
'''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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase=0						)->      int:







      """simple docstring"""






      if name is None:
            __A													=      None
      else:
            __A													=      '.' * max(0   ,	spaces - 2						) + '# {:' + str(5_0 - spaces						) + 's}'
            __A													=      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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      __A													=      {}

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

            __A													=      ds_args.tokenizer_type
            __A													=      ds_args.padded_vocab_size
            __A													=      ds_args.max_position_embeddings
            __A													=      ds_args.hidden_size
            __A													=      ds_args.num_layers
            __A													=      ds_args.num_attention_heads
            __A													=      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													=      config.num_attention_heads
      # The hidden_size per head.
      __A													=      config.hidden_size // heads
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            __A													=      input_state_dict['checkpoint_version']
      else:
            __A													=      0.0

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

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

      # The position embeddings.
      __A													=      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													=      pos_embeddings

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

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

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

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

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

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

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

            # The name of the layer.
            __A													=      f'bert.encoder.layer.{layer_idx}'

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'						):
                  __A													=      'attention.ln' if op_name.startswith('input'						) else 'ln'
                  __A													=      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													=      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.
                  __A													=      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													=      attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                  __A													=      attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                  __A													=      attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

                  # Clear the stored tensor.
                  __A													=      None

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

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

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

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

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

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

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

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

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

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

      # It should be done!
      return output_state_dict





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







      """simple docstring"""
      __A													=      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.'   ,	)
      __A													=      parser.parse_args()

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

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

      # Convert.
      print('Converting'						)
      __A													=      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.
      __A													=      os.path.join(UpperCAmelCase   ,	'pytorch_model.bin'						)
      print(f'Saving checkpoint to "{output_checkpoint_file}"'						)
      torch.save(UpperCAmelCase   ,	UpperCAmelCase						)


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

if __name__ == "__main__":
 main()

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









def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       int:

 """simple docstring"""






 return n * (2 * n - 1)







def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       bool:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = (1 + 24 * n) ** 0.5
 return ((1 + root) / 6) % 1 == 0







def _a					(			SCREAMING_SNAKE_CASE						:     int = 1_44						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = start
 __lowerCAmelCase:  List[str]        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 while not is_pentagonal(SCREAMING_SNAKE_CASE						):
  n += 1
  __lowerCAmelCase:  Any        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 return num


if __name__ == "__main__":
  print(f"{solution()} = ")





