


"""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      a_				(   SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Dict=0					):
       '''simple docstring'''





       if name is None:
              _lowerCamelCase	:		Union[str, Any]          =None
       else:
              _lowerCamelCase	:		Dict          ='.' * max(0		,      spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
              _lowerCamelCase	:		List[str]          =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      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





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




def      a_				(   SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				Any					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          ={}

       # old versions did not store training args
       _lowerCamelCase	:		Tuple          =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))

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

       # The model.
       _lowerCamelCase	:		List[Any]          =input_state_dict['model']
       # The language model.
       _lowerCamelCase	:		Any          =model['language_model']
       # The embeddings.
       _lowerCamelCase	:		Any          =lm['embedding']

       # The word embeddings.
       _lowerCamelCase	:		Union[str, Any]          =embeddings['word_embeddings']['weight']
       # Truncate the embedding table to vocab_size rows.
       _lowerCamelCase	:		List[str]          =word_embeddings[: config.vocab_size, :]
       # Store the word embeddings.
       _lowerCamelCase	:		Any          =word_embeddings

       # The position embeddings.
       _lowerCamelCase	:		int          =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	:		str          =pos_embeddings

       # The token-type embeddings.
       _lowerCamelCase	:		Optional[Any]          =embeddings['tokentype_embeddings']['weight']
       # Store the position embeddings.
       _lowerCamelCase	:		List[Any]          =tokentype_embeddings

       # The transformer.
       _lowerCamelCase	:		Optional[Any]          =lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

       # The simple map of names for "automated" rules.
       _lowerCamelCase	:		Any          ={
           '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	:		Tuple          =None

       # Extract the layers.
       for key, val in transformer.items():
              # Match the name.
              _lowerCamelCase	:		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.
              _lowerCamelCase	:		Union[str, Any]          =int(m.group(1					)					)
              # The name of the operation.
              _lowerCamelCase	:		Optional[int]          =m.group(2					)
              # Is it a weight or a bias?
              _lowerCamelCase	:		List[str]          =m.group(3					)

              # The name of the layer.
              _lowerCamelCase	:		str          =F'''bert.encoder.layer.{layer_idx}'''

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

                     _lowerCamelCase	:		Any          =fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      3		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
                     # Split the bias.
                     _lowerCamelCase	:		Tuple          =out_val[0 * config.hidden_size : 1 * config.hidden_size]
                     _lowerCamelCase	:		str          =out_val[1 * config.hidden_size : 2 * config.hidden_size]
                     _lowerCamelCase	:		Any          =out_val[2 * config.hidden_size : 3 * config.hidden_size]

                     # Store.
                     _lowerCamelCase	:		Dict          =q
                     _lowerCamelCase	:		Union[str, Any]          =q_bias
                     _lowerCamelCase	:		str          =k
                     _lowerCamelCase	:		str          =k_bias
                     _lowerCamelCase	:		int          =v
                     _lowerCamelCase	:		Tuple          =v_bias

                     # Clear the stored tensor.
                     _lowerCamelCase	:		Any          =None

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

    # The final layernorm.
       _lowerCamelCase	:		List[Any]          =transformer['final_layernorm.weight']
       _lowerCamelCase	:		Optional[int]          =transformer['final_layernorm.bias']

       # The pooler.
       _lowerCamelCase	:		Any          =lm['pooler']

       # Store the matrix and the bias.
       _lowerCamelCase	:		List[Any]          =pooler['dense.weight']
       _lowerCamelCase	:		int          =pooler['dense.bias']

       # The LM head from Megatron (for RACE).
       _lowerCamelCase	:		List[str]          =model['lm_head']

       # The transform matrix.
       _lowerCamelCase	:		int          =lm_head['dense.weight']
       _lowerCamelCase	:		List[Any]          =lm_head['dense.bias']

       # The transform LN.
       _lowerCamelCase	:		Optional[int]          =lm_head['layernorm.weight']
       _lowerCamelCase	:		List[str]          =lm_head['layernorm.bias']

       # For the decoder, we replicate the weights.
       _lowerCamelCase	:		Tuple          =word_embeddings
       _lowerCamelCase	:		Any          =lm_head['bias']

