



'''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_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=0	)				->				Union[str, Any]:
  # Format the message.
  if name is None:
    __lowerCamelCase	:	int   	=			None
  else:
    __lowerCamelCase	:	Tuple   	=			'.' * max(0	,spaces - 2	) + '# {:' + str(50 - spaces	) + 's}'
    __lowerCamelCase	:	List[Any]   	=			fmt.format(_lowerCAmelCase	)

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

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Dict:
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace BERT.
  __lowerCamelCase	:	Union[str, Any]   	=			param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    __lowerCamelCase	:	Union[str, Any]   	=			(num_heads, hidden_size, num_splits) + input_shape[1:]
    __lowerCamelCase	:	Dict   	=			param.view(*_lowerCAmelCase	)
    __lowerCamelCase	:	str   	=			param.transpose(0	,2	)
    __lowerCamelCase	:	Optional[Any]   	=			param.transpose(1	,2	).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    __lowerCamelCase	:	Dict   	=			(num_heads, num_splits, hidden_size) + input_shape[1:]
    __lowerCamelCase	:	List[str]   	=			param.view(*_lowerCAmelCase	)
    __lowerCamelCase	:	Any   	=			param.transpose(0	,1	).contiguous()
  __lowerCamelCase	:	List[Any]   	=			param.view(*_lowerCAmelCase	)
  return param

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Tuple:
  # The converted output model.
  __lowerCamelCase	:	Union[str, Any]   	=			{}

  # old versions did not store training args
  __lowerCamelCase	:	List[Any]   	=			input_state_dict.get('args'	,_lowerCAmelCase	)
  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	:	Optional[Any]   	=			ds_args.tokenizer_type
    __lowerCamelCase	:	Optional[int]   	=			ds_args.padded_vocab_size
    __lowerCamelCase	:	Union[str, Any]   	=			ds_args.max_position_embeddings
    __lowerCamelCase	:	List[str]   	=			ds_args.hidden_size
    __lowerCamelCase	:	Tuple   	=			ds_args.num_layers
    __lowerCamelCase	:	Union[str, Any]   	=			ds_args.num_attention_heads
    __lowerCamelCase	:	Union[str, 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	:	Optional[int]   	=			config.num_attention_heads
  # The hidden_size per head.
  __lowerCamelCase	:	List[Any]   	=			config.hidden_size // heads
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    __lowerCamelCase	:	Optional[Any]   	=			input_state_dict['checkpoint_version']
  else:
    __lowerCamelCase	:	str   	=			0.0

  # The model.
  __lowerCamelCase	:	Optional[int]   	=			input_state_dict['model']
  # The language model.
  __lowerCamelCase	:	Union[str, Any]   	=			model['language_model']
  # The embeddings.
  __lowerCamelCase	:	Any   	=			lm['embedding']

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

  # The position embeddings.
  __lowerCamelCase	:	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.
  __lowerCamelCase	:	List[Any]   	=			pos_embeddings

  # The token-type embeddings.
  __lowerCamelCase	:	str   	=			embeddings['tokentype_embeddings']['weight']
  # Store the position embeddings.
  __lowerCamelCase	:	Tuple   	=			tokentype_embeddings

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

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

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

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    __lowerCamelCase	:	List[Any]   	=			layer_re.match(_lowerCAmelCase	)

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

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

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

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'	):
      __lowerCamelCase	:	str   	=			'attention.ln' if op_name.startswith('input'	) else 'ln'
      __lowerCamelCase	:	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	:	List[str]   	=			fix_query_key_value_ordering(_lowerCAmelCase	,_lowerCAmelCase	,3	,_lowerCAmelCase	,_lowerCAmelCase	)
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      __lowerCamelCase	:	List[Any]   	=			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	:	List[str]   	=			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      __lowerCamelCase	:	Union[str, Any]   	=			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      __lowerCamelCase	:	int   	=			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

      # Clear the stored tensor.
      __lowerCamelCase	:	int   	=			None

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

    # The final layernorm.
  __lowerCamelCase	:	Tuple   	=			transformer['final_layernorm.weight']
  __lowerCamelCase	:	Optional[int]   	=			transformer['final_layernorm.bias']

  # The pooler.
  __lowerCamelCase	:	Union[str, Any]   	=			lm['pooler']

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

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

  # The transform matrix.
  __lowerCamelCase	:	str   	=			lm_head['dense.weight']
  __lowerCamelCase	:	Any   	=			lm_head['dense.bias']

  # The transform LN.
  __lowerCamelCase	:	str   	=			lm_head['layernorm.weight']
  __lowerCamelCase	:	Dict   	=			lm_head['layernorm.bias']

  # For the decoder, we replicate the weights.
  __lowerCamelCase	:	int   	=			word_embeddings
  __lowerCamelCase	:	Dict   	=			lm_head['bias']

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

  # Store the classifier.
  __lowerCamelCase	:	Union[str, Any]   	=			binary_head['weight']
  __lowerCamelCase	:	List[Any]   	=			binary_head['bias']

  # It should be done!
  return output_state_dict

def 	a_					(							)				->				Any:
  # Create the argument parser.
  __lowerCamelCase	:	int   	=			argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'	,action='store_true'	)
  parser.add_argument('path_to_checkpoint'	,type=_lowerCAmelCase	,help='Path to the ZIP file containing the checkpoint'	)
  parser.add_argument(
      '--config_file'	,default=''	,type=_lowerCAmelCase	,help='An optional config json file describing the pre-trained model.'	,)
  __lowerCamelCase	:	Tuple   	=			parser.parse_args()

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

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

  # Convert.
  print('Converting'	)
  __lowerCamelCase	:	str   	=			convert_megatron_checkpoint(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

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

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

  # Store the state_dict to file.
  __lowerCamelCase	:	Optional[int]   	=			os.path.join(_lowerCAmelCase	,'pytorch_model.bin'	)
  print(F'Saving checkpoint to "{output_checkpoint_file}"'	)
  torch.save(_lowerCAmelCase	,_lowerCAmelCase	)


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

if __name__ == "__main__":
   main()

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











'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




  def 						_lowercase					(			self				:     Tuple       ) -> bool:
    return (self.x**2 + self.y**2) <= 1




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




def 	a_					(							_lowerCAmelCase	)				->				float:
  if number_of_simulations < 1:
    raise ValueError('At least one simulation is necessary to estimate PI.'	)

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			Point.random_unit_square()

    if random_point.is_in_unit_circle():
      number_in_unit_circle += 1

  return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
   # import doctest

   # doctest.testmod()
   from math import pi

   _UpperCamelCase							    =						'Please enter the desired number of Monte Carlo simulations: '
   _UpperCamelCase							    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






