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		)		->	Tuple:
   # Format the message.
   if name is None:
    A__ =       None
   else:
    A__ =       "." * max(0    ,				spaces - 2		) + "# {:" + str(50 - spaces		) + "s}"
    A__ =       fmt.format(lowercase_		)
  
   # Print and recurse (if needed).
   if isinstance(lowercase_    ,				lowercase_		):
    if msg is not None:
     print(lowercase_		)
    for k in val.keys():
     recursive_print(lowercase_    ,				val[k]    ,				spaces + 2		)
   elif isinstance(lowercase_    ,				torch.Tensor		):
    print(lowercase_    ,				":"    ,				val.size()		)
   else:
    print(lowercase_    ,				":"    ,				lowercase_		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	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.
   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(*lowercase_		)
    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(*lowercase_		)
    A__ =       param.transpose(0    ,				1		).contiguous()
   A__ =       param.view(*lowercase_		)
   return param
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   # The converted output model.
   A__ =       {}
  
   # old versions did not store training args
   A__ =       input_state_dict.get("args"    ,				lowercase_		)
   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(lowercase_		)
  
    # 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(lowercase_    ,				lowercase_    ,				3    ,				lowercase_    ,				lowercase_		)
     # 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(lowercase_    ,				lowercase_    ,				3    ,				lowercase_    ,				lowercase_		)
     # 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     (							)		->	str:
   # Create the argument parser.
   A__ =       argparse.ArgumentParser()
   parser.add_argument("--print-checkpoint-structure"    ,				action="store_true"		)
   parser.add_argument("path_to_checkpoint"    ,				type=lowercase_    ,				help="Path to the ZIP file containing the checkpoint"		)
   parser.add_argument(
       "--config_file"    ,				default=""    ,				type=lowercase_    ,				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(lowercase_    ,				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(lowercase_    ,				lowercase_    ,				lowercase_		)
  
   # Print the structure of converted state dict.
   if args.print_checkpoint_structure:
    recursive_print(lowercase_    ,				lowercase_		)
  
   # Store the config to file.
   print("Saving config"		)
   config.save_pretrained(lowercase_		)
  
   # Store the state_dict to file.
   A__ =       os.path.join(lowercase_    ,				"pytorch_model.bin"		)
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""		)
   torch.save(lowercase_    ,				lowercase_		)
  
  
  ####################################################################################################
  
  if __name__ == "__main__":
        main()
  
  ####################################################################################################
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  