       # The classifier from Megatron (for MLNI).
       _lowerCamelCase	:		Optional[int]          =model['binary_head']

       # Store the classifier.
       _lowerCamelCase	:		int          =binary_head['weight']
       _lowerCamelCase	:		int          =binary_head['bias']

       # It should be done!
       return output_state_dict




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =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.'		,      )
       _lowerCamelCase	:		Optional[Any]          =parser.parse_args()

       # Extract the basename.
       _lowerCamelCase	:		Dict          =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	:		List[Any]          =torch.load(SCREAMING_SNAKE_CASE__		,      map_location='cpu'					)
       else:
              _lowerCamelCase	:		str          =torch.load(args.path_to_checkpoint		,      map_location='cpu'					)

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

       # Convert.
       print('Converting'					)
       _lowerCamelCase	:		int          =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.
       _lowerCamelCase	:		str          =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()

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






from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class A  (       UpperCamelCase_					):






      @abstractmethod
      def __lt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __gt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __eq__( self						:					T      ,       lowercase_						:					object			) ->						bool:
             """simple docstring"""




             pass




lowerCamelCase						 =	TypeVar('T', bound=Comparable)




class A  (       Generic[T]					):






      def __init__( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		list[T]          =[]
             _lowerCamelCase	:		int          =0






      def __repr__( self						:					int			) ->						str:
             """simple docstring"""




             return str(self.h			)






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             if child_idx > 0:
                    return (child_idx - 1) // 2
             return None






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =2 * parent_idx + 1
             if left_child_index < self.heap_size:
                    return left_child_index
             return None






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =2 * parent_idx + 2
             if right_child_index < self.heap_size:
                    return right_child_index
             return None






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




             if index < self.heap_size:
                    _lowerCamelCase	:		int          =index
                    _lowerCamelCase	:		Any          =self.left_child_idx(lowercase_			)
                    _lowerCamelCase	:		int          =self.right_child_idx(lowercase_			)
                    # check which child is larger than its parent
                    if left_child is not None and self.h[left_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =left_child
                    if right_child is not None and self.h[right_child] > self.h[violation]:
                           _lowerCamelCase	:		str          =right_child
                    # if violation indeed exists
                    if violation != index:
                           # swap to fix the violation
                           _lowerCamelCase     ,					_lowerCamelCase	:		List[str]          =self.h[index], self.h[violation]
                           # fix the subsequent violation recursively if any
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Iterable[T]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =list(lowercase_			)
             _lowerCamelCase	:		Dict          =len(self.h			)
             if self.heap_size > 1:
                    # max_heapify from right to left but exclude leaves (last level)
                    for i in range(self.heap_size // 2 - 1      ,       -1      ,       -1			):
                           self.max_heapify(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict			) ->						T:
             """simple docstring"""




             if self.heap_size >= 2:
                    _lowerCamelCase	:		Optional[int]          =self.h[0]
                    _lowerCamelCase	:		Optional[int]          =self.h.pop(-1			)
                    self.heap_size -= 1
                    self.max_heapify(0			)
                    return me
             elif self.heap_size == 1:
                    self.heap_size -= 1
                    return self.h.pop(-1			)
             else:
                    raise Exception('Empty heap'			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					T			) ->						None:
             """simple docstring"""




             self.h.append(lowercase_			)
             _lowerCamelCase	:		List[Any]          =(self.heap_size - 1) // 2
             self.heap_size += 1
             while idx >= 0:
                    self.max_heapify(lowercase_			)
                    _lowerCamelCase	:		Tuple          =(idx - 1) // 2






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.heap_size
             for j in range(size - 1      ,       0      ,       -1			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.h[j], self.h[0]
                    self.heap_size -= 1
                    self.max_heapify(0			)
             _lowerCamelCase	:		Union[str, Any]          =size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
     [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
     [-45, -2, -5],
 ]:
  print(F"""unsorted array: {unsorted}""")

  lowerCamelCase						 =	Heap()
  heap.build_max_heap(unsorted)
  print(F"""after build heap: {heap}""")

  print(F"""max value: {heap.extract_max()}""")
  print(F"""after max value removed: {heap}""")

  heap.insert(1_00)
  print(F"""after new value 100 inserted: {heap}""")

  heap.heap_sort()
  print(F"""heap-sorted array: {heap}\n""")


