dense.weight"""]
					_lowerCAmelCase      =   pooler["""dense.bias"""]

					# The LM head from Megatron (for RACE).
					_lowerCAmelCase      =   model["""lm_head"""]

					# The transform matrix.
					_lowerCAmelCase      =   lm_head["""dense.weight"""]
					_lowerCAmelCase      =   lm_head["""dense.bias"""]

					# The transform LN.
					_lowerCAmelCase      =   lm_head["""layernorm.weight"""]
					_lowerCAmelCase      =   lm_head["""layernorm.bias"""]

					# For the decoder, we replicate the weights.
					_lowerCAmelCase      =   word_embeddings
					_lowerCAmelCase      =   lm_head["""bias"""]

					# The classifier from Megatron (for MLNI).
					_lowerCAmelCase      =   model["""binary_head"""]

					# Store the classifier.
					_lowerCAmelCase      =   binary_head["""weight"""]
					_lowerCAmelCase      =   binary_head["""bias"""]

					# It should be done!
					return output_state_dict


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   argparse.ArgumentParser()
					parser.add_argument("""--print-checkpoint-structure"""     ,						action="""store_true"""      )
					parser.add_argument("""path_to_checkpoint"""     ,						type=snake_case     ,						help="""Path to the ZIP file containing the checkpoint"""      )
					parser.add_argument(
					    """--config_file"""     ,						default=""""""     ,						type=snake_case     ,						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(snake_case     ,						map_location="""cpu"""      )
					else:
										_lowerCAmelCase      =   torch.load(args.path_to_checkpoint     ,						map_location="""cpu"""      )

					if args.config_file == "":
										# Default config of megatron-bert 345m
										_lowerCAmelCase      =   MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										_lowerCAmelCase      =   input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
					else:
										_lowerCAmelCase      =   MegatronBertConfig.from_json_file(args.config_file      )

					# Convert.
					print("""Converting"""      )
					_lowerCAmelCase      =   convert_megatron_checkpoint(snake_case     ,						snake_case     ,						snake_case      )

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

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

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


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

if __name__ == "__main__":
	main()

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








# Lint as: python3

import hashlib
from typing import Union


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if isinstance(snake_case     ,						snake_case      ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(snake_case     ,						snake_case      ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										_lowerCAmelCase      =   hash_data.replace("""\\"""     ,						"""/"""      )
					elif isinstance(snake_case     ,						snake_case      ):
										_lowerCAmelCase      =   str(snake_case      )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(snake_case      )

					return hash_data.encode("""utf-8"""      )





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   """\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
									_lowerCAmelCase      =   F'\nFound Key {hash_data} of type {type(_snake_case		)}'
									_lowerCAmelCase      =   """\nKeys should be either str, int or bytes type"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case=""		):



									"""simple docstring"""





									_lowerCAmelCase      =   key
									_lowerCAmelCase      =   duplicate_key_indices
									_lowerCAmelCase      =   fix_msg
									_lowerCAmelCase      =   """Found multiple examples generated with the same key"""
									if len(_snake_case		) <= 20:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(_snake_case		)} have the key {key}'
									else:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(duplicate_key_indices[:20]		)}... ({len(_snake_case		) - 20} more) have the key {key}'
									_lowerCAmelCase      =   """\n""" + fix_msg if fix_msg else """"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   hashlib.mda(_as_bytes(_snake_case		)		)




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self._split_mda.copy()
									_lowerCAmelCase      =   _as_bytes(_snake_case		)
									mda.update(_snake_case		)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()						,    16		)



