"""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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]=0						)    ->     Union[str, Any]:







     '''simple docstring'''


     if name is None:
          __A					:					str								=		None
     else:
          __A					:					Optional[Any]								=		"." * max(0  ,       spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
          __A					:					Dict								=		fmt.format(_SCREAMING_SNAKE_CASE						)

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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Optional[int]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     int:







     '''simple docstring'''


     __A					:					Any								=		{}

     # old versions did not store training args
     __A					:					List[Any]								=		input_state_dict.get('args'  ,       _SCREAMING_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))

          __A					:					int								=		ds_args.tokenizer_type
          __A					:					Any								=		ds_args.padded_vocab_size
          __A					:					Union[str, Any]								=		ds_args.max_position_embeddings
          __A					:					Optional[Any]								=		ds_args.hidden_size
          __A					:					Dict								=		ds_args.num_layers
          __A					:					int								=		ds_args.num_attention_heads
          __A					:					List[str]								=		ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

     # The number of heads.
     __A					:					str								=		config.num_attention_heads
     # The hidden_size per head.
     __A					:					Dict								=		config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __A					:					Optional[Any]								=		input_state_dict["checkpoint_version"]
     else:
          __A					:					Tuple								=		0.0

     # The model.
     __A					:					Any								=		input_state_dict["model"]
     # The language model.
     __A					:					Optional[Any]								=		model["language_model"]
     # The embeddings.
     __A					:					Optional[int]								=		lm["embedding"]

     # The word embeddings.
     __A					:					int								=		embeddings["word_embeddings"]["weight"]
     # Truncate the embedding table to vocab_size rows.
     __A					:					Union[str, Any]								=		word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     __A					:					Optional[Any]								=		word_embeddings

     # The position embeddings.
     __A					:					List[str]								=		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.
     __A					:					List[str]								=		pos_embeddings

     # The token-type embeddings.
     __A					:					Optional[int]								=		embeddings["tokentype_embeddings"]["weight"]
     # Store the position embeddings.
     __A					:					int								=		tokentype_embeddings

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

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

     # The simple map of names for "automated" rules.
     __A					:					int								=		{
         "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.
     __A					:					Tuple								=		None

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __A					:					Optional[int]								=		layer_re.match(_SCREAMING_SNAKE_CASE						)

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

          # The index of the layer.
          __A					:					Optional[Any]								=		int(m.group(1						)						)
          # The name of the operation.
          __A					:					Any								=		m.group(2						)
          # Is it a weight or a bias?
          __A					:					str								=		m.group(3						)

          # The name of the layer.
          __A					:					List[Any]								=		F'bert.encoder.layer.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'						):
               __A					:					str								=		"attention.ln" if op_name.startswith('input'						) else "ln"
               __A					:					Optional[int]								=		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, ""

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

               __A					:					Optional[int]								=		fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       3  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
               # Split the bias.
               __A					:					str								=		out_val[0 * config.hidden_size : 1 * config.hidden_size]
               __A					:					str								=		out_val[1 * config.hidden_size : 2 * config.hidden_size]
               __A					:					Tuple								=		out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               __A					:					List[Any]								=		q
               __A					:					List[Any]								=		q_bias
               __A					:					Dict								=		k
               __A					:					int								=		k_bias
               __A					:					List[str]								=		v
               __A					:					str								=		v_bias

               # Clear the stored tensor.
               __A					:					List[str]								=		None

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

    # The final layernorm.
     __A					:					List[str]								=		transformer["final_layernorm.weight"]
     __A					:					Any								=		transformer["final_layernorm.bias"]

     # The pooler.
     __A					:					Dict								=		lm["pooler"]

     # Store the matrix and the bias.
     __A					:					Tuple								=		pooler["dense.weight"]
     __A					:					int								=		pooler["dense.bias"]

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

     # The transform matrix.
     __A					:					List[str]								=		lm_head["dense.weight"]
     __A					:					List[Any]								=		lm_head["dense.bias"]

     # The transform LN.
     __A					:					Optional[Any]								=		lm_head["layernorm.weight"]
     __A					:					Optional[int]								=		lm_head["layernorm.bias"]

     # For the decoder, we replicate the weights.
     __A					:					str								=		word_embeddings
     __A					:					int								=		lm_head["bias"]

     # The classifier from Megatron (for MLNI).
     __A					:					List[Any]								=		model["binary_head"]

     # Store the classifier.
     __A					:					Optional[int]								=		binary_head["weight"]
     __A					:					Optional[int]								=		binary_head["bias"]

     # It should be done!
     return output_state_dict




def 						_lowercase				(  )    ->     Union[str, Any]:







     '''simple docstring'''


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

     # Extract the basename.
     __A					:					Union[str, Any]								=		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:
                    __A					:					Any								=		torch.load(_SCREAMING_SNAKE_CASE  ,       map_location='cpu'						)
     else:
          __A					:					Union[str, Any]								=		torch.load(args.path_to_checkpoint  ,       map_location='cpu'						)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          __A					:					List[str]								=		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
          __A					:					List[Any]								=		input_state_dict["model"]["lm_head"]["bias"].numel()
     else:
          __A					:					Union[str, Any]								=		MegatronBertConfig.from_json_file(args.config_file						)

     # Convert.
     print('Converting'						)
     __A					:					Dict								=		convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)

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

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

     # Store the state_dict to file.
     __A					:					Union[str, Any]								=		os.path.join(_SCREAMING_SNAKE_CASE  ,       'pytorch_model.bin'						)
     print(F'Saving checkpoint to "{output_checkpoint_file}"'						)
     torch.save(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
       main()

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






import unittest
from timeit import timeit







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     first_num if first_num >= second_num else second_num
     _lowerCAmelCase :       List[str]	   =     max_num
     while (common_mult % first_num > 0) or (common_mult % second_num > 0):
          common_mult += max_num
     return common_mult







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return b if a == 0 else greatest_common_divisor(b % a		,				SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return first_num // greatest_common_divisor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					) * second_num







def 						_snake_case		(     )							->   str:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     (
         "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
     )
     print(
         "least_common_multiple_slow():"		,				timeit("least_common_multiple_slow(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)
     print(
         "least_common_multiple_fast():"		,				timeit("least_common_multiple_fast(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)





class A__     (			unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    (
     (10, 20),
     (13, 15),
     (4, 31),
     (10, 42),
     (43, 34),
     (5, 12),
     (12, 25),
     (10, 25),
     (6, 9),
 )
 _lowercase  :       Dict  			=    (20, 195, 124, 210, 1_462, 60, 300, 50, 18)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      for i, (first_num, second_num) in enumerate(self.test_inputs							):
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_slow(A_  ,    A_							)
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_fast(A_  ,    A_							)
           with self.subTest(i=A_							):
                self.assertEqual(A_  ,    self.expected_results[i]							)
                self.assertEqual(A_  ,    self.expected_results[i]							)


if __name__ == "__main__":
  benchmark()
  unittest.main()






