






# 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase=0					)   -> str:

     '''simple docstring'''
     # Format the message.
     if name is None:
          UpperCAmelCase__			:     Dict										= None
     else:
          UpperCAmelCase__			:     int										= """.""" * max(0   ,       spaces - 2					) + """# {:""" + str(50 - spaces					) + """s}"""
          UpperCAmelCase__			:     Union[str, 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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> int:

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





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase   ,       __lowerCamelCase					)   -> int:

     '''simple docstring'''
     # The converted output model.
     UpperCAmelCase__			:     Optional[Any]										= {}

     # old versions did not store training args
     UpperCAmelCase__			:     int										= 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))

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

     # The model.
     UpperCAmelCase__			:     str										= input_state_dict["""model"""]
     # The language model.
     UpperCAmelCase__			:     str										= model["""language_model"""]
     # The embeddings.
     UpperCAmelCase__			:     List[str]										= lm["""embedding"""]

     # The word embeddings.
     UpperCAmelCase__			:     int										= embeddings["""word_embeddings"""]["""weight"""]
     # Truncate the embedding table to vocab_size rows.
     UpperCAmelCase__			:     Optional[int]										= word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     UpperCAmelCase__			:     List[Any]										= word_embeddings

     # The position embeddings.
     UpperCAmelCase__			:     Optional[Any]										= 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.
     UpperCAmelCase__			:     Any										= pos_embeddings

     # The token-type embeddings.
     UpperCAmelCase__			:     Tuple										= embeddings["""tokentype_embeddings"""]["""weight"""]
     # Store the position embeddings.
     UpperCAmelCase__			:     int										= tokentype_embeddings

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

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

     # The simple map of names for "automated" rules.
     UpperCAmelCase__			:     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.
     UpperCAmelCase__			:     Optional[Any]										= None

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

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

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

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

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("""layernorm"""					):
               UpperCAmelCase__			:     Optional[int]										= """attention.ln""" if op_name.startswith("""input"""					) else """ln"""
               UpperCAmelCase__			:     Any										= 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, ""

               UpperCAmelCase__			:     Tuple										= 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.
               UpperCAmelCase__			:     Tuple										= 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.
               UpperCAmelCase__			:     Optional[Any]										= attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase__			:     Dict										= attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               UpperCAmelCase__			:     Optional[Any]										= attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

               # Store.
               UpperCAmelCase__			:     str										= q
               UpperCAmelCase__			:     Dict										= q_bias
               UpperCAmelCase__			:     Any										= k
               UpperCAmelCase__			:     Any										= k_bias
               UpperCAmelCase__			:     Optional[int]										= v
               UpperCAmelCase__			:     Tuple										= v_bias

               # Clear the stored tensor.
               UpperCAmelCase__			:     Optional[Any]										= None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               UpperCAmelCase__			:     List[Any]										= megatron_to_transformers[op_name]
               UpperCAmelCase__			:     Union[str, Any]										= val

    # The final layernorm.
     UpperCAmelCase__			:     List[Any]										= transformer["""final_layernorm.weight"""]
     UpperCAmelCase__			:     List[str]										= transformer["""final_layernorm.bias"""]

     # The pooler.
     UpperCAmelCase__			:     List[str]										= lm["""pooler"""]

     # Store the matrix and the bias.
     UpperCAmelCase__			:     List[Any]										= pooler["""dense.weight"""]
     UpperCAmelCase__			:     Tuple										= pooler["""dense.bias"""]

     # The LM head from Megatron (for RACE).
     UpperCAmelCase__			:     Dict										= model["""lm_head"""]

     # The transform matrix.
     UpperCAmelCase__			:     str										= lm_head["""dense.weight"""]
     UpperCAmelCase__			:     str										= lm_head["""dense.bias"""]

     # The transform LN.
     UpperCAmelCase__			:     List[Any]										= lm_head["""layernorm.weight"""]
     UpperCAmelCase__			:     int										= lm_head["""layernorm.bias"""]

     # For the decoder, we replicate the weights.
     UpperCAmelCase__			:     Dict										= word_embeddings
     UpperCAmelCase__			:     Tuple										= lm_head["""bias"""]

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

     # Store the classifier.
     UpperCAmelCase__			:     Optional[int]										= binary_head["""weight"""]
     UpperCAmelCase__			:     Optional[int]										= binary_head["""bias"""]

     # It should be done!
     return output_state_dict





def    _lowerCamelCase	(	)   -> Union[str, Any]:

     '''simple docstring'''
     # Create the argument parser.
     UpperCAmelCase__			:     Optional[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."""   ,       )
     UpperCAmelCase__			:     Optional[int]										= parser.parse_args()

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

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

     # Convert.
     print("""Converting"""					)
     UpperCAmelCase__			:     List[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.
     UpperCAmelCase__			:     Dict										= os.path.join(__lowerCamelCase   ,       """pytorch_model.bin"""					)
     print(F"Saving checkpoint to \"{output_checkpoint_file}\""					)
     torch.save(__lowerCamelCase   ,       __lowerCamelCase					)


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

if __name__ == "__main__":
      main()

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:       str   							=							[
          """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DinatForImageClassification""",
          """DinatModel""",
          """DinatPreTrainedModel""",
          """DinatBackbone""",
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






