

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A	:       Any        =						{
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Dict        =						['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       str        =						[
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	__A	:       Union[str, Any]        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











# 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   A		(__A  :       List[Any]  ,							__A  :       str  ,							__A  :       Optional[int]=0			)  -> Dict:







     """simple docstring"""


     if name is None:
          UpperCAmelCase_		=				None
     else:
          UpperCAmelCase_		=				'''.''' * max(0  ,							spaces - 2			) + '''# {:''' + str(50 - spaces			) + '''s}'''
          UpperCAmelCase_		=				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   A		(__A  :       Any  ,							__A  :       str  ,							__A  :       Tuple  ,							__A  :       Optional[int]  ,							__A  :       Any			)  -> Union[str, Any]:







     """simple docstring"""


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


def   A		(__A  :       Optional[Any]  ,							__A  :       Tuple  ,							__A  :       Optional[int]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				{}

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

          UpperCAmelCase_		=				ds_args.tokenizer_type
          UpperCAmelCase_		=				ds_args.padded_vocab_size
          UpperCAmelCase_		=				ds_args.max_position_embeddings
          UpperCAmelCase_		=				ds_args.hidden_size
          UpperCAmelCase_		=				ds_args.num_layers
          UpperCAmelCase_		=				ds_args.num_attention_heads
          UpperCAmelCase_		=				ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

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

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

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

     # The position embeddings.
     UpperCAmelCase_		=				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.
     UpperCAmelCase_		=				pos_embeddings

     # The token-type embeddings.
     UpperCAmelCase_		=				embeddings['''tokentype_embeddings''']['''weight''']
     # Store the position embeddings.
     UpperCAmelCase_		=				tokentype_embeddings

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

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

     # The simple map of names for "automated" rules.
     UpperCAmelCase_		=				{
         '''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.
     UpperCAmelCase_		=				None

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

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

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

          # The name of the layer.
          UpperCAmelCase_		=				F"""bert.encoder.layer.{layer_idx}"""

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''			):
               UpperCAmelCase_		=				'''attention.ln''' if op_name.startswith('''input'''			) else '''ln'''
               UpperCAmelCase_		=				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, ""

               UpperCAmelCase_		=				fix_query_key_value_ordering(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               UpperCAmelCase_		=				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.
               UpperCAmelCase_		=				attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

               # Clear the stored tensor.
               UpperCAmelCase_		=				None

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

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

     # The pooler.
     UpperCAmelCase_		=				lm['''pooler''']

     # Store the matrix and the bias.
     UpperCAmelCase_		=				pooler['''dense.weight''']
     UpperCAmelCase_		=				pooler['''dense.bias''']

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

     # The transform matrix.
     UpperCAmelCase_		=				lm_head['''dense.weight''']
     UpperCAmelCase_		=				lm_head['''dense.bias''']

     # The transform LN.
     UpperCAmelCase_		=				lm_head['''layernorm.weight''']
     UpperCAmelCase_		=				lm_head['''layernorm.bias''']

     # For the decoder, we replicate the weights.
     UpperCAmelCase_		=				word_embeddings
     UpperCAmelCase_		=				lm_head['''bias''']

     # The classifier from Megatron (for MLNI).
     UpperCAmelCase_		=				model['''binary_head''']

     # Store the classifier.
     UpperCAmelCase_		=				binary_head['''weight''']
     UpperCAmelCase_		=				binary_head['''bias''']

     # It should be done!
     return output_state_dict


def   A		()  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure'''  ,							action='''store_true'''			)
     parser.add_argument('''path_to_checkpoint'''  ,							type=__A  ,							help='''Path to the ZIP file containing the checkpoint'''			)
     parser.add_argument(
         '''--config_file'''  ,							default=''''''  ,							type=__A  ,							help='''An optional config json file describing the pre-trained model.'''  ,							)
     UpperCAmelCase_		=				parser.parse_args()

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

     if args.config_file == "":
          # Default config of megatron-bert 345m
          UpperCAmelCase_		=				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
          UpperCAmelCase_		=				input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
     else:
          UpperCAmelCase_		=				MegatronBertConfig.from_json_file(args.config_file			)

     # Convert.
     print('''Converting'''			)
     UpperCAmelCase_		=				convert_megatron_checkpoint(__A  ,							__A  ,							__A			)

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

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

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


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

if __name__ == "__main__":
    main()

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






