
"""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_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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase=0       )				-> Optional[int]:
	# Format the message.
	if name is None:
		_lowerCAmelCase								=None
	else:
		_lowerCAmelCase								=""".""" * max(0    , spaces - 2       ) + """# {:""" + str(50 - spaces       ) + """s}"""
		_lowerCAmelCase								=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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	# 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 BERT.
	_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(*__UpperCamelCase       )
		_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(*__UpperCamelCase       )
		_lowerCAmelCase								=param.transpose(0    , 1       ).contiguous()
	_lowerCAmelCase								=param.view(*__UpperCamelCase       )
	return param



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Optional[int]:
	# The converted output model.
	_lowerCAmelCase								={}

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

		_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(__UpperCamelCase       )

		# 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(__UpperCamelCase    , __UpperCamelCase    , 3    , __UpperCamelCase    , __UpperCamelCase       )
			# 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(__UpperCamelCase    , __UpperCamelCase    , 3    , __UpperCamelCase    , __UpperCamelCase       )
			# 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     _lowerCamelCase()				-> Optional[Any]:
	# Create the argument parser.
	_lowerCAmelCase								=argparse.ArgumentParser()
	parser.add_argument("""--print-checkpoint-structure"""    , action="""store_true"""       )
	parser.add_argument("""path_to_checkpoint"""    , type=__UpperCamelCase    , help="""Path to the ZIP file containing the checkpoint"""       )
	parser.add_argument(
	    """--config_file"""    , default=""""""    , type=__UpperCamelCase    , 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(__UpperCamelCase    , 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(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )

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

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

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


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

if __name__ == "__main__":
			main()

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


