code: 
  
  """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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=0   ):
  					"""simple docstring"""
  					# Format the message.
  					if name is None:
  										A    =      None
  					else:
  										A    =      '.' * max(0    ,							spaces - 2   ) + '# {:' + str(50 - spaces   ) + 's}'
  										A    =      fmt.format(snake_case__   )
  
  					# Print and recurse (if needed).
  					if isinstance(snake_case__    ,							snake_case__   ):
  										if msg is not None:
  															print(snake_case__   )
  										for k in val.keys():
  															recursive_print(snake_case__    ,							val[k]    ,							spaces + 2   )
  					elif isinstance(snake_case__    ,							torch.Tensor   ):
  										print(snake_case__    ,							":"    ,							val.size()   )
  					else:
  										print(snake_case__    ,							":"    ,							snake_case__   )
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					# 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.
  					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(*snake_case__   )
  										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(*snake_case__   )
  										A    =      param.transpose(0    ,							1   ).contiguous()
  					A    =      param.view(*snake_case__   )
  					return param
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					# The converted output model.
  					A    =      {}
  
  					# old versions did not store training args
  					A    =      input_state_dict.get("args"    ,							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    =      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(snake_case__   )
  
  										# 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(snake_case__    ,							snake_case__    ,							3    ,							snake_case__    ,							snake_case__   )
  															# 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(snake_case__    ,							snake_case__    ,							3    ,							snake_case__    ,							snake_case__   )
  															# 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      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					# Create the argument parser.
  					A    =      argparse.ArgumentParser()
  					parser.add_argument("--print-checkpoint-structure"    ,							action="store_true"   )
  					parser.add_argument("path_to_checkpoint"    ,							type=snake_case__    ,							help="Path to the ZIP file containing the checkpoint"   )
  					parser.add_argument(
  					    "--config_file"    ,							default=""    ,							type=snake_case__    ,							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(snake_case__    ,							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(snake_case__    ,							snake_case__    ,							snake_case__   )
  
  					# Print the structure of converted state dict.
  					if args.print_checkpoint_structure:
  										recursive_print(snake_case__    ,							snake_case__   )
  
  					# Store the config to file.
  					print("Saving config"   )
  					config.save_pretrained(snake_case__   )
  
  					# Store the state_dict to file.
  					A    =      os.path.join(snake_case__    ,							"pytorch_model.bin"   )
  					print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""   )
  					torch.save(snake_case__    ,							snake_case__   )
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
  	main()
  
  ####################################################################################################
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  