

'''simple docstring'''
import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Optional[int]=0       )   ->    Optional[Any]:
     """simple docstring"""




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




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


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  str							,				_UpperCamelCase		:  Tuple       )   ->    str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												={}

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

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

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

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

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

     # The position embeddings.
     _SCREAMING_SNAKE_CASE												=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.
     _SCREAMING_SNAKE_CASE												=pos_embeddings

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

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

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

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

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

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

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

          # The name of the layer.
          _SCREAMING_SNAKE_CASE												=f"bert.encoder.layer.{layer_idx}"

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

               _SCREAMING_SNAKE_CASE												=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.
               _SCREAMING_SNAKE_CASE												=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.
               _SCREAMING_SNAKE_CASE												=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               _SCREAMING_SNAKE_CASE												=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               _SCREAMING_SNAKE_CASE												=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

               # Clear the stored tensor.
               _SCREAMING_SNAKE_CASE												=None

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

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

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

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

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

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

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

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

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

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

     # It should be done!
     return output_state_dict


def  _lowerCAmelCase      (		)   ->    str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=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.'							,				)
     _SCREAMING_SNAKE_CASE												=parser.parse_args()

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

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

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


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

if __name__ == "__main__":
      main()

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



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  list       )   ->    list:
     """simple docstring"""




     for i, _ in enumerate(_UpperCamelCase       ):
          if (i % 2 == 1) == (nums[i - 1] > nums[i]):
               _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=nums[i], nums[i - 1]

     return nums


if __name__ == "__main__":
     print("Enter the array elements:")
     lowerCamelCase				:   Tuple      				=  list(map(int, input().split()))
     print("The unsorted array is:")
     print(array)
     print("Array after Wiggle sort:")
     print(wiggle_sort(array))
