
"""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_			(					lowercase						, lowercase						, lowercase=0      )     ->							List[str]:


     """simple docstring"""







     if name is None:
          UpperCAmelCase_						:    List[Any]    					=  None
     else:
          UpperCAmelCase_						:    Union[str, Any]    					=  """.""" * max(0						, spaces - 2      ) + """# {:""" + str(50 - spaces      ) + """s}"""
          UpperCAmelCase_						:    Union[str, Any]    					=  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 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase      )     ->							Any:


     """simple docstring"""







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




def 			A_			(					lowercase						, lowercase						, lowercase      )     ->							List[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Union[str, Any]    					=  {}

     # old versions did not store training args
     UpperCAmelCase_						:    str    					=  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))

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

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

     # The word embeddings.
     UpperCAmelCase_						:    Tuple    					=  embeddings["""word_embeddings"""]["""weight"""]
     # Truncate the embedding table to vocab_size rows.
     UpperCAmelCase_						:    Dict    					=  word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     UpperCAmelCase_						:    List[str]    					=  word_embeddings

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

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

     # The transformer.
     UpperCAmelCase_						:    List[str]    					=  lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

     # The regex to extract layer names.
     UpperCAmelCase_						:    Union[str, Any]    					=  re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""      )

     # The simple map of names for "automated" rules.
     UpperCAmelCase_						:    int    					=  {
         """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_						:    Any    					=  layer_re.match(lowercase      )

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

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

          # The name of the layer.
          UpperCAmelCase_						:    Union[str, Any]    					=  f'''bert.encoder.layer.{layer_idx}'''

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("""layernorm"""      ):
               UpperCAmelCase_						:    Union[str, Any]    					=  """attention.ln""" if op_name.startswith("""input"""      ) else """ln"""
               UpperCAmelCase_						:    Optional[int]    					=  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_						:    Optional[Any]    					=  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.
               UpperCAmelCase_						:    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.
               UpperCAmelCase_						:    Union[str, Any]    					=  attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase_						:    Tuple    					=  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_						:    Dict    					=  fix_query_key_value_ordering(lowercase						, lowercase						, 3						, lowercase						, lowercase      )
               # Split the bias.
               UpperCAmelCase_						:    Tuple    					=  out_val[0 * config.hidden_size : 1 * config.hidden_size]
               UpperCAmelCase_						:    Union[str, Any]    					=  out_val[1 * config.hidden_size : 2 * config.hidden_size]
               UpperCAmelCase_						:    List[str]    					=  out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               UpperCAmelCase_						:    Any    					=  q
               UpperCAmelCase_						:    Dict    					=  q_bias
               UpperCAmelCase_						:    List[str]    					=  k
               UpperCAmelCase_						:    Optional[Any]    					=  k_bias
               UpperCAmelCase_						:    List[str]    					=  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_						:    Tuple    					=  val

    # The final layernorm.
     UpperCAmelCase_						:    Optional[int]    					=  transformer["""final_layernorm.weight"""]
     UpperCAmelCase_						:    Optional[int]    					=  transformer["""final_layernorm.bias"""]

     # The pooler.
     UpperCAmelCase_						:    Tuple    					=  lm["""pooler"""]

     # Store the matrix and the bias.
     UpperCAmelCase_						:    int    					=  pooler["""dense.weight"""]
     UpperCAmelCase_						:    Optional[Any]    					=  pooler["""dense.bias"""]

     # The LM head from Megatron (for RACE).
     UpperCAmelCase_						:    Union[str, Any]    					=  model["""lm_head"""]

     # The transform matrix.
     UpperCAmelCase_						:    Optional[Any]    					=  lm_head["""dense.weight"""]
     UpperCAmelCase_						:    List[Any]    					=  lm_head["""dense.bias"""]

     # The transform LN.
     UpperCAmelCase_						:    Optional[int]    					=  lm_head["""layernorm.weight"""]
     UpperCAmelCase_						:    Optional[int]    					=  lm_head["""layernorm.bias"""]

     # For the decoder, we replicate the weights.
     UpperCAmelCase_						:    Union[str, Any]    					=  word_embeddings
     UpperCAmelCase_						:    Optional[int]    					=  lm_head["""bias"""]

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

     # Store the classifier.
     UpperCAmelCase_						:    Union[str, Any]    					=  binary_head["""weight"""]
     UpperCAmelCase_						:    int    					=  binary_head["""bias"""]

     # It should be done!
     return output_state_dict




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


     """simple docstring"""







     UpperCAmelCase_						:    Any    					=  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."""						, )
     UpperCAmelCase_						:    List[str]    					=  parser.parse_args()

     # Extract the basename.
     UpperCAmelCase_						:    Dict    					=  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_						:    str    					=  torch.load(lowercase						, map_location="""cpu"""      )
     else:
          UpperCAmelCase_						:    Optional[int]    					=  torch.load(args.path_to_checkpoint						, map_location="""cpu"""      )

     if args.config_file == "":
          # Default config of megatron-bert 345m
          UpperCAmelCase_						:    List[str]    					=  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_						:    Dict    					=  input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
     else:
          UpperCAmelCase_						:    Tuple    					=  MegatronBertConfig.from_json_file(args.config_file      )

     # Convert.
     print("""Converting"""      )
     UpperCAmelCase_						:    int    					=  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.
     UpperCAmelCase_						:    Optional[int]    					=  os.path.join(lowercase						, """pytorch_model.bin"""      )
     print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
     torch.save(lowercase						, lowercase      )


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

if __name__ == "__main__":
       main()

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_						    =					{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_						    =					[
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       lowercase_						    =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